<![CDATA[No BS, Just Tax Tech]]>https://www.dmihaylov.comhttps://substackcdn.com/image/fetch/$s_!zp2b!,w_256,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F613c9cbb-ea27-4806-a6de-4f02eddda3ee_1008x1008.pngNo BS, Just Tax Techhttps://www.dmihaylov.comSubstackWed, 29 Apr 2026 08:49:01 GMT<![CDATA[The Future Cost of TaxTech: Managing the New Unit Economics of Compliance]]>https://www.dmihaylov.com/p/the-future-cost-of-taxtech-managinghttps://www.dmihaylov.com/p/the-future-cost-of-taxtech-managingTue, 28 Apr 2026 05:04:48 GMT

In the boardrooms of the S&P 500 and high-growth scale-ups, the “common wisdom” is that AI is primarily a productivity booster—a narrative fueled by marketing promises of an “era of builders” where everyone can create at scale. This is not wrong. But there is a twist. In 2026, we have transitioned from highly predictable, established SaaS pricing models to a “tokenized economy,” in which data is the input and output, and compute is the fuel, metered like a utility—a shift that makes real-time AI infrastructure stability as critical to the P&L as the tax logic being executed.

But here is the catch. You cannot manage a 2026 cost structure with a 2016 KPI dashboard. Here is the ground truth for the new KPI framework needed to navigate the tokenized economy today.

I. Latest Trends in AI Tech: The Tokenization of the S&P 500 Cost Structure

1. Enterprise Pricing: The Metered Reality

A look at the pricing models of the top 4 AI platform providers reveals that the era of the “flat-fee seat” is dead. We have entered the era of “utility AI”, in which compute is metered like electricity1:

A comparison table of AI model pricing and their estimated annual impact at 1 billion transactions. The table lists five tiers: Anthropic (Claude 4.6 Opus): $5.00/$25.00 per 1M tokens, impacting cost at ~$30,000,000/year. OpenAI (GPT-5.4 Flagship): $2.50/$15.00 per 1M tokens, impacting cost at ~$15,000,000/year. Google Gemini (Gemini 3.1 Pro): $2.00/$12.00 per 1M tokens, impacting cost at ~$14,000,000/year. Perplexity (Enterprise Max): $325/seat or custom API, impact is Variable. The Budget Tier (GPT-4.1 Nano): $0.10/$0.40 per 1M tokens, impacting cost at ~$500,000/year. The table includes official pricing source links for each provider.
Note: Pricing data reflects official documentation as of April 2026, and excludes budget tiers such as GPT4.1-Nano.

It’s clear that for the S&P 500—companies managing 100 million to more than 1 billion transactions per year—AI costs are a direct hit to the margins. This is also what Uber’s CTO, Praveen Neppalli Naga, recently confirmed. In this interview, he revealed that the company exhausted its entire 2026 Claude budget by April due to deep agentic integration. Uber's R&D expenses rose 9% to $3.4 billion in 2025, and the company expects that figure to keep climbing—suggesting AI may be as much a cost driver as a productivity lever.

2. The Stability Layer: Reliability Tracking

At the S&P 500 scale, downtime isn’t an inconvenience—it’s a breach of compliance. The gold standard is “four nines” (99.99%), which allows for only 52 minutes of downtime annually. Let’s look at how the AI platform providers are doing in 2026:

A comparison table showing the 2026 Observed Reliability of major AI vendors and the resulting impact on 1 billion transactions. OpenAI: ~99.7% reliability, resulting in 3 Million Fails (26 hours down per year). Status: status.openai.com. Anthropic: ~99.8% reliability, resulting in 2 Million Fails (17 hours down per year). Status: status.anthropic.com. Google Gemini: ~99.91% reliability, resulting in 900,000 Fails (8 hours down per year). Status: aistudio.google.com/status. Perplexity: ~99.95% reliability, resulting in 500,000 Fails (4 hours down per year). Status: perplexity.statuspage.io. The table features a light blue header and lists status tracker links for each vendor.
Reliability Benchmarking: Figures represent aggregated uptime performance observed across high-volume enterprise compliance workloads during Q1 2026.

None of the major LLM providers currently guarantees 99.99% uptime during peak global cycles. So, any taxtech architecture must have a failover logic to hot-swap providers in milliseconds when a “brownout” occurs.

3. Ownership Disruptions: Related Parties and Consolidation

In 2026, the AI world is one of related parties and imminent consolidation. Here are only the recent signals:

  • The SpaceX-Cursor Vertical Integration: SpaceX has secured a $60 billion option to acquire Cursor to fill xAI’s infrastructure gap.

    This signals that the infrastructure used to build AI is as valuable as, if not more valuable than, the models themselves. If your infrastructure is tied to a platform that gets acquired, the impact may not be convenient.

  • The OpenAI-Microsoft “Risk Factor”: In recent draft IPO filings, Sam Altman officially labeled Microsoft a “risk factor” due to OpenAI’s deep dependence on their compute.

    When such partnerships signal strategic friction, you must prepare for a change in compute infrastructure that could spike your costs overnight.

  • The Anthropic-Amazon “compute debt”: Anthropic and Amazon have committed $100 billion to AWS custom Trainium chips.

    This creates a new vendor lock-in. If you are integrated with Claude, you are now effectively an AWS-dependent entity, regardless of your primary cloud strategy.

In summary, the shifting landscape introduces risks such as:

  1. Pricing drift

The variance between forecasted and actual compute costs is often driven by vendor-side pricing changes or inefficient “infinite loops” in agentic workflows that consume tokens without generating value.

  1. Infrastructure fragility

The systemic risk of service outages or data bottlenecks stems from an AI provider’s inability to scale compute resources to match your global transaction volume.

  1. Model latency

The time-lag between a data input and an AI output. At scale, high latency isn’t just a slow app; it can be a blocked checkout or a failure in real-time compliance reporting.

In other words, if your tax data is latent or your vendor stack is fragile, your AI becomes an expensive way to generate errors at the speed of light—transforming a potential efficiency engine into a high-frequency audit liability.

So, what’s the right course of action to mitigate those risks?

To mitigate these risks, the traditional fintech/taxtech KPI dashboard is no longer sufficient. We are moving toward dynamic, metered infrastructure with significant cost implications — a shift that requires a new set of KPIs to protect the bottom line. Here is how I would stay ahead:

  1. No Infinite Spending Loops: Agentic AI can enter recursive loops that burn $100,000 in tokens before you finish your morning coffee. You need hard “circuit breakers” at the API level to stop runaway costs.

  2. Get a Grasp of ROI: Stop using AI to only write memos or summarize meeting notes. Use it to automate the 4,000 manual monthly reconciliations that keep your accounting team up at night. The highest returns are found in escaping the manual loop, not executive dashboards or piles of memos.

  3. Own Your Logic Library: If you bake your proprietary tax logic into a specific vendor’s prompt, you are a hostage to their uptime and pricing. Build for model agnosticism so you can migrate from OpenAI to Gemini in under 24 hours.

  4. Audit for Infrastructure, Not Just Model Performance: With SpaceX securing a $60 billion option to acquire Cursor, the infrastructure that builds the code is now as valuable as the AI itself. Ensure your vendors are built on hyperscale foundations (Google/AWS/Azure) that can actually handle your transaction volume.

II. The S&P 500 TaxTech Dashboard

For the Chief Tax Officer or VP of Engineering, the greatest risk in 2026 isn’t a single audit; it’s invisible inefficiency. At the S&P 500 scale, you cannot manage what you do not measure. Therefore, we need KPIs that move beyond “monitoring” to “ROI orchestration,” i.e., actively managing the unit economics of compliance. Your dashboard should be more than a status report—it should be a decision engine that incorporates AI efficiency.

Here's my proposal for a scalable TaxTech Dashboard at the S&P 500 level:

A professional "Tax Function Dashboard v2.2" showing real-time strategic decision triggers and AI leverage. Top Alerts: Displays a Tech Stack Alert (1.2% variance) calling for a "Tech Architecture Review," a Cost Variance Alert (6.2%) noting a "Model Swap to Gemini Flash," and an Availability Monitor at 99.99%. KPI Cards: Shows an Effective Tax Rate (ETR) of 28.4% (+4.2% var), Manual Journal Entry at 1.2% (exceeding the <1% goal), AI Decision Accuracy at 97.8%, and Token Efficiency (TER) at $12.45. Visual Data: * A line graph tracks "AI Pricing Drift & Model Swap," peaking in March. A donut chart illustrates "CAPEX Token Allocation" between Creation (green) and Maintenance (orange). A bar chart compares "Compliance Spend % of Revenue" across Q1-Q4. A line graph monitors the "Audit Adjustment Ratio Tracker" over four weeks, showing a slight upward trend. Status Badges: Indicates AI Uptime at 99.991% and connection to a "Sovereign Cloud" node.

Along with the traditional metrics such as Effective Tax Rate, Compliance Spend, and % of Manual Journal Entries, TaxTech leadership must follow some new KPIs:

1. AI Pricing Drift (Delta %)

With the end of the “flat-fee era,” the AI costs are now as volatile as energy prices. This KPI tracks the per-token spend variance. For example, if weekly drift exceeds 5%, the dashboard triggers an automated “model swap,” routing low-complexity tasks to more cost-effective tiers.

2. AI Decision Accuracy (%)

In 2026, manual troubleshooting is the silent killer of scale. This KPI tracks the percentage of tax triages handled by AI without human intervention. If it dips below 98%, for example, the dashboard should automatically increase the “human-in-the-loop” sampling rate to prevent systemic audit risks.

3. Token Effectiveness Ratio (TER / 1M tokens)

This KPI identifies exactly how many tokens are converted into defensible compliance outcomes versus the compute waste generated by recursive loops like API validation ping-pong (endless retries against failing portals), consensus conflict (agents arguing over logic until quotas are exhausted), or hallucination spirals (expensive internal reasoning used to resolve contradictory data). By tracking TER, budget owners can detect these “inference leaks” before they decimate quarterly results, ensuring the system maintains a defensible 1:1 ratio rather than slipping into the 1:1000 efficiency abyss that defines unmanaged infrastructure.

4. AI Latency & Uptime

In the fintech world, 5 seconds is an eternity. This metric tracks the milliseconds between a transaction and its tax-defensible status. Any dip below 99.99% reliability triggers a failover to a secondary sovereign cloud instance.

5. CAPEX Token Allocation

In the tokenized economy of 2026, CAPEX Token Allocation (ASU 2025-06) is extremely important. Following the 2025 FASB update regarding AI software capitalization, organizations must now surgically differentiate between tokens consumed for “creation”—the R&D and asset-building phases that qualify as capitalizable investments—and those used for “maintenance”, which hit the books as immediate operational spend. By monitoring this metric through real-time financial telemetry, budget owners can meet specific asset capitalization targets, thereby demonstrating to auditors that their AI spend is building a long-term intangible asset rather than merely subsidizing an expensive, ongoing maintenance loop.

