Every major platform — Xero, Avalara, Thomson Reuters, QuickBooks, Sovos, Sage, Vertex — has shipped an AI product this year. Most look identical in features. The real differences are in the stack underneath.

This week, I am taking a quick look at what’s under the hood of the leading TaxTech products.

🔀 Multi-model is the default. A single model may be risky.

Thomson Reuters runs Claude, GPT-4, and Gemini simultaneously in CoCounsel Tax. Xero uses Anthropic for financial reasoning and OpenAI for web research. Avalara combines six providers with proprietary SLMs in its ALFA framework.

These multi-model architectures came to be on purpose, I think. When a frontier model gets a jurisdictional edge case wrong — and they all do — you want a fallback that isn’t the same model with a different temperature. Or when you run out of compute (tokens) during your period close, you want a fallback option. I wrote a full article about the contingencies you’d need in the future.

A reliance on a single model will most likely ruin your weekend.

🏗️ Intuit went the other direction — and it worked.

While everyone else built on top of frontier models, Intuit built underneath them. Proprietary Financial LLMs (GenOS), fine-tuned on financial data at scale: 90% accuracy on transaction categorization, 50% lower latency than off-the-shelf alternatives.

The case for owning your model is real — but only when the data flywheel justifies the build. If a vendor tells you they built their own AI, ask what data it was trained on. “We built our own model” without a data advantage is a marketing decision, not a technology one. And it won’t last.

As I covered in Your Tax AI Vendor Cannot Fix This — the constraint has never been the model. It’s the data layer underneath it.

VentureBeat, “How Intuit built custom financial LLMs that cut latency 50% while boosting accuracy”

🔌 MCP support is the integration signal nobody’s reading.

Avalara and Sovos have both shipped Model Context Protocol (MCP) servers. That’s how any AI agent can query a vendor’s compliance APIs using a standard protocol. No custom middleware.

This is the difference between a vendor that’s an island and one that’s a node in your system. If your TaxTech vendor hasn’t shipped an MCP server, ask when it’s on the roadmap. If it’s not on the roadmap, you’re writing bespoke connectors — and maintaining them every time either side changes an API.

Avalara, “Connecting AI to Tax Automation: How MCP Servers Bridge the Gap,” November 2025

🤔 Every vendor calls it “agentic.” Ask what the agent actually executes.

Avalara’s Avi can ingest transaction data, calculate tax, validate exemptions, generate a return, and file it — autonomously, with human approval checkpoints. That’s an agent completing a workflow end-to-end.

Sage’s Finance Intelligence Agent routes your question to the right data source and composes an answer. That’s an agent retrieving and summarizing.

Both are called the same thing. The distinction matters enormously for how you architect around them. Before signing any contract with “AI agents” in scope, ask for a demo showing the agent completing a specific workflow from trigger to output. Not a slide. A working execution.

📋 The contract your organization signs this quarter is an architecture decision.

When a Tax Director signs a three-year deal with a single-LLM vendor and no MCP server, they’ve made a product architecture decision for their PM and engineering teams. They’ve locked in the integration surface, the agent compatibility layer, and the model fallback strategy — without realizing they’ve made that choice.

In Issue 004, I mapped every major AI partnership in TaxTech YTD. The pattern is consistent: AI capability is being acquired at the marketing layer, not the architecture layer. PMs who understand the stack before the contract is signed will have significantly more optionality in 18 months. Those who don’t will be managing a migration.

The question I keep coming back to: why aren’t product teams in the room during TaxTech contract negotiations? Reply and let me know if that’s changing at your organization.