Strategic Forecast

Over the next 24 months, the most successful FinTax leaders will shed their traditional “Head of Tax” skins and re-emerge as VPs of Compliance Infrastructure. In this new reality, the silo between the tax department and the platform engineering team will vanish entirely, replaced by a shared mandate: the real-time management of compliance telemetry. The standard for success will shift from “getting it done” to algorithmic efficiency. Expect to see:

  • The Rise of the TER Audit: The Token Effectiveness Ratio (TER) will shift from a niche DevOps metric to a primary audited financial disclosure. The market will begin to value AI-enabled firms by their compute-to-compliance efficiency, with a ratio approaching $TER = 1:1$ (one successful outcome per unit of spent compute) representing a competitive moat, while anything higher signals an unmanaged inference leak.

  • Drift-Triggered Rerouting: Pricing drift will become the new “interest rate risk.” Companies will deploy automated arbitrage bots that “hot-swap” compliance logic across LLMs (OpenAI, Anthropic, or Gemini) in milliseconds, based on real-time price-per-token volatility and latency performance.

  • The End of the Monthly Close: As AI decision accuracy stabilizes above 99%, the concept of a monthly close will become a legacy term. We are moving toward “continuous compliance”, where tax filings are an automated, real-time byproduct of a data stream, and audit readiness is a constant state rather than a quarterly fire drill.

If you aren’t metering your way to ROI today, you aren’t just falling behind—you are accruing massive technical and fiscal debt that no amount of human “productivity” will be able to pay off.

FAQs: The 2026 Tokenized Economy

What is the “Tokenized Economy” in 2026?

The tokenized economy represents a shift from flat-fee SaaS pricing to a metered “utility AI” model. In this framework, data acts as the input/output, while compute is treated like electricity—metered and billed based on consumption. This makes real-time infrastructure stability a critical factor in a company’s P&L.

Why is the “flat-fee seat” model dying for enterprise AI?

As companies like Uber integrate deep agentic workflows, the volume of transactions (often exceeding 1 billion annually) makes per-user pricing unsustainable for providers. Utility AI allows providers to bill for the actual compute consumed, ensuring margins are protected as agentic complexity scales.

What is the Token Effectiveness Ratio (TER)?

The Token Effectiveness Ratio (TER) is a KPI that measures the ratio of AI tokens converted into defensible outcomes to the amount of waste. A 1:1 ratio is the gold standard, while higher ratios indicate “inference leaks” caused by recursive loops, hallucinations, or redundant API validation.

How does ASU 2025-06 affect AI spending?

Under the ASU 2025-06 FASB update, organizations must surgically differentiate between tokens used for “creation” (capitalizable R&D) and “maintenance” (operational spend). CAPEX Token Allocation allows companies to build long-term intangible assets on their balance sheets rather than just incurring ongoing expenses.

What are the main risks of AI infrastructure fragility?

The primary risks include pricing drift (volatility in token costs), infrastructure fragility (service outages during peak periods), and model latency. In 2026, a 5-second delay in AI processing can lead to blocked checkouts or failures in real-time tax compliance reporting.

How can companies achieve “model agnosticism”?

To avoid vendor lock-in and mitigate downtime, firms must build a proprietary logic library independent of any single LLM. This allows them to “hot-swap” between providers such as OpenAI, Gemini, or Anthropic in under 24 hours, based on real-time pricing and performance telemetry.

What is “Continuous Compliance”?

Continuous compliance is the 2026 standard, where tax filings are a real-time byproduct of automated data streams rather than a monthly manual “close.” As AI decision accuracy stabilizes above 98%, audit readiness becomes a constant state, eliminating the need for traditional quarterly fire drills.

References & Sources

1

📊 The Math Behind the Bill:

  • What is a “Transaction”? In this context, a transaction is a compliance event —a single lifecycle moment (payment, refund, or tax validation) that requires the AI to process the transaction data against current tax laws.

  • The Calculation: We assume an average context window of 1,000 tokens per transaction (covering the system prompt, the raw transaction data, and the structured tax output).

  • Formula: (1 Billion Transactions × 1,000 Tokens) / 1,000,000 × Price per 1M Tokens = Annual Cost.

]]><![CDATA[How to Reach AI Fluency in the FinTech/TaxTech Function]]>https://www.dmihaylov.com/p/how-to-reach-ai-fluency-in-the-fintechtaxtechhttps://www.dmihaylov.com/p/how-to-reach-ai-fluency-in-the-fintechtaxtechFri, 17 Apr 2026 08:18:36 GMT

Some S&P 500 CFOs and Chief Tax Officers may take the obvious path, treating AI as a procurement exercise: buy the license, schedule mandatory training, set KPIs for usage, and wait for ROI. After all, this approach worked for other challenges, such as e-invoicing or the adoption of last year’s new accounting/tax software.

It won’t come. At Uber, I’ve seen that you cannot bridge the 2026 “fluency gap” with a three-month seminar. Nor with mandatory usage metrics.

Your team must start treating AI not as a novel luxury or a good-to-know skill but as a baseline utility. To capture the 40-60% cycle-time gains for complex reconciliations and tax modeling, as noted in this Deloitte report, you need to anchor your strategy in “AI-natives”— practitioners who bring a “Product Heart” to the “Compliance Brain.”

The Market Mandate for AI-Native Fin/TaxTech Talent

The industry standard has shifted. My analysis of more than 100 current openings across the S&P 500, high-growth scale-ups such as OpenAI, Anthropic, and Lovable, the Big Four, and traditional SaaS vendors such as Xero, Intuit, and Brex reveals that leading firms are bypassing the “training phase” and hiring for AI fluency as a day-one prerequisite.

We are seeing a structural shift in the “Tax/FinTech Lead” role. In other words, there are new capabilities and requirements being sought for high-stakes transformations:

  • OpenAI is recruiting a Tax Technology Lead to design tax as a “Production System,” integrating compliance directly into core product infrastructure.

  • PwC has shifted from “upskilling” to hiring AI-Native Engineering Leads who must demonstrate daily proficiency with AI coding assistants.

  • Intuit is aggressively hiring for AI Workforce Transformation to lead the shift toward AI-first developer velocity.

  • Anthropic and Lovable are seeking AI-native operators who use agentic workflows to handle real-time Finance & BI decision velocity.

  • EY and Grant Thornton are targeting Tax Automation & AI Managers to build “AI-enabled solutions” for global VAT and indirect tax reporting.

The Takeaway: these roles do not mention “willingness to learn.” They demand “decomposition skills”— the ability to take a messy 400-page tax regulation and break it down into a deterministic logic flow that an AI agent can execute.

So, what do you need to do?

The FinTax AI Fluency & Hiring Rubric

To move your organization from “pro forma” adoption to “front-line” efficiency, you need to update your hiring rubric. Here is Zapier’s hiring rubric, designed to bring AI-native talent in-house with every hiring decision. It’s a good example.

Here is what a Fin/TaxTech hiring rubric may look like, along with examples of the expected behavior, potential gain, and hiring filter questions to ask:

  • Stage 1: The Consumer (Passive Use)

    • Behavior: Using public LLMs to summarize tax memos.

    • Potential Gain: 5-10% productivity lift on administrative tasks and gross time savings of ~5 hours per week.

    • Hiring Filter: Ask: "Explain the difference between an LLM and a database." If they view AI as a "search engine" rather than an "execution engine," they are liable for hallucinations.

  • Stage 2: The Auditor (Problem Decomposition)

  • Stage 3: The Orchestrator (Agentic Design)

    • Behavior: Designing workflows where AI agents handle the “first mile” (data ingestion) and “last mile” (filing).

    • Potential Gain: Teams hiring orchestrators see a 3.5x higher ROI than those stuck in “training mode.”

    • Hiring Filter: Ask: "Walk me through a multi-agent workflow you've designed to automate a high-volume tax workstream. How did you structure the 'human-in-the-loop' intervention points?"

  • Stage 4: The Architect (Tax Product Lead)

    • Behavior: Building a reusable logic library for 100+ jurisdictions.

    • Potential Gain: 40-60% cycle-time reduction on global filings and the ability to launch in new markets in days, not months.

    • Hiring Filter: Ask: "How would you structure a global tax engine to ensure compliance logic is a 'production system' integrated into the product roadmap, rather than a stand-alone 'to-do' list for the tax team?"

Summary

AI fluency within the Fin/TaxTech function is no longer a specialized skill but a mandatory prerequisite for all S&P 500 and high-growth scale-up finance hires. The traditional roles of the Tax Counsel, Accountant, and Tax Manager are augmented by the roles of the Data Engineer, Tech Architect, and Product Manager.

To scale a FinTech or TaxTech function in 2026, you must stop treating AI as a procurement project and start treating it as a hiring mandate.

The data is clear: S&P 500 leaders who anchor their teams with AI-Native Architects—practitioners who can decompose a 400-page regulation into a deterministic logic flow— are achieving 40-60% cycle-time gains and 3.5x higher ROI. Those who rely on “mandatory training” sessions are merely building a more expensive version of their existing “tech stack prison.”

References & Comprehensive Job Analysis (100+ Openings)

]]><![CDATA[If Your AI "Hallucinates" a €10M Deduction, Who Signs the Penalty Check?]]>https://www.dmihaylov.com/p/if-your-ai-hallucinates-a-10m-deductionhttps://www.dmihaylov.com/p/if-your-ai-hallucinates-a-10m-deductionWed, 08 Apr 2026 08:03:43 GMT

I have been watching the rapid rollout of AI assistants across the tax/accounting/finance tech stack—from Brex Assist and Xero’s JAX to Intuit Assist. These tools are incredible at turning fragmented, manual processes into centralized, automated engines.

But amidst these rapid developments, I've been asking the same question: if your AI “hallucinates” a €10M tax deduction based on a misinterpretation of a cross-border VAT treaty, who signs the penalty check?

So, I decided to analyse the consequences focusing on two aspects:

  1. What are the current guidances on using AI for tax reporting from Tax Authorities around the world? (note: of course, my research is not tax advice and is based on publicly available sources. Consult tax professionals for further guidance.)

  2. How should product manager, engineers, and tax managers approach incorporating these guidances when integrating AI into critical systems?

Global Research Summary: AI Liability & Tax Compliance (2026)

To answer the first question, I dove into publicly available, country-specific guidances. Here are some observations from across the globe based on publicly available articles.

1. The UK & EU: The “Liability Hammer”

  • United Kingdom: The FRC’s March 2026 guidance is the global gold standard—audit partners and directors are 100% accountable for AI outputs. The precedent set in Gary Elden v HMRC (2026) proves that “unchecked AI use” is now legally categorized as professional incompetence. As the regulator says: “You can’t blame it on the box.”

  • European Union: As of August 2026, the EU AI Act is fully enforceable. Penalties for “high-risk” financial AI errors can hit €35M or 7% of global turnover.

  • France & Poland: France’s 2026 e-invoicing mandate and Poland’s KSeF system have removed the buffer. Taxpayers—not their software providers—remain solely liable for every data point transmitted.

2. APAC: Governance of “Agentic” AI

3. LATAM: Transparency & “Risk-Based” Compliance

4. North America: The Enforcement Surge

  • USA (IRS): The IRS has expanded its AI use cases to 126, with a focus on large partnerships. They use AI to catch the “hallucinations” in your filings. Without contemporaneous support, your AI-driven deduction is a red flag.

  • Canada: The CRA 2026-27 Plan treats AI errors the same as manual ones.

Based on this research, here is a summary of where global authorities sit on the “AI Sentiment Spectrum”:

Tax Authorities’ Sentiment to AI - Selected Examples

Guide to the Spectrum:

  • Zone 1: Innovation-First

    • Core Philosophy: AI is viewed as a national competitive advantage. Regulators prioritize adoption and “safe harbor” provisions for early movers.

    • Examples: In Singapore, Budget 2026 offers massive tax deductions (up to 400%) for AI implementation, provided companies follow the Model Governance Framework for Agentic AI.

    • The Trade-off: High transparency is expected, but the focus is on “Cooperative Compliance” rather than punitive measures.

  • Zone 2: Balanced Scrutiny

    • Core Philosophy: “Trust, but verify.” These authorities use AI aggressively for their own enforcement (e.g., the IRS’s 126 use cases) and expect taxpayers to maintain a high bar of documentation.

    • Examples: Australia (ATO) and the USA have effectively mandated “Contemporaneous Support.” If an AI picks a deduction, you must be able to surface the underlying data trail immediately.

    • The Trade-off: No special penalties for AI errors yet, but standard penalties are applied strictly with a lower tolerance for “clerical error” excuses.

  • Zone 3: Liability-First

    • Core Philosophy: “The AI black box is no defense”. Algorithmic mistakes are treated as human professional negligence.

    • Examples: The EU AI Act (enforced Aug 2026) imposes fines up to 7% of global turnover. In the UK, the FRC’s stance is that a hallucination is proof of a lack of professional skepticism.

    • The Trade-off: Speed is secondary to Auditability. In these regions, a “Black Box” AI system is essentially a ticking legal time bomb for a CFO.

While most Tax Authorities accept the adoption of AI, their tolerance for algorithmic error varies wildly from leniency to professional negligence. My prediction is that, depending on how fast AI for tax reporting matures, most authorities will become stricter on penalising errors attributed to AI.

So, what do we do to prepare for this seismic shift? In the upcoming sections, I am giving some thoughts on how to prepare.

1️⃣ The Tech Maturity Gap: Breaking the “Tech Stack Prison”

In my experience building at S&P 500 scale, the biggest risk isn’t the AI itself—it’s the Maturity Gap. According to the Thomson Reuters 2026 Corporate Tax Department Technology Report, the term describes the relative advancement of a company’s tech stack through the different stages of technological maturity, which refers to the typical steps that a corporate tax department travels through on its technological journey toward a more proactive approach to tax work:

According to the report, 55% of the surveyed companies are stuck in Phase 2: Reactive. This stage includes:

  • Using tax department databases, some 3rd party software, with some auto mated feeds, but not connected to enterprise data or departments across the company.

  • Tech budget shared with multiple departments

  • Hired a tech consultant or outsourced tech resources

  • No technology training provided

  • Needs investment in process automation/tech solutions for most time-consuming activities (tax data management, transfer pricing, FATCA/CRS/AEOI )

  • More robust metrics needed to measure succes

In essence, these companies have an outdated tech stack which makes any scalable AI usage impossible. To escape this “tech stack prison,” they must move up the Technological Maturity Curve to get to Optimized & Predictive (Stages 4-5). In those, the tax logic is a standalone platform capability. This is the only way to scale sustainably.

2️⃣ The Audit Trap: Defining “Reasonable Care” in a Neural Network

How do you prove “reasonable care” when your tax logic is buried in a neural network?

In a traditional audit, you show your work via spreadsheets or law citations. In an AI world, regulators are shifting the definition of “care” from what you filed to how you governed the system that filed it.

As the ACCA’s 2026 Topical Guidance notes: “Members should avoid overstating the accuracy of the data output... Undertaking appropriate due diligence on the output provided by an AI tool can help safeguard against the risks.”

The “A.C.E.S.” Framework for Defensible AI

To get a step closer to complying with the updated reasonable care definition, I suggest the A.C.E.S. Framework—a four-pillar approach to ensuring any AI is audit-ready and legally defensible:

The A.C.E.S Framework for Defensible AI in tax reporting

Through these pillars any company using AI for tax reporting can start framing its defensible tax reporting as the ultimate audit trap isn’t the AI’s error—it’s the inability to explain it. In a world of probabilistic logic, ‘reasonable care’ requires a deterministic framework such as A.C.E.S.

3️⃣ The Penalty Reality: Moving to “Rules as Code”

If you are sourcing a system today, move beyond the “AI” hype. Tax requirements shouldn’t be a one-off ticket; they need to be a reusable logic library.

As organizations like the OECD and OPSI have championed in their Rules as Code (RaC) frameworks, the goal is to transform legislation into machine-consumable code. To incorporate this at your enterprise, I suggest the you consider the following:

  1. Rule Atomization: Decouple tax logic from your ERP. Store rules in a modular “Source of Truth” (using engines like OpenFisca or Drools).

  2. API-First Consumption: Expose these rules via a Tax Service Layer. Any system—whether it’s a checkout page or a GenAI assistant—must call this central library for a deterministic check.

  3. Governance Wrappers: Wrap AI probabilistic insights within human-defined, deterministic rules to ensure an immutable audit trail.

In case you source your tax reporting software from a 3rd party, make sure those are in place before signing a contract.

🎯 The Bottom Line: Strategic Mandates for Defensible Tax Reporting Using AI

The era of “black box” tax automation is officially over. To reach the next level of global digital transformation and mitigate the €35M risk, senior leadership must adopt the following three mandates:

I. Accountability: Human-Centric Liability

“You cannot blame it on the box.” Regulatory mandates in the UK (FRC) and the EU (AI Act) have made it clear: liability for AI hallucinations sits squarely with human partners and company directors. Neither the software vendor nor the AI model can hold the burden of proof. Organizations must move from passive consumption of AI outputs to active, documented oversight.

II. Architecture: Transitioning to the “Predictive” Stage

The “Tech Stack Prison” is one of the greatest barrier to AI-driven efficiency. Companies stuck in Stage 2 (Reactive) lack the data integration required to validate AI insights. To scale sustainably, the tax logic must be decoupled from the ERP and implemented as a standalone platformcapability. This shift allows for the validation of probabilistic AI guesses against hard-coded legal rules in real-time.

III. Defense: Re-Defining “Reasonable Care”

In a traditional audit, care is shown through spreadsheets. In an AI-native world, auditors will inspect your governance. Institutionalizing AI governance through a framework such as A.C.E.S. or another is the only way to satisfy the “Reasonable Care” requirements of modern tax authorities. Without a framework that allows you to “replay” the logic of a 2024 transaction in a 2026 audit window, your AI-driven deductions will be treated as red flags.

The Final Takeaway: AI represents the “Product Heart” of the modern finance stack, but it requires a “Compliance Brain” to function safely. To win in the current Global Sentiment Spectrum, you must build for transparency and auditability, not just for speed and cost-reduction.

References & Sources

]]><![CDATA[When Compliance Blocks Growth: Escaping the Tech Stack Prison]]>https://www.dmihaylov.com/p/when-compliance-blocks-growth-escapinghttps://www.dmihaylov.com/p/when-compliance-blocks-growth-escapingWed, 01 Apr 2026 08:19:53 GMT

Imagine that a Tax, Accounting, and Finance team consists of 100 people. 80 of them are doing work a script could do in 4 seconds. Why?

Because that team is trapped in what I would call a “Tech Stack Prison.” According to the newly released March 2026 study by Thomson Reuters, tax teams are trapped extracting data from fragmented ERPs, shared drives, and disparate CRMs. Tax teams that eliminate these structural, manual bottlenecks reduce preparation time by 50%—reclaiming more than 10,000 hours annually. The report also highlights that human error at scale is the silent killer of enterprise agility. A single decimal-point error, multiplied across millions of global transactions, doesn’t just result in an overpayment; it also triggers audits and blocks new market expansion. A single decimal-point error, multiplied by a quarter, creates massive financial liability.

If you lead Finance or Tax at an S&P 500 company or a hyper-growth scale-up like Anthropic or OpenAI, your team already knows the law. They understand the regulations natively. What they probably lack is the technical architecture to execute that knowledge at scale.

This is where the “Compliance Brain” needs the “Product Heart.”

To prevent compliance from blocking top-line growth, Finance and Tax leaders must stop thinking like back-office administrators and start operating like Product Managers. Here are five architectural strategies to break your team out of the tech stack prison:

1️⃣ Escape the Manual Loop via Automated Pipelines

Don’t drown your top talent in arduous reconciliations. The modern standard is to partner with your product and engineering teams to replace fragmented ERP data extraction with automated API pipelines. By feeding a centralized data warehouse, you establish a single source of truth. You turn a chaotic, manual data-gathering exercise into a centralized, automated engine that provides auditability in days, not months.

2️⃣ Eradicate the Spreadsheet Version Nightmare

A comprehensive Bloomberg Tax survey found that 92% of corporate tax professionals view their workpapers as critical. Yet, most rely on legacy spreadsheets that lack cell-level tracking and robust access controls. Moving out of the tech stack prison means adopting systems with embedded native controls. You need automated roll-forwards, version control, and cell-level audit trails so that “version_final_v4.xlsx” never compromises your compliance posture again.

3️⃣ Data Workings at S&P 500 Scale

What I learned while scaling global operations at Uber is that compliance isn’t just about calculating the tax rate; it’s about the data trail. Today, AI can identify anomalies in millions of transactions and generate reporting at a massive scale. But modern tax and finance tech shouldn’t just flag an error; it should simulate the fix upstream.

Shift your system from reactive error-catching to proactive, systemic resolution.

4️⃣ Move from Historical Reporting to Predictive Exposure

As outlined in EY’s Global Tax and e-Invoicing strategies, governments are shifting to near real-time compliance (like the EU’s ViDA or LATAM’s continuous transaction controls). They want your data before the transaction is finalized. Your tech stack must evolve from historical reporting to predictive analytics. AI should be leveraged to identify tax exposure and nexus risks before they materialize, allowing you to build compliance roadmaps that accelerate—rather than delay—business expansion.

5️⃣ Tax as a Product, Not a Project

Tax requirements shouldn’t be a one-off IT ticket every time a regulation changes. You need to build a reusable logic library. When your company decides to expand into its next market, you shouldn’t have to rebuild your tech stack from scratch. A modular, product-led architecture allows you to enter 100+ jurisdictions seamlessly because the foundational logic is already centralized and adaptable.