References

1. Xero — “Xero and Anthropic Collaborate” — https://www.xero.com/us/media-releases/xero-and-anthropic-collaborate/ — June 2026

2. Avalara — “Avalara Launches Agentic Tax and Compliance™” — https://newsroom.avalara.com/2025-09-30-Avalara-Launches-Agentic-Tax-and-Compliance-TM-AI-Agents-That-Work-for-You — September 2025

3. Avalara — “MCP Servers Bridge the Gap” — https://www.avalara.com/blog/en/north-america/2025/11/model-context-protocol-servers-bridge-the-gap.html — November 2025

4. Sovos — “Sovos Expands Sovi AI” — https://sovos.com/press-releases/sovos-expands-sovi-ai/ — March 2026

5. Thomson Reuters — “One Million Professionals Turn to CoCounsel” — https://www.thomsonreuters.com/en/press-releases/2026/february/one-million-professionals-turn-to-cocounsel-as-thomson-reuters-scales-ai-for-regulated-industries — February 2026

6. VentureBeat — “How Intuit Built Custom Financial LLMs” — https://venturebeat.com/ai/how-intuit-built-custom-financial-llms-that-cut-latency-50-while-boosting — 2025

7. Wolters Kluwer — “CCH Axcess Expert AI Launch” — https://www.wolterskluwer.com/en/news/wolters-kluwer-launches-cch-axcess-expert-ai — November 2025

8. Vertex Inc — “Vertex Advances AI-Powered Capabilities” — https://www.globenewswire.com/news-release/2026/04/07/3269067/0/en/Vertex-Advances-AI-Powered-Capabilities-to-Improve-How-Enterprises-Execute-Compliance.html — April 2026

9. Sage — “Sage Debuts Finance Intelligence AI Agent” — https://www.channelinsider.com/ai/llms-chatbots-and-agents/sage-finance-intelligence-agent/ — 2026

10. Intuit — “GenOS Press Release” — https://investors.intuit.com/news-events/press-releases/detail/1272/intuit-rapidly-advances-genos-to-accelerate-development-of-agentic-ai-experiences-at-scale — 2025

Every major player in the TaxTech space has an AI strategy. Most have AI platforms. Some have actual results. The competition is now focused on platform procurement (external or internal), adoption by business customers, and results. And it’s getting hot out there.

What the Models Can (and Can’t) Do

The AIMultiple Finance LLM Benchmark — 238 hard financial reasoning questions, updated June 2026 — has the clearest current ranking.

Claude Opus 4.8 leads at 89.08%, dethroning GPT-5 (88.23%) while using 7× fewer output tokens. Notably, the top six models are within 2.5 points of each other. Looks like the model choice barely moves the needle at this level. DeepSeek R1 (open-source) trails at 62.18% but is still relevant for European deployments where transaction data can’t leave the infrastructure. It also needs further training to be on par with the rest of the pack.

Clearly, the constraint is not the model.

The Providers Building the Infrastructure

Before evaluating any AI vendor, it helps to know who’s funding the underlying models — and how quickly they’re growing.

Anthropic grew from $1B to $30B ARR in 18 months. OpenAI hit $25B ARR — over 40% from enterprise. Mistral crossed $400M ARR through European enterprise deals, 20× in a year.

The interesting dynamic: Anthropic and OpenAI have competing claims on the same enterprise clients. Anthropic signed Deloitte and PwC. OpenAI signed Bain and H&R Block. Both signed Intuit, separately.

Big Four: $9 Billion and Counting

The combined AI investment across the Big Four is $9B+. Here’s where it went.

The platforms are real. Client-facing outcome data — tax cycle times, audit error rates, jurisdiction efficiency — is still emerging. Whoever publishes that data first and proves fidelity will hold the relationship for the next decade.

Everyone Else Who Actually Shipped

The more interesting story is happening outside the Big Four.

Ramp’s Stack — a full AI OS for accounting firms, every agent action audit-logged to source data — launched June 3. Anrok raised $55M Series C in October 2025 and now handles VAT, GST, and sales tax for Anthropic, Notion, and Cursor across 100+ countries. Wolters Kluwer shipped Expert AI across CCH Axcess — their unified tax, audit, and firm management platform — in November 2025. Filed built an AI Tax Preparer that processes 3× more returns without adding headcount. A European institution applied LLMs to supplier invoices and achieved a 10% cost reduction on a multibillion-euro spend basis.

79% of tax leaders say AI will be transformational. 37% have invested. Early adopters are getting mixed results. Some will figure it out — and when they do, they’ll have 18 months of hard architecture decisions baked in before the first case study is written. By the time best practice gets published, the advantage is already in the past.