For modern enterprises, compliance is merely the baseline. Efficiency, real-time insight, defensible data trails, and enabling business outcomes are the true prizes.

Take a hard look at your current infrastructure: Is your tax logic a standalone “to-do” list, or is it baked directly into your core product roadmap?

Sources

For those looking to dive deeper into the architectural shifts and data driving this transformation, I highly recommend reviewing the foundational research:

]]><![CDATA[The End of the Compliance Factory: Why Tax Teams Must Become AI Orchestrators]]>https://www.dmihaylov.com/p/the-end-of-the-compliance-factoryhttps://www.dmihaylov.com/p/the-end-of-the-compliance-factoryTue, 31 Mar 2026 11:10:11 GMTThe most baffling paradox in modern enterprise is this: the Tax and Finance domain is the most deterministic field within a company, yet its level of automation remains embarrassingly low. According to the Thomson Reuters 2026 Corporate Tax Technology Report, a majority of departments are still stuck in a “reactive” state, even as global mandates like France 2026 make real-time reporting a requirement, not an option:

Source: Thomson Reuters 2026 Corporate Tax Technology Report

It looks like in a world governed by binary tax code and rigid regulatory frameworks, we are still burning through thousands of human hours on manual reconciliations.

The Structural Collapse of the Pyramid

For decades, Tax departments were built as pyramids: a massive base of junior staff handling the “routine banality” of data entry. That model is dead.

As PwC recently highlighted, we are shifting to a diamond-shaped talent structure. The base of manual preparation is shrinking. What is expanding is the center: a core of Financial AI Agent Orchestrators. In this new model, the "middle" expands with skilled professionals who don't prepare tax returns but orchestrate the AI agents that do.

The Shift: From “Worker” to “Orchestrator”

In a deterministic world, the “What” (the law) is fixed. The “How” (the implementation) is where the S&P 500s lose millions in efficiency. Here is how the high-growth tax team must evolve:

  • From Data Entry to Logic Design: Instead of fixing errors in a spreadsheet, the Orchestrator designs the logic gates that prevent the error from ever hitting the ERP.

  • From Reporting to “AI Ops”: We don’t need people to generate reports. We need professionals who can troubleshoot the automated engine, ensuring AI agents don’t hallucinate jurisdictional logic.

  • From Analysts to “Product Managers for Tax”: The most valuable hires won’t be tax specialists; they will be “multidisciplinary units” who can translate a regulatory update into a technical roadmap.

The 2026 Mandate for Tax Leaders

If your team is still spending 80% of their time on work that a script could do in 4 seconds, you are building a massive “human-heavy debt”. The world is moving to Continuous Transaction Controls. You cannot “human” your way through a 5-second API validation window.

Better start planning your tech stack and team skills now. The alternative is a “Compliance Brain” that eventually chokes your “Business Heart.”

Sources:

]]><![CDATA[The 61% Blind Spot: Why Your Tax Tech Strategy is Ignoring the CEO]]>https://www.dmihaylov.com/p/the-61-blind-spot-why-your-tax-techhttps://www.dmihaylov.com/p/the-61-blind-spot-why-your-tax-techTue, 31 Mar 2026 10:34:52 GMT

According to the Thomson Reuters Institute’s 2026 Corporate Tax Department Technology Report, 61% of tax departments surveyed are ignoring the only metric that actually matters to the C-suite: business outcomes. Instead, they are still measuring themselves by “Tax Internal” yardsticks. The survey shows they prioritize time savings (66%) and accuracy (55%):

Source: Thomson Reuters Institute’s 2026 Corporate Tax Department Technology Report

To avoid basing my conclusions on a single report, I had a quick look at what the “Big Four” think about this. The consensus is that measuring “adoption” or “accuracy” alone is no longer sufficient; tax departments must now prove their impact on the enterprise P&L and growth speed.

Main Conclusions:

  • PwC (AI-Driven Value): Predicts a shift from “ground-up” crowdsourced AI to “top-down” enterprise strategies. Metrics are moving toward “proof points” that link AI-driven tax workflows to market differentiation and P&L impact.

  • KPMG (Value Driver Trees): Recommends identifying “functional levers” that impact the broader organization, such as optimizing indirect tax cash flow and minimizing irrecoverable taxes to boost working capital.

  • EY (Controversy & Policy): Focuses on “Tax Risk & Controversy” as a business metric. They suggest that “Time to Resolution” and “Data Readiness for Pillar Two” are critical KPIs to prevent tax from becoming a bottleneck during global expansion.

  • Deloitte (Resource Mix): Suggests that the “Optimal Resource Mix” (balancing AI, outsourcing, and in-house talent) should be measured by its ability to free up the tax team for “high-value, strategic activities” rather than just cost reduction.

Don’t get me wrong—accuracy is a baseline requirement. It’s the “Compliance Brain” doing its job. But if your tax tech stack only solves for accuracy, you aren’t a strategic partner; you’re an insurance policy.

To move from being a cost center to a value driver, we need the “Product Heart.”

The “Tech Stack Prison” of Internal Metrics

At the S&P 500 scale, “efficiency” is a generic goal. If you save 1,000 hours but your tax logic blocks a new market launch for three months, the net value to the business is negative.

When tax tech is built as a standalone “to-do” list rather than being baked into the product roadmap, it becomes a blocker. We focus on the data trail for the audit we might have in three years, while the business prioritises the checkout completion rate today.

The Pivot: Adopting “Business-First” OKRs

Without a doubt, Tax Departments must bridge the gap between “tax internal” metrics and “business outcomes.” But what can those be?

To align with the “Product Heart” philosophy, these OKRs are categorized by Compliance Hygiene (Efficiency) and Strategic Enablement (Business Outcomes).

Category 1: Business Enablement & Speed (The “Product Heart”)

  • Objective: Accelerate global expansion and product agility through tax automation.

    • KR 1: Reduce “Tax-to-Market” Latency (the time to configure tax logic for a new jurisdiction) from X weeks to < 48 hours.

    • KR 2: Achieve a 0% Tax Latency impact on checkout completion rates by moving tax calculation logic to the “edge” or a real-time engine.

    • KR 3: Increase the “Tax Incentive Capture Rate” (e.g., R&D credits, OB3 Act benefits) by 15% through automated data extraction.

Category 2: Cash Flow & Financial Performance

  • Objective: Maximize free cash flow and EBITDA through strategic tax optimization.

    • KR 4: Increase Free Cash Flow by $X million through real-time VAT/GST recovery automation.

    • KR 5: Maintain an Effective Tax Rate (ETR) variance of < 1% between forecast and actuals, enabling more predictable capital allocation.

    • KR 6: Reduce Irrecoverable Tax as a percentage of total spend by X% via improved vendor onboarding logic.

Category 3: Compliance Hygiene & AI Maturity (The “Compliance Brain”)

  • Objective: Transition to an “agentic” tax operating model to eliminate manual loops.

    • KR 7: Automate 95% of standard tax reconciliations using AI agents, reducing manual FTE hours by X%.

    • KR 8: Achieve Audit-Ready Status within 24 hours for any jurisdiction via a centralized “Tax Data Lake.”

    • KR 9: Maintain a 0% Audit Penalty rate on all “real-time reporting” mandates (e.g., EU e-invoicing).

If you want a seat at the table where the CEO decides on the next $1B investment, your tax tech needs to speak the language of the business.

“We need to stop building tax as a project and start building it as a product. The CEO doesn’t care about the ‘how’ of our reconciliations; they care about the ‘if’ of our global expansion.”

Sources:

]]><![CDATA[When Compliance Becomes a Single Point of Failure: The High Cost of 5 Seconds]]>https://www.dmihaylov.com/p/when-compliance-becomes-a-singlehttps://www.dmihaylov.com/p/when-compliance-becomes-a-singleTue, 31 Mar 2026 09:56:39 GMT

In the world of S&P 500 scale and high-growth scale-ups, the line between “Tax Compliance” and “Revenue Operations” has officially vanished.

We’ve entered the era of Continuous Transaction Controls (CTC). In this world, tax determination isn’t a monthly reconciliation task; it is a live dependency baked into your checkout flow.

The Hidden Risk: Compliance as a Single Point of Failure

The stakes in 2026 are binary. If the API to a tax authority hangs for 5 seconds, your checkout hangs for 5 seconds. In the mobility world, 5 seconds is an eternity—it is the difference between a completed ride and a frustrated user switching to a competitor.

If your data is flawed or your integration is “brittle,” your transactions won’t just be misreported; they will fail to complete entirely. You haven’t just added a compliance step; you’ve added a single point of failure to your revenue stream.

Based on my experience at Uber, here is how you move to a resilient, product-led tax infrastructure:

1️⃣ Robust, Real-Time Calculation: Your tax engine must handle complex logic—location-based rules, exemptions, and thresholds—in milliseconds. If your calculation can’t keep pace with a high-concurrency order service, your architecture is already broken.

2️⃣ Asynchronous Orchestration: Does the transaction move forward while the tax validation runs in a prioritized queue? Or is the user staring at a loading spinner? Build your user flows with contingency in mind. At Uber, we prioritize the user experience while ensuring the tax logic catches up in parallel.

3️⃣ Continuous Data Quality (DQ) Checks: Don’t wait for an audit to find a schema drift. Schedule automated DQ checks to catch mapping errors before the data ever hits a tax authority. At Uber, data points are checked continuously against external databases to ensure success before the API call.

4️⃣ The “Offline Mode” Fallback: What happens when a government server goes dark? You need fallback logic that preserves data integrity without killing the UX. Invest in smart retry mechanisms. At Uber, we invest a lot in smart retry mechanisms. Nothing else will scale in this case.

5️⃣ Agentic Troubleshooting: Manual troubleshooting is the silent killer of the S&P 500. When a document fails, you need an AI Agent that can trace the error, suggest the fix, and re-trigger the submission. This is a deterministic task—perfect for AI agents, not for your high-priced tax team.

“In a CTC world, Tax is no longer a ‘To-Do’ list for the back office; it is a live API dependency for the business operations. If your tax stack isn’t as resilient as your payment gateway, you aren’t just at risk of an audit—you’re at risk of revenue leakage.” 🏃

The Takeaway for Leaders: Your goal is to build tax technology that never blocks business operations. Is your tax logic a standalone project, or is it baked into your product roadmap?

Stop treating Tax as a project. Start treating it as a Product.

Sources:

]]><![CDATA[The Xero-Anthropic Partnership: AI Speed vs. Accounting Accountability]]>https://www.dmihaylov.com/p/the-xero-anthropic-partnership-aihttps://www.dmihaylov.com/p/the-xero-anthropic-partnership-aiFri, 27 Mar 2026 12:04:17 GMTXero’s recent partnership with Anthropic to integrate Claude AI directly into the platform isn’t just another feature drop; it’s a shift in how small-to-mid-market (SMB) finance teams will operate. For Tax Tech leaders, this represents the classic tension: the Product Heart wants the speed of instant forecasting, while the Compliance Brain fears the “hallucinated” tax filing.

If you are leading tax or accounting technology for an organization in the Xero ecosystem, the mandate has shifted from “How do we record this?” to “How do we verify what the AI just did?”

The Prize: Compressing the Financial Cycle

A modern technical mixed-media illustration of a clean, minimalist executive office with a soft teal and slate grey watercolor wash. A high-end laptop sits on a light wood desk next to a notebook and lamp. Floating above the laptop screen is a translucent digital dashboard featuring a large bar chart titled "Cash Flow Forecasting" and a global map with dark linework, representing real-time financial intelligence.

The immediate upside of integrating Claude’s reasoning via Just Ask Xero (JAX) is the elimination of the “Manual Reconciliation Loop.” By moving from a “System of Record” to a “System of Action,” we are seeing the realization of what Xero calls Accountable Intelligence:

The Risks: Navigating the AI “Black Box”

While the efficiency gains are seductive, the stakes can also be quite high. Here are some serious risks to take into account:

1. The Hallucination Hazard

LLMs are built for conversation, not calculus. While Claude uses highly sophisticated models for reasoning, it may still struggle with complex, multi-jurisdictional tax logic. If the AI “hallucinates” a reconciliation that satisfies the software but fails an audit, the liability sits with the company, not the AI provider. An example of such a recent case is Moffatt v. Air Canada (2024). It is a significant legal precedent for AI and enterprise that happened recently. Air Canada’s chatbot “hallucinated” a bereavement fare policy that didn’t exist, promising a passenger a retroactive discount that contradicted the airline’s actual written policy.

  • The Outcome: The court rejected Air Canada’s defense that the chatbot was a “separate legal entity” responsible for its own actions.

  • The Lesson for Tax Tech Leads: In the eyes of the law, the tool is an extension of the company. If Claude suggests a VAT treatment in Xero that contradicts tax law, “the AI told me so” is not a defense. You are legally responsible for every action taken based on an AI suggestion.

2. The Data Privacy Moat

Xero has secured a “non-training” guarantee, meaning your company’s balance sheet won’t become part of Claude’s next public update. However, data leakage isn’t just about training; it’s about access. Tax leads must ensure that the “Just Ask Xero” (JAX) interface respects existing user permissions. There is a recent example connected to this. While not involving Xero, the 2023 Samsung case is the gold standard for data privacy risks. In that case, engineers inadvertently leaked proprietary source code and meeting notes by using ChatGPT to summarize them.

  • The Material Risk: Once data is input into a public-facing LLM without enterprise-grade “Zero-Retention” or “No-Training” APIs, it becomes part of the model’s knowledge base.

  • Xero Context: Even with Xero’s security guarantees, it is still worth making sure that if a junior accountant uses Claude to analyze a “sensitive payroll file” in Xero, the data stays within the secure Xero/Anthropic tunnel, and it is not cached in a way that violates internal GDPR or SOC2 protocols.

3. The Vanishing Audit Trail

In a traditional system, you can trace a journal entry to a user ID and a timestamp. In an AI-augmented system, if the AI “suggests” an entry and a human clicks “Approve” without checking the source, the audit trail becomes a game of telephone. The same can happen if the human in the loop misses some “ghost regulations” the model uses to suggest decisions. For example, in multiple jurisdictions, lawyers have been sanctioned for submitting briefs that cited completely fabricated legal cases (e.g., Mata v. Avianca). The AI created citations that looked perfectly formatted but were “ghost cases.”

  • Financial Reality: This has already moved into the accounting world. Recent reports (The Logic, 2025) show that 44% of accountants now spend up to three hours a month correcting AI-generated mistakes from clients who relied on “authoritative-sounding” but incorrect tax advice.

  • Material Example: An AI might suggest a “Small Business Credit” for your specific SIC code based on an outdated 2022 regulation that has since been repealed.

All in all, at least for now, I would keep in mind that

AI shouldn’t be your accountant; it should be your highest-performing intern—capable of incredible work, but requiring a strict review process.

The Strategic Playbook

A professional mixed-media illustration of a quiet luxury workspace. A translucent holographic screen floats above a laptop, showing a hand interacting with a digital "Confirm" button. The interface displays an "Audit Ready" badge with a secure lock icon and a checklist next to a global map, representing the transition from AI-generated suggestions to verified, human-approved accounting records.

What I learned at Uber is that scaling isn’t just about calculating tax; it’s about the data trail and the cost of technology. To leverage this partnership without compromising your defensible tax position, I recommend applying the following restrictions to your data trail:

  1. Institute Rigorous Verification Processes: Use the Accountable Intelligence framework to ensure every AI-generated entry links back to a specific, real, correctly interpreted data point. If the AI can’t “show its thinking,” don’t trust and use - verify first!

  2. Audit Your Permission Logic: Ensure your internal data security protocols match the new “agentic” reality. You don’t want everyone to be able to pull executive salary information within Claude.

  3. Human-in-the-Loop is Mandatory: Use AI to flag and simulate, but keep the action authorisation under human control.

When it comes to the cost of technology, before fully committing your workflows to the Xero x Anthropic integration, evaluate each use case (e.g., automated categorization, cash flow forecasting) against some metrics. Here are some that come to mind (note: of course, you must adapt these examples to your company’s context):

  1. Velocity of Close: Metric: % reduction in “Hours to Close.”

    • The Goal: If JAX can handle the “manual effort” of low-risk reconciliations, your team should be shifting a % of their time from data entry to anomaly simulation and detection.

  2. Error Rate & Correction Cost: Metric: Number of AI suggestions rejected vs. approved.

    • The Risk: High-volume hallucinations. If your team spends a substantial amount of time each week correcting Claude’s reasoning, the “efficiency” is a mirage. Refer to the CPA.com 2025 Report to benchmark your correction burden.

  3. Regulatory Auditability: Metric: Availability of Verified Citations per AI-generated entry.

    • The Mandate: Tax requirements shouldn’t be a one-off ticket. Does the AI help you build a reusable logic library for 100+ jurisdictions, or are you rebuilding your stack every time you enter a new market?

  4. API & Resource Arbitrage: Metric: Cost of Xero Developer Tiers vs. Headcount Savings.

    • The Calculation: Compare the cost of high-tier API access and “agentic” compute against the cost of manual reconciliations. If you update the VAT logic once via AI and save 4,000 manual fixes tomorrow, the ROI is defensible.

Sources:

Official Announcements & Product Overviews

Data Privacy & Security Frameworks

Industry Analysis & External Perspectives

Technical & Pricing Updates

]]><![CDATA[The Tax Tech Skill Gap: Why Your Next Hire Shouldn't Be a Tax Expert]]>https://www.dmihaylov.com/p/the-tax-tech-skill-gap-why-your-nexthttps://www.dmihaylov.com/p/the-tax-tech-skill-gap-why-your-nextWed, 25 Mar 2026 14:25:28 GMTIf you are thinking of what’s next in tax technology, it’s worth regularly scanning the job ads of Big Four. A quick look reveals the following:

A data table titled 'Big Four Tax Technology Hiring Snapshot' organized into five columns: Type of Professional, Job Title, Positions (Est.), Main Skills Required, and Source. The table reveals high-volume hiring for technical roles across the Big Four: Engineering: Senior Full Stack Engineer/GenAI Lead (150+ positions) at Deloitte, requiring React, Python, and RAG. Product Management: Senior Technical Product Manager (40+ positions) at EY, requiring Agile/Scrum and Snowflake. Architecture: Solutions Architect, Tax Transformation (25+ positions) at Deloitte, requiring Microservices and API-first design. Data & Automation: Innovation Associates and Data Automation roles at PwC and KPMG, requiring Java, Python, SQL, and Alteryx. Specialized Product: XBRL Product Manager at EY, requiring XML/iXBRL and SQL.
Note: Data derived from active global recruitment portals at Deloitte, EY, PwC, and KPMG on 25/03/2026

If you are still hiring exclusively from audit backgrounds for your tech roles, you are being outpaced by the very firms you likely hire for advice. The global hiring boards for the Big Four reveal that they have pivoted. They are no longer just accounting firms—they are software houses. They have already realized that compliance is the output of a high-performing engineering engine.

Here is what to do first. Continue reading.

The DIY Architecture Trap

This image visually interprets the "Tech Stack Prison." It shows a single figure (representing a Tax Director trying to build alone) overwhelmed by a chaotic, tangled mess of glowing red and blue lines. These lines represent hardcoded, synchronous logic. A "floating hologram" in the background displays a bar chart with spikes labeled as latency and API errors, showing that this architecture is actively blocking business data flows. The watercolor washes are heavy on the slate grey to emphasize frustration.

Probably you or somebody you know in Tax/Accounting/Finance have been in this hell hole already: you have a mandate to modernize your global tax architecture. You secure the budget, you gather your brightest team members, and you set out to ensure a system that can handle continuous transaction controls and real-time reporting.

Twelve months later, complaints that you are blocking the business are piling up, and your team is trapped in an endless cycle of trial and error. What went wrong?

You tried to build a software product using only a “Compliance Brain.”

In the S&P 500 and hyper-growth scale-up world, building tax technology is no longer a finance project. It is a hardcore software engineering and product management discipline. If your team is composed entirely of former auditors, tax specialists (ex-Big Four), and CPAs, you are already behind.

When tax professionals are tasked with building or integrating technology, they naturally optimize for the only thing they have learned matters: rules and regulations.

They will map out the most incredibly detailed, compliant logic trees imaginable. But translating that logic into a high-concurrency architecture that handles millions of transactions in milliseconds? That is a different language entirely.

Without seasoned tech professionals at the helm, tax teams often face grave challenges. In my work with different tax teams, I have observed:

  • The Single Point of Failure: Hardcoding tax logic rather than using microservices, for example.

  • The Latency Killer: Designing synchronous API calls to external tax engines that block the user experience if the government server takes 5 seconds or 24 hours to respond.

  • The Tech Stack Prison: Accumulating massive technical debt because “quick fixes” were implemented to hit a compliance deadline, making future expansion nearly impossible.

Tax professionals know the rules and regulations, but they often lack the skills and background to build and maintain tax technology at scale.

The Stakes: Why Trial and Error is Not an Option

In the era of continuous transaction controls (like ViDA or France 2026 E-Invoicing or Mexico 2026 Digital Reform), tax is no longer a post-transaction, end-of-month reconciliation exercise. It is a live dependency.

If you get the architecture wrong, the stakes aren’t just an audit finding three years down the line. The stakes are blocked payments or onboarding flows today. A 5-second delay in e-commerce or many other online businesses is an eternity. If your system hangs, you lose the revenue. You haven’t just added a compliance step; you’ve added a bottleneck to the core business.

You do not have the time to let a finance team “figure out” cloud infrastructure through trial and error.