As I argued in Your Tax AI Vendor Cannot Fix This: AI on top of bad data fails quietly. The firms winning right now fixed the data layer first.

The Indirect Tax Infrastructure Layer

The enterprise indirect tax market — the layer between your ERP and global VAT/GST compliance — is having one of its most active M&A years on record.

Fonoa is the headline story. $110M Series C in May 2026, and they simultaneously acquired PwC’s Indirect Tax Edge platform — the same platform PwC was building to compete with them. They now own both sides of that argument. Current clients include Uber, Netflix, Canva, and Booking.com.

Avalara Launched Agentic Tax & Compliance in September 2025. The ALFA framework enables agents to act autonomously across SAP, Oracle, NetSuite, and SYSPRO — and shipped MCP servers for tool-level integration. An IPO is planned for 2026. The company that defined indirect tax compliance for a generation is now positioning itself as an AI-native orchestration layer.

Sovos Launched Sovi AI in March 2026 and acquired IRIS APAC to expand Asia-Pacific reach. Their Label product handles FATCA, CRS, and CARF. The positioning is deliberate: compliance intelligence as a distinct product category from compliance execution.

Thomson Reuters launched ONESOURCE+ in November 2025 — an AI-powered Intelligent Compliance Network that connects enterprise indirect tax, trade, and risk infrastructure — and then followed with ONESOURCE Sales and Use Tax AI in January 2026. Powered by CoCounsel, it cuts routine reporting time by 65% and audit exposure by 75%. The CoCounsel Tax product already has 1 million users across 107 countries. ONESOURCE is where the indirect tax workflow actually runs — and now it has an AI layer on top.

Vertex acquired AI tax categorization from Ryan in December 2025, added Kintsugi AI the same month, and shipped 65 product enhancements throughout 2025. Vertex Copilot is already embedded in Oracle, SAP, and Salesforce — meaning the AI sits where transactions actually happen.

Fintua — formerly Taxback International — rebranded in July 2025, collapsing VAT recovery, e-invoicing, and payments into a single platform. The rebrand signals compliance as a commodity and automation as the margin.

Every significant player in indirect tax infrastructure either built an AI product, acquired one, or bought a competitor’s platform this year.

Two weeks ago, I posted on LinkedIn: “Can someone please kill the internal AI leaderboards? Or at least put the right KPIs in place?”

Last week, Amazon did:

From the article:
Dave Treadwell, Senior VP, told staff to stop using AI “just for the sake of using AI” after employees “tokenmaxxed” their scores on Kiro Rank — running meaningless tasks through the system to inflate usage numbers. Amazon replaced it with “normalized deployments”: AI output that is actually shipped.

The coverage called it a leaderboard problem. To me, it really looks like the appearance of efficiency from AI use is more important than the return on AI spending, measured, for example, in ARR.

It is a measurement pattern — one that has repeated three times in 150 years.

The gaming is only half the problem

According to Goodhart’s Law, when a metric becomes a target, it ceases to be a good metric. In other words, once a specific metric is used to reward performance, people will manipulate the system to optimize that metric, inadvertently undermining its original purpose. Amazon set an 80%+ weekly AI usage target. Developers met it by running AI on tasks that didn’t need it (no wonder 🤷‍♂️).

Victorino Group has documented the same failure at three enterprises in 2026 — different tools, identical result.

But the deliberate gaming is only half the problem. METR’s May 2026 study found that workers with no leaderboard to game still overestimated AI’s effect on their own productivity by 40 percentage points on average.

The moat always appears when measuring

Paul David’s 1990 research on electrification documented a 40-year productivity lag: factories installed electric motors in 1895 and saw no gains until the 1920s. The ones that captured the gains didn’t measure kilowatts installed. They reorganized their floors around electricity’s properties — unit drive over group drive — and measured output per reorganized unit.

Cloud/SaaS compressed the same pattern to 15 years: the companies that invented LTV/CAC and NRR in 2000–2015 built durable moats while the rest measured seat counts.

AI is in year three. As I explored in The Future Cost of TaxTech, the compliance cost stack has already changed. ‘Tis the time to establish the right metrics. Most enterprises haven’t noticed.