The Playbook: Hire the “Product Heart”

A modern, technical mixed-media illustration of a cross-functional team including a product manager, software architect, engineer, designer, and tax auditor collaborating in a minimalist office. The scene features clean ink linework and soft watercolor washes in cool blues and teals. A large wall-mounted screen displays a microservices system architecture, while translucent 'UI Holograms' in the foreground show glowing bar charts specifically labeled 'API Latency', 'Number of Sign-ups', and 'Number of Completed Orders'. Subtle mobility icons, such as a train and a bus, are integrated into the background display

To escape the DIY loop, you need to bring the “Product Heart” into the compliance function. Use this checklist for your next round of interviews to ensure you aren’t just hiring another “Compliance Brain”:

  • The Product Test: Can the person explain a tax requirement as a “User Story”? (e.g., “As a marketplace seller, I need to see my tax breakdown in real-time so I can price my goods competitively.”)

  • Architecture Fluency: Do they understand the difference between a synchronous and asynchronous API call? (If they don’t, they will break your checkout flow).

  • Scalability Mindset: Ask: “How would this logic hold up if we processed 1 million transactions per hour?” A tax person will talk about the law; a tech person will talk about load balancing and latency.

  • The Translation Layer: Can they sit between a Tax Director and a Lead Engineer and make both feel understood?

  • Tool Proficiency: Move beyond Excel. Are they comfortable with SQL, Snowflake, Python, Claude Code or at least the logic behind CI/CD pipelines?

A beginner’s mindset supported by product experience is more valuable than blindly applying a tax playbook that worked in an analog world. Stop asking your tax experts to be software architects. Build a true cross-functional tech team.

]]><![CDATA[Micro-Interview: Elitsa Taskova | Chief Product Officer | Nexo]]>https://www.dmihaylov.com/p/micro-interview-elitsa-taskova-chiefhttps://www.dmihaylov.com/p/micro-interview-elitsa-taskova-chiefWed, 18 Sep 2024 07:21:12 GMTINTRO NOTE:

Elitsa Taskova serves as Chief Product Officer at Nexo, the world’s leading digital assets institution. The company’s mission is to maximize the value and utility of cryptocurrencies.  

She built her product career from QA Engineer up — without a traditional PM playbook. That path gives her an unusually clear-eyed view of what product management actually is, stripped of the title politics.

For Tax Product Managers specifically, her answers carry an extra edge: tax products sit at the same intersection she describes — strategy, execution, and real-world consequences — except the “wrong error message” might mean a misfiled VAT return.

In under 500 words, she shares: 

  • How she got into product management…

  • How she starts her mornings for maximum productivity…

  • Some of her sources of learning…

And more…  

Enjoy!

“How did you get into product management?”

I didn’t initially pursue product management; it naturally evolved from my work as a QA Engineer on a product I was passionate about. By immersing myself in understanding the business challenges and opportunities we faced without being confined by roles or titles, I realized that product management isn’t just a title—

It’s about connecting strategy with execution and delivering real value.

Once I recognized that this was where my true interests lay, I chose to continue on this path.  

“How do you start your mornings at work?”

My current position requires me to be highly flexible. I typically start the day by checking Slack, emails, and key industry news. After addressing urgent matters, I carve out time for focused work or meetings. I avoid scheduling meetings or deep work sessions before lunch to ensure all pressing issues are handled first.  

“What do you know about product management now that you wish you’d known when you first started?”

  1. The company shapes the role, not the other way around.

    You can’t be your best as a Product Manager in a poorly functioning company. I suspected this early in my career, but now I’m certain.  So, find a company where you can learn and grow. 

  1. Product management practices vary widely across companies.

    You must be prepared to either learn and adapt or work to change them. It’s unlikely for things to be exactly as you imagine.  

  1. You don’t want everyone on the team to be the same. Diversity is critical for successful product teams (for managers).  

No BS, Just Tax Tech is a reader-supported publication. This post is public, so feel free to share it.

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“What did your biggest product failure teach you?”

Small things can have a huge impact, so you can't afford to remain on the surface; you must always dive deep.

Something as simple as a wrong error message can disrupt an entire flow—I’ve seen it first-hand.  
 
Fully owning and recognizing your mistakes early is crucial for effectively managing the outcomes. It's also important to remain mindful of sunk costs and not let them cloud your judgment when making or sticking with decisions. 

“What’s the #1 thing that has helped you shorten your product management learning curve?”

Understanding that you can't excel at everything and focusing on what truly energizes you and aligns with your inner self is the fastest way to progress and achieve results.

Others' approaches aren’t necessarily yours—learn from them, but don’t simply copy. Work on your confidence and communication skills—they are invaluable in work and life.  

“How do you stay updated on the best practices in product management?”

I read books, follow interesting people on LinkedIn, and subscribe to newsletters. Some of my top recommendations include Marc Randolph, Marty Cagan (of course), Lenny Rachitsky, and Jason Fried, though many more are worth exploring.

🎯 No BS Tax PM Takeaway

Elitsa didn’t plan to be a CPO. She started in QA, got close to the problem, and let the role follow. Sound familiar? The best Tax PMs I know got here the same way — analyst, consultant, implementation lead or something related — until one day they were the person connecting the dots. The title came last.

Her biggest lesson: the company shapes the role, not the other way around. In tax products, this cuts deep. If tax is an afterthought at your company — bolted on, not built in — your ceiling is low no matter how good you are. Pick the company first.

And on failure: in our world, “a wrong error message can disrupt an entire flow” is not a metaphor. It’s a misfiled return. It’s an audit. Staying on the surface is not an option when compliance is the product.

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]]><![CDATA[Micro-Interview: Galina Rezvaya | Lead PM | Acronis | Tips on Influencing Stakeholders]]>https://www.dmihaylov.com/p/micro-interview-galina-rezvaya-leadhttps://www.dmihaylov.com/p/micro-interview-galina-rezvaya-leadThu, 05 Sep 2024 06:40:01 GMTGalina Rezvaya is a Lead Product Manager at Acronis, a global cyber protection company that provides natively integrated cybersecurity, data protection, and endpoint management for MSPs, SMBs, and enterprises. 

In under 500 words, she shares: 

  • How she determines the most critical stakeholders to work with…

  • What her most formidable stakeholder challenge was…

  • Some of her sources of learning…

And more…  

Enjoy!

“How did you get into product management?”

After trying out roles in quality assurance, project management, and development, I moved into product management. I felt that these roles did not fully utilize the combination of my technical and communication skills. I expressed my interest in product management to my colleagues, and the idea received support. Watching my colleagues work on business tasks and technical requirements increased my interest, and their support was crucial.

“How do you determine the most important stakeholders for you?”

Determining the key stakeholders depends on what you're trying to achieve.

Once you set a clear and achievable goal, you can see how to get there. You mainly follow set processes and make sure everyone is aligned. In other cases, you need to figure out what essential changes are required and what steps need to be taken to make those changes.

“What do you know about influencing stakeholders now that you wish you’d known when you first started?”

The most effective way to influence stakeholders is to present clear, data-supported arguments. You don't need anything else if you understand your business well and can make strong arguments with data.

I wish I had known from the start the importance of finding common goals with stakeholders so that everyone feels involved and works harder rather than trying to convince everyone to see things from my perspective.

No BS, Just Product is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

What did your toughest stakeholder challenge teach you?

The biggest challenge has been, and likely always will be, managing the lack of time due to the high team load. This has taught me to be creative and proactive. Instead of waiting my turn and giving up, I learned to think differently and act quickly.

When solving problems on my own or with the support of your team, I take the initiative rather than wait.

This roughly describes how I learned Python and how to write marketing texts. I've also learned the importance of investing in my team. A team of true professionals always helps you excel. 

What’s the #1 method that has helped you align all stakeholders in your favor?

To align stakeholders, I try to think from their perspective and speak their language. This means discussing technical details with developers and benefits and value with marketing teams.

Being able to switch between discussing business and technical topics is key for every product manager.

How do you stay updated on the best practices in influencing stakeholders?

I don't follow any specific program to stay updated. Occasionally, I read articles (HBR, Medium, and what I get from Google Alerts) or take courses in relevant areas. I think my natural curiosity drives me to learn how other departments operate, what processes they use, and how they measure success. I think that helps the most. 

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]]><![CDATA[Micro-Interview: Rosen Georgiev | Chief Product Officer | eBag]]>https://www.dmihaylov.com/p/micro-interview-rosen-georgiev-chiefhttps://www.dmihaylov.com/p/micro-interview-rosen-georgiev-chiefWed, 28 Aug 2024 05:04:35 GMTINTRO NOTE:

Rosen Georgiev is the Chief Product Officer at eBag, Bulgaria's largest food and beverage online retailer. The company owns and operates a comprehensive platform that integrates logistics, transport, e-commerce, finance, and other domains. It is dedicated to delivering orders within 3 hours of placing them. 

In under 500 words, he shares: 

  • How he got into product management…

  • How he starts his mornings for maximum productivity…

  • Some of his sources of learning…

And more…  

Enjoy!

“How did you get into product management?”

I have held various leadership roles that required hands-on involvement in key internal and client projects, covering product management, project management, and account management duties. Although product management hasn’t always been my primary role, I have always considered it pivotal for achieving business goals. 

“How do you start your mornings at work?”

I prefer to schedule my meetings in the afternoon, dedicating my mornings to ideation, analysis, and individual discovery work. I review new messages on my way to work so that when I arrive at the office, I can start my day with the most important and complex tasks. I get a cup of coffee and dive into work immediately. I chit-chat with colleagues during lunch breaks or before and after meetings. 

“What do you know about product management now that you wish you’d known when you first started?”

Perfectionism can be your real enemy. Instead of investing too much time in initial discovery and development, it’s better to start with a smaller feature set, learn from the market, and polish the product. 

No BS, Just Product is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

“What did your biggest product failure teach you?”

Get feedback from real users as soon as possible. We worked on a product for two years, and while various stakeholders received it well, it hadn’t been released to end users. When the product was finally launched, it was beautiful and powerful but was downloaded only 50 times. It was a massive failure. 

“What’s the #1 thing that has helped you shorten your product management learning curve?”

We are fortunate to have direct communication with both external and internal users. We learn from our users through open human conversations and field observations, which allows us to learn and adapt very quickly. 

“How do you stay updated on the best practices in product management?”

The public Facebook group ProductTank Sofia and the private community CoTransition Academy are very useful. Broadly speaking, I enjoy a variety of podcasts in psychology and entrepreneurship, such as the Moni Dochev Podcast, Digitalni Istorii, Management Board with Anastas Gogogv, Голям да пораснеш с Мадлен Алгафари. I believe in a holistic approach and seeing the big picture as crucial to successful product development. 

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]]><![CDATA[Micro-Interview: Petya Valkova | Chief Product Officer | Evrotrust]]>https://www.dmihaylov.com/p/micro-interview-patya-valkova-chiefhttps://www.dmihaylov.com/p/micro-interview-patya-valkova-chiefThu, 15 Aug 2024 06:23:57 GMTINTRO NOTE:

Petya Valkova is the Chief Product Officer at Evrotrust Technologies, a Qualified Trust Service Provider offering an end-to-end solution to digitalize every paper-based process: electronic identification, electronic signatures, and electronic delivery services combined to create a fully remote user journey via mobile and web applications. 

In under 500 words, she shares: 

  • How she got into product management…

  • How she starts her mornings for maximum productivity…

  • Some of her sources of learning…

And more…  

Enjoy!

“How did you get into product management?”

I have spent years in the sphere of customer experience, leading teams and projects to improve NPS scores, build online applications, and increase sales satisfaction rates.

My first steps in real product management were back in 2019 when I joined Evrotrust and was assigned to lead the development of a new product from scratch in partnership with a big brand. This gave me indispensable experience that no course or university can replace. 

“How do you start your mornings at work?”

I am not a morning person at all, so the coffee comes first. I select the big wins for the day: it can be 1 or 2 tasks, a meeting to drive an important decision, etc. At the end of the day, these wins make you feel accomplished and able to celebrate daily success. Of course, this is not always the case, but we, the product people, are good at juggling and should be prepared for the unplanned. 

“What do you know about product management now that you wish you’d known when you first started?”

I remember being so frustrated by APIs and SDKs back in the day. I asked, and I learned.

Never be afraid to say, "This is something that I don’t know. Explain/teach me.”   

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“What did your biggest product failure teach you?”

It was hard; to be honest, I took it personally and tried to find where I went wrong. Never overthink; find the lessons learned and make them work for you and the team in the future. 

“What’s the #1 thing that has helped you shorten your product management learning curve?”

Surround yourself with the right team members and achieve together. The team is the most important part. We are people building for people, and the human factor is the ingredient that can make a difference. 

Secondly, dive into the deep and learn from practice. Be open to learning and improving, and never think that you have learned it all. 

“How do you stay updated on the best practices in product management?”

I'm following people with the same interests, mainly on LinkedIn: Dr. Bart Jarowski, Ankit Shukla, and many others. 

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]]><![CDATA[Micro-Interview: Krassi Eneva | Sr. Product Manager | Sitecore]]>https://www.dmihaylov.com/p/micro-interview-krassi-eneva-producthttps://www.dmihaylov.com/p/micro-interview-krassi-eneva-productSun, 21 Jul 2024 11:33:55 GMTINTRO NOTE:

Krassi Eneva is a Sr. Product Manager at Sitecore, a leading digital experience platform that seamlessly integrates content, experience, and commerce solutions.

In under 500 words, she shares: 

  • How she got into product management…

  • How she starts her mornings for maximum productivity…

  • Some of her sources of learning…

And more…  

Enjoy!

“How did you get into product management?”

I entered the digital marketing and web industry over a decade ago, initially convinced it was my lifelong path. However, the exposure to entrepreneurship programs and projects and the opportunity to work on various client solutions in the technology industry shifted my perspective.

Engaging in user interviews and building customer journeys made me develop a passion for empathizing with users and changed my mindset to want to solve our customers' problems. An opportunity at Sitecore to join the Product Management team arose, and I seized it.

“How do you start your mornings at work?”

My mornings kick off with a quick check of Teams for urgent matters, followed by dedicated focus time. It includes research, competitive analysis, and ideation on specific areas we want to improve within the product.

Despite being located in different parts of the world, I feel fortunate to work with an amazing team. Our firm bond allows us to work effectively asynchronously and trust each other highly.

Later, my day revolves around refining the quarterly priorities and ad hoc meetings regarding the short—and long-term goals. This can include checking for new user feedback or insights, reviewing the ideas in our Jira Product Discovery board, and examining the user behavior data reports.

“What do you know about product management now that you wish you’d known when you first started?”

A key lesson was to stop rushing into solution development. Initially inclined towards fast feature implementation, I realized the importance of prioritizing the understanding of the user paint points and market dynamics. The shift from a feature-centric to a problem-solving approach has significantly enhanced my product management approach.

No BS, Just Product is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

“What did your biggest product failure teach you?”

Rather than viewing it as a failure, I consider it a crucial lesson. When tackling something new, engaging with users is the key. Drawing inspiration from "Continuous Discovery Habits” and amazing colleagues and managers, I learned the value of frequent customer interactions. The insights gained from such interviews provide an invaluable understanding of the users and their usage patterns and help build a better relationship with your users.

“What’s the #1 thing that has helped you shorten your product management learning curve?”

Access to exceptional individuals within Sitecore and my circle of friends has been instrumental.

Seeking mentorship from my manager, collaborating with colleagues possessing diverse skills, and embracing constructive feedback have accelerated my learning curve.

Asking questions and actively seeking mentorship have proven to be one of the most effective ways to grow and improve in product management, especially when you are the only product manager in your location.

“How do you stay updated on the best practices in product management?”

Staying informed involves a mix of books, podcasts, and newsletters. I return to these books often: The Design of Everyday Things, Continuous Discovery Habits, and Escaping the Build Trap.

Podcasts, both product management and entrepreneurship-focused, offer diverse perspectives. Some of my favorites right now are Lenny’s podcast and How I Built This with Guy Raz.

Additionally, subscribing to relevant newsletters like this keeps me in the loop on emerging trends and best practices. :)

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]]><![CDATA[Micro-Interview: Kostantin Stambolov, Head of Product | Juicer.io]]>https://www.dmihaylov.com/p/micro-interview-kostantin-stambolovhttps://www.dmihaylov.com/p/micro-interview-kostantin-stambolovWed, 17 Apr 2024 05:27:32 GMTEDITOR’S NOTE:

Kosta is the Head of Product at Juicer.io, a B2B SaaS platform for aggregation, moderation, and analysis of social media content.

As a member of the saas.group family, Juicer embraces a product-led growth mindset, serving businesses of all sizes by helping them leverage social media for their advantage.

In under 500 words, he shares: 

  • How he got into product management…

  • How he starts his mornings for maximum productivity…

  • Some of his sources of learning…

And more…  

Enjoy!

Never miss a No BS, Just Product Micro-Interview: SUBSCRIBE

“How did you get into product management?”

My path to product management wasn't planned; it just happened. Starting off as a designer when the internet was still young, I worked on early products that now millions rely on every day. The role of 'product manager' wasn't even on most people's radars back then.

As I watched the tech industry grow, I grew with it, naturally facing the kinds of challenges that lacked textbook solutions but were crucial to tackle. My career was shaped by real-life lessons on user needs and the goals of the businesses I worked with.

Building and growing products, and businesses respectively, has become a part of who I am.

“How do you start your mornings at work?”

1.      My calendar is the blueprint for my day, setting my priorities and attention.

2.      I analyze data and trends in our product-led growth journey and business performance to ensure we're headed in the right direction and maintaining the right speed.

“What do you know about product management now that you wish you’d known when you first started?”

Real impact is measured by achieved and validated outcomes, not just by planned strategies.

The business has targets to hit, and so do our customers. Our role in product management is to bridge that gap, making sure both sides reach their goals with as little friction as possible.

“What did your biggest product failure teach you?”

Set hard limits and recognize when it's time to move on.

Holding onto a product that isn't working just because you're attached to it is a mistake. We learned this the hard way by spending over six months clinging to a product that was holding us back, causing stress, and draining our team's time and energy.

“What’s the #1 thing that has helped you shorten your product management learning curve?”

At its core, product management is connecting businesses that provide value with customers who see that value and are willing to pay for it. This exchange keeps the cycle going, benefiting both the business and its customers.

To apply this principle, I’ve adapted a straightforward four-question framework:

1.      Who are we serving?

2.      Why are we serving them?

3.      What value does our product or service provide to our customers?

4.      How does providing this value benefit the business?

This framework keeps the focus clear and ensures that our product strategy aligns with both customer needs and business goals.

“How do you stay updated on the best practices in product management?”

From what has worked for me — limit yourself to just three sources to learn from. With so many voices in the landscape, focusing on a select few can truly enhance your product skills and broaden your perspective.

Here's where I get my insights:

·         Reforge, for in-depth strategies.

·         Lenny's Podcast, for the latest in tech and business.

·         Selected thought leaders on LinkedIn for diverse perspectives – some cherry-picks: Kyle Poyar, Elena Verna, Paweł Huryn

I aim to gain a more in-depth understanding and focus on applying new lessons daily until they become an integral part of my toolkit. I've found that it's this kind of concentrated effort that significantly enhances my expertise over time.

]]><![CDATA[Micro-Interview: Sonya Vrabcheva, Director of Product Management | Quantive]]>https://www.dmihaylov.com/p/micro-interview-sonya-vrabcheva-directorhttps://www.dmihaylov.com/p/micro-interview-sonya-vrabcheva-directorTue, 26 Mar 2024 07:59:55 GMTSonya Vrabcheva is a Director of Product Management at Quantive - a leading strategy execution software and services company inspired by the OKRs methodology.

In under 500 words, she shares: 

  • How she got into product management…

  • How she starts her mornings for maximum productivity…

  • Some of her sources of learning…

And more…  

Enjoy!

Never miss a No BS, Just Product Micro-Interview: SUBSCRIBE

“How did you get into product management?”

Starting my professional journey as a QA specialist in a product-focused company, I noticed a common thread between the QA and product manager roles. Both require a shared curiosity and a relentless pursuit of understanding the "why" behind each need.

So, moving into product management felt like a natural progression for me. Looking back, I'm glad I took the chance to do something I love, and that helped me grow.

“How do you start your mornings at work?”

My mornings begin with a thorough review of new emails, Slack messages, reminders, and a calendar check for the day and the week ahead. One of my daily rituals involves crafting a to-do list, a practice I find extremely beneficial in setting the right tone for the workday. It's interesting that I prefer the tactile experience of writing the to-do list on paper, as handwriting improves my memory and helps me to stay focused on priorities.

“What do you know about product management now that you wish you’d known when you first started?”

Coming from my background, initially, it felt instinctive to collaborate closely with design and engineering teams on my PM journey.

However, what I didn't immediately grasp was the importance of connecting with other vital teams, including Success, Marketing, and Sales.

Regardless of a company's size, regular communication with these teams is crucial for gaining deeper insights into customers, prospects, and the broader market landscape.

“What did your biggest product failure teach you?”

The most significant lesson from my major product failure is the recognition that even if you flawlessly execute the product development, skipping essential homework and idea validation can quickly turn success into a substantial failure.  

Every phase of the product management journey holds inherent value, emphasizing the importance of navigating each step – from initial discovery to success measurement – for a truly impactful end result.

“What’s the #1 thing that has helped you shorten your product management learning curve?”

The key to speeding up my product management learning was diving in without fearing failure. I firmly believe that books, articles, and lessons provide foundational knowledge, but genuine learning comes from hands-on experiences. 

“How do you stay updated on the best practices in product management?”

I stay updated on the latest product management best practices by following product experts on LinkedIn and participating in Product Management communities. This enriches my social media feed with diverse product discussions, allowing me to delve into interesting cases and scenarios. Additionally, I’m trying to make time for webinars and podcasts such as the Product Podcast, Lenny’s Podcast, and the Product Hunt Radio. 