Those measuring outcomes reap enormous benefits from AI

PwC’s 2026 AI Performance Study (n=1,217 executives, 25 sectors) found that leaders generate 7.2× more revenue and efficiency gains than the average competitor. What separates them: twice as likely to redesign workflows around AI rather than layering tools on top, 2.8× more likely to automate decisions without human intervention. The single strongest predictor of AI financial performance is identifying growth opportunities — not cost reduction, not usage frequency. They are measuring outcomes, not activity.

Tax and Finance have two value categories that are structurally invisible

BCG’s first systematic AI ROI study in finance found that only 29% of finance executives are able to confidently measure AI ROI. The root cause is that standard measurement captures only labor (effort) savings. It misses other important metrics, such as audit risk reduction (exposure shifted before the filing) and compliance failures avoided (the penalty never incurred). Both have real expected value. Neither shows up on any dashboard.

The leaderboard was never the problem. The metric was.

What does your AI ROI calculation include?

REFERENCES & FURTHER READING

1. Financial Times — “Amazon scraps AI leaderboard to stop workers chasing usage scores” — ft.com — May 2026

2. The Decoder — “Amazon kills internal AI leaderboard after employees gamed it with pointless tasks” — https://the-decoder.com/amazon-kills-internal-ai-leaderboard-after-employees-gamed-it-with-pointless-tasks/ — Accessed June 2026

3. Victorino Group — “Three Companies, One Failure Mode: Goodhart’s Law Comes for AI Adoption” — https://victorinollc.com/thinking/goodhart-ai-adoption-tokenmaxxing-mainstream — Accessed June 2026

4. METR — “Measuring the Self-Reported Impact of Early-2026 AI on Technical Worker Productivity” — https://metr.org/blog/2026-05-11-ai-usage-survey/ — May 2026

5. PwC — “Three-quarters of AI’s economic gains are being captured by just 20% of companies” — https://www.pwc.com/gx/en/news-room/press-releases/2026/pwc-2026-ai-performance-study.html — April 2026

6. PwC — “Want ROI from AI? Go for growth” (full study PDF) — https://www.pwc.com/gx/en/so-you-can/2026/content/roi-from-ai.pdf — 2026

7. BCG — “How Finance Leaders Can Get ROI from AI” — https://www.bcg.com/publications/2025/how-finance-leaders-can-get-roi-from-ai — 2025

8. Paul David — “The Dynamo and the Computer: An Historical Perspective on the Modern Productivity Paradox” — 1990. Accessible via AEI updated commentary: https://www.aei.org/articles/the-dynamo-the-computer-and-chatgpt-explaining-todays-productivity-paradox/

9. CNBC — “Almost every Fortune 500 is tracking overall AI usage: What that means for employees” — https://www.cnbc.com/2026/05/05/ai-use-work-employee-monitoring-tech-surveillance.html — May 2026

🎯 Start with the outcome. Not the data.

90% of AI projects never reach fruition. Not because of bad data, but because of the wrong starting point.

According to the research, most TaxTech projects begin with a data inventory - invoices, spreadsheets, PDFs, and returns.

RoAI Institute, “Crossing the AI Chasm: Start with Outcomes, Not Data” · HBR, “Most AI Initiatives Fail. This 5-Part Framework Can Help,” November 2025

🪤 Pilot purgatory. Where most AI initiatives die.

HBR calls it “pilot purgatory”: an AI initiative succeeds in proof of concept, then stalls before it scales. They mapped 7 frictions behind it — pilot proliferation, process debt, accountability gaps, tribal knowledge loss, governance failures, integration complexity, and the efficiency trap.

Look at that list from a tax function’s perspective, and you’ll recognize every single one.

HBR, “The Last Mile Problem Slowing AI Transformation,” March 2026

💰 By 2029, strategic CFOs will add 10 margin pp.

Tax is a margin lever. Every recovered VAT euro, every avoided penalty is direct P&L. CFOs designing TaxTech against financial outcomes now are building that advantage — compliance first, features second. Everyone else is watching demos.