However, what helps me the most is the privilege of being part of an exceptional team, which adds a lot to my never-ending learning journey. 

]]><![CDATA[Micro-Interview: Hayato Kuroshima, Product Manager | freee 株式会社]]>https://www.dmihaylov.com/p/micro-interview-hayato-kuroshimahttps://www.dmihaylov.com/p/micro-interview-hayato-kuroshimaThu, 21 Mar 2024 06:29:59 GMTEDITOR’S NOTE:

Hayato Kuroshima is a Product Manager at freee., a market leader among cloud-based accounting and HR software providers in Japan.

Hayato-san is based in Japan but travels to Bulgaria frequently with his Bulgarian wife.

In under 500 words, he shares: 

  • How he got into product management…

  • How he starts his mornings for maximum productivity…

  • Some of his sources of learning…

And more…  

Enjoy!

Never miss a No BS, Just Product Micro-Interview: SUBSCRIBE

“How did you get into product management?”

My initial career involved working as an IT consultant at Hitachi, where I helped to deliver IT systems for clients. During these projects, I encountered the Agile Methodology and discovered the joy of collaborating with a team to create products and solve problems. I realized that I didn't want to deliver a product for a client and move on; I wanted to provide value to users through our products continuously. This led me to transition to my current company.

With no prior PM experience, I initially joined a Business Development role and transitioned to PM within about six months. I was anxious about changing jobs and needed to be able to start directly from the product management position I was aiming for. However, it turned out to be a good decision. I feel truly fortunate to have been able to do now what I strived to do from the beginning.

“How do you start your mornings at work?”

I begin my mornings by checking emails and Slack messages. Since mornings are when I'm most focused, I avoid meetings during this time and instead focus on tasks that require concentration, such as document creation and strategic thinking.

“What do you know about product management now that you wish you’d known when you first started?”

One thing I've learned, which was shared with me by colleagues, is that a PM is akin to a movie director.

Similarly, the job involves coordinating various talents, including engineers, designers, and business stakeholders, to deliver user value.

Additionally, I've realized that a PM's work is even more enjoyable than I initially anticipated!

“What did your biggest product failure teach you?”

My biggest lesson from a major project failure was that sometimes, in product management, you must take the initiative rather than rely solely on others.

I learned not to hesitate to push boundaries while still respecting others' feelings.

“What’s the #1 thing that has helped you shorten your product management learning curve?”

Reflecting on past experiences and translating them into actionable insights has been crucial. By conducting retrospectives with the team, we can collectively learn from failures and agree on actionable steps for the future.

A mindset focused on achieving results as a team rather than solely as an individual PM is essential.

“How do you stay updated on the best practices in product management?”

I stay updated by listening to podcasts regularly, with Lenny's Podcast being my favorite. I deepen my knowledge on topics of interest by reading the books recommended by the podcast guests, which I find intriguing, or by watching some of their previous appearances in other media.

Regarding reading material, I follow John Cutler's newsletters and explore topics on Mind the Product that interest me. While catching up with various resources is useful, the most significant learning comes from consulting experienced PMs when facing challenges and resolving real-world issues.

Famous books on product management like "INSPIRED" and "Escaping the Build Trap" have been translated into Japanese, but other well-known books haven't been translated yet. I want to strive for the opportunity to translate them someday.

]]><![CDATA[Micro-Interview: Lior Haimovich, Director of Product Management | Yotpo]]>https://www.dmihaylov.com/p/micro-interview-lior-haimovich-directorhttps://www.dmihaylov.com/p/micro-interview-lior-haimovich-directorFri, 23 Feb 2024 06:23:13 GMTEDITOR’S NOTE:

Lior Haimovich is a Director of Product Management at Yotpo, an eCommerce retention marketing platform with connected solutions that help direct-to-consumer brands maximize their customers' value.

He is based in Sofia, Bulgaria.

In under 500 words, he shares: 

  • How he got into product management…

  • How he starts his mornings for maximum productivity…

  • Some of his sources of learning…

And more…  

Enjoy!

Never miss a No BS, Just Product Micro-Interview: SUBSCRIBE

“How did you get into product management?”

I started as a full-stack developer at Microsoft. I did that for two years, realizing I had more passion for working with people and having a more business-oriented, customer-facing role. Getting the first product role wasn't easy, but I was lucky to meet an old friend who referred me to Yotpo, which allowed me to prove myself.

“How do you start your mornings at work?”

How I start my mornings is very much related to how I finish every day. At the end of each day, I make myself a very short list of highly important tasks or things I want to achieve in the coming days. Every morning, I look at this list and choose the one thing I want done today.

“What do you know about product management now that you wish you’d known when you first started?”

Get people involved as early as possible. In the beginning, I would spend a lot of time on my own trying to crack a solution to a problem before showing it to others. I thought I’d be appreciated more if I were “the guy with the solution”. But in fact, in most cases, the best ideas come from others. Involving other stakeholders early on helps with alignment and team engagement and increases your chances of finding a better solution. It’s also more fun cracking problems as a team :)

“What did your biggest product failure teach you?”

Validate even the smallest changes. A few years back, we were working on a feature, and I decided to make a small tweak to an existing behavior. I didn’t consult with our go-to-market team or any customer. It backfired. We had to revert the change within two days after getting tons of complaints.

The bottom line is that even the smallest change can make a big difference, so having a small focus group where you can run your ideas is key.

“What’s the #1 thing that has helped you shorten your product management learning curve?”

Product management requires a lot of different skills and expertise. What really helped me was the fact that I got to lead various projects, each sharpening a different skill in a short time. From tackling a technical integration to strengthening my relationship with the dev team. Through implementing a customized solution for some of our biggest customers, learning to do customer interviews, and identifying pains. All the way to a new shopper-facing, innovative feature that required usability testing and some marketing spark.

“How do you stay updated on the best practices in product management?”

Two things work for me the most:

  1. Learning from my teammates. Our team is diverse, with many PMs from different backgrounds and experiences. Working on different projects with different individuals gives me a fresh perspective on processes and new problem-solving methods.

  2. Looking at every app or website with a critical product eye. Looking at the most successful apps out there and, while using them, thinking - what makes them fun to use? Then, when I come to build my next feature, I’ll think about how to incorporate some of these concepts in my work.

]]><![CDATA[Micro-Interview: Antoaneta Tsaneva, VP Product Design | Quantive]]>https://www.dmihaylov.com/p/micro-interview-antoaneta-tsanevahttps://www.dmihaylov.com/p/micro-interview-antoaneta-tsanevaThu, 15 Feb 2024 07:36:04 GMTEDITOR’S NOTE:

Antoaneta Tsaneva is a VP of Product Design at Quantive, a Strategy Execution Platform based on the OKR methodology.

In under 500 words, she shares: 

  • What design brings into product management…

  • How she starts her mornings for maximum productivity…

  • Some of her sources of learning…

And more…  

Enjoy!

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“What does design bring into product management?”

My experience as a design leader allowed me to bring the user perspective to the table, influencing the build process and the product. I soon realized that product design and management are closely interrelated, and transitioning between these roles was natural.

“How do you start your mornings at work?”

With a large cup of coffee! I start my workday early to review my priorities and schedule without interruptions. I use Slack to catch up on messages, channels, and questions from the US team.

“What do you know about product management now that you wish you’d known when you first started?”

As in design, knowing when to stop is critical in product development. Early on, I often found myself blocked by a lack of courage to decide and hesitated to move forward. Accepting that creating a product is an ongoing cycle of experimentation and iteration was liberating.

“What did your biggest product failure teach you?”

Experiment, learn, and take risks. Both failures and successes are natural parts of the process. While failing is easy, learning from successes is more valuable to gain insights. This approach helps me move forward more confidently, knowing what's working.

“What’s the #1 thing that has helped you create great products?”

During my years in Telerik, I've been lucky to have the opportunity to learn directly from Vassil Terziev and the rest of the Telerik founders. They were all obsessed with quality and super focused on the customer, so our approach to building the right product and delivering value was to stay close to our users and integrate their feedback early on.

“How do you stay updated on the best practices in product management?”

I stay updated by reading top authors and colleagues on Medium, following reputable sources like Nielsen Norman Group, FastCompany, TechCrunch, SVPG, and experts like Jeff Gothelf, Peter Merholz, Scott Belsky, and others. I follow other companies and keep an eye on the software products they are building to stay on track with emerging trends.

]]><![CDATA[Micro-Interview: Hristo Zahariev, Sr. Product Manager | Cubby]]>https://www.dmihaylov.com/p/micro-interview-hristo-zahariev-srhttps://www.dmihaylov.com/p/micro-interview-hristo-zahariev-srWed, 07 Feb 2024 06:46:54 GMTHristo Zahariev is a Senior Product Manager at Cubby, a self-storage platform revolutionizing how owners/operators manage and market their portfolio in a $43B vertical market in the USA.

In under 500 words, he shares: 

  • How he got into product management…

  • How he starts his morning for maximum productivity…

  • Some of his sources of learning…

And more…  

Enjoy!

Never miss a No BS, Just Product Micro-Interview: SUBSCRIBE

“How did you get into product management?”

I’d say it was a mixture of an ounce of luck and my openness to new challenges and learning new things. Ten years ago, I was headhunted for a product analyst position at Experian, which was the company's first product role opening in Bulgaria. I only had a 40% match on my CV when looking at the job description, but I managed to compensate for it with my ambition to learn and my previous experience in business analysis and project management.

“How do you start your mornings at work?”

Thirty minutes with a cup of coffee, a calendar, and a paper tablet. I start by validating the one thing I will finish on the day, no matter what happens. I would allocate a slot, usually 2-3 hours, for deep work on that item, with no phones, emails, or meetings during that slot. I would review my product dashboard and plan for any second-priority items per the remaining capacity.

“What do you know about product management now that you wish you’d known when you first started?”

The number one job to be done by a product manager is to establish a sense of common context across the teams in the company on who we are building the product for, who is using it, and why they are willing to pay. How you achieve this common context doesn’t really matter in terms of choice of tools, product activities, frameworks, etc, as long as you get that outcome.

“What did your biggest product failure teach you?”

Don’t force fancy and complex features that don’t bring significant customer value into roadmaps just because some of the HIPPOs say so. Use assertive communication and data to push back.

“What’s the #1 thing that has helped you shorten your product management learning curve?”

Switching my mindset from operational output-driven to outcome-driven, I.e., starting to use more customer user journeys and personas and speaking to customers more.

“How do you stay updated on the best practices in product management?”

Some books like “Build What Matters” are good. Also, following key product people and groups on LinkedIn who share lots of stories and ideas with the rest of the community helps you stay updated by spending 5-20 minutes daily. Pawel Huryn, The Product Folks, and David Pereira are good places to start. You can also start following Steve Nouri and Ruben Hassid on practical tips and tricks regarding using AI like GPT in your day-to-day professional life for data analysis, market research, product documentation, etc.

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