Gartner, “By 2029, CFOs Who Implement Strategic AI Deployment Will Add 10 Margin Points of Growth,” April 28, 2026

📉 74% want AI to grow revenue. 20% are doing it.

Deloitte, 3,200+ leaders: Only 20% of the interviewed actually achive revenue growth when using AI. That’s not a technology gap — I would aruge that it’s a design choice made from the beginning. Revenue impact requires defining the outcome before you build and holding someone accountable for the result. Most organizations skip both.

Deloitte, “State of AI in the Enterprise 2026,” surveyed 3,200+ leaders, Aug–Sep 2025

🔍 72% of CIOs are losing money on AI. Here’s what separates the 28%.

Gartner, 782 I&O leaders: 72% of CIOs are breaking even or losing money on AI. The 28% who succeed embed AI into existing workflows and invest 4× as much in data foundations before any model is deployed.

For tax: the hard decision isn’t which tool to buy — it’s whether to invest in the integration layer before the business case is approved. You can’t model your way out of a data architecture problem.

Gartner, “AI Projects in I&O Stall Ahead of Meaningful ROI Returns,” April 7, 2026 · Gartner, “Organizations with Successful AI Initiatives Invest Up to Four Times More in Data and Analytics Foundations,” April 16, 2026

Missed Issue #001? Read more about the data readiness gap in enterprise AI.

📊 Everyone’s experimenting. Almost no one is scaling.

Three years into the gen AI era, 88% of enterprises use AI in at least one function, and 61% use it in Tax/Finance. Only 33% are scaling it, and only 39% see any impact on EBIT.

In tax and finance, it’s worse. Use cases are still stuck in document processing and chatbots. Not reconciliation automation. Not real-time VAT determination. Chatbots at most.

Looks like Tax folks are still in the pilot phase. Worth remembering that a TaxTech roadmap without workflow redesign isn’t a business reinvention for the AI era — it’s making the old way a bit faster. I even wrote an article about this.

McKinsey QuantumBlack, Nov 2025

🏗️ The bottleneck isn’t the AI. It’s the plumbing.

94% of organizations say connected data is critical to AI success. Only 27% actually have it. And only 39% of unstructured data — the kind that dominates tax departments — is ready for AI use.

Tax data lives across ERPs, invoicing platforms, e-invoicing networks, and legacy GL systems. Nothing is connected by design. You can’t drop an AI reconciliation agent on top of that and expect it to work.

The model isn’t the problem. You need to assign a Tax PM to the data architecture problem.

Hyland / HBR Analytic Services, May 2026

🔐 Your AI vendor stack is now an attack surface.

The IMF flagged AI-fueled cyberattacks as a mounting financial stability risk. The FSB named third-party AI concentration as a new systemic vulnerability. Both were published this month.

Tax data is a high-value target — VAT filings, entity structures, transfer pricing docs. Two or three global AI providers now underpin most of financial services’ critical processes. When your AI tax-determination vendor goes down, your ability to file goes with it.

It will be an interesting SLA conversation during the next contract review.

IMF Blog, May 2026 · FSB Report, Oct 2025

🧠 AI can now read its own thoughts. This matters for tax.

Anthropic published Natural Language Autoencoders this week — a method that converts a model’s internal activations into readable text. Claude suspected it was being evaluated on 26% of benchmark tasks, though it never said so.

Your regulator will eventually ask: how do you know what your AI was reasoning about? Today, you can’t answer that. NLAs are the first credible path to an audit trail of AI reasoning — not just outputs.

Watch this space.

Anthropic Research, May 7, 2026

📈 Spend is up. Governance is not.

Seven consecutive years of rising AI investment in financial services. The same top concerns as year one: data quality, third-party oversight, and regulatory lag. The IIF-EY survey just released its 7th annual edition.

For tax specifically, this is a compounding risk. Multi-jurisdictional obligations, unstructured data, and growing vendor dependency — the failure modes are multiplying faster than the controls designed to catch them.

The firms building governance infrastructure now — before the audit — will have a structural advantage. The rest will retrofit under pressure.

IIF-EY Annual Survey on AI in Financial Services, Oct 2025