Did we miss a partner program update from any of the major players which you feel is important?

In March 2020, The Economist ran a piece called "The Rise and Rise of Accenture." Total shareholder returns at that point, including dividends, had come to 118% over the prior five years – more than double the S&P 500. By the end of 2021, the company's market cap peaked at nearly $273bn. The bet Accenture had made a decade earlier - to become the first call for enterprises trying to bring technology into their businesses - was paying off handsomely.

Having already reinvented itself several times over, it seemed reasonable to assume it would be one of the biggest beneficiaries of AI. The company had the credibility, the relationships, and the institutional trust to act as a bridge between frontier models and the companies those models could transform. It had decades of experience doing exactly that kind of work, profitably and at scale.

Yet here we are. Since January this year, Accenture shares are down 52%. The damage is not isolated: Cognizant, Capgemini, Tata Consultancy Services, and Infosys are each down by a third or more. Something is wrong across the category.

What makes this particularly striking is the gap between what is happening in public markets and what is happening in private ones. Sierra most recently raised at roughly 79x ARR. PhysicsX raised at around 48x ARR. Faculty AI, which Accenture itself acquired earlier this year for approximately $1bn, or about 17.7x revenue, commanded a premium that Accenture's own stock cannot get anywhere near. The company currently trades at 8.5x forward PE and roughly 2x revenue. The market is, in other words, willing to pay a dramatic premium for the AI-native version of what Accenture does, while marking down the incumbent version sharply.

There are four theories circulating. They are not mutually exclusive.

1. It ran out of room

The first explanation has nothing to do with AI. Accenture may have simply grown too large for its own good. At its scale, the company has saturated much of the addressable enterprise market, which is why acquisitions have become such a consistent part of the growth story. The Faculty AI deal is part of a broader pattern: buying top-line growth because organic growth is harder to find. You can only sell so many transformation programmes to the Fortune 500. Inorganic growth was a big part of the story for the past ten years, adding ~2-3% annual growth every year. We covered Accenture’s M&A strategy in detail when we broke down its acquisition of Faculty AI. Just last week, our market monitor picked up the acquisition of 650 FTE strong Industries eXcellence Group (IndX) - a Siemens industrial software specialist.

2. AI is eating its revenue directly

The second theory is more structurally alarming. A meaningful share of Accenture's revenue comes from the kind of manual, labour-intensive work that AI is beginning to automate at scale: data migration, systems integration, testing, process documentation, offshore delivery. If AI compresses the time and headcount required for that work, it does not just change the margin profile. It removes the billable hours altogether. Clients who used to need 200 consultants for a two-year programme may soon need 40 for six months.

3. The labs are going direct

The third theory is that Accenture is being cut out. In search of revenue and enterprise proof points ahead of their own IPOs, the frontier AI labs have been going after large clients themselves rather than routing work through intermediaries. When OpenAI announced the launch of DeployCo last month - alongside its acquisition of the consulting firm Tomoro - Accenture's market value fell below $100bn for the first time in six years. The market read that move as a direct competitive threat, not a partnership opportunity.

4. Enterprise AI ROI is not materialising

The fourth theory is that corporate AI spending is slowing because the returns are not coming through. A growing body of research - from MIT, McKinsey, Bain, and others - suggests that many enterprise AI deployments are not yet delivering the productivity gains that justified the initial investment. If clients are becoming more cautious about what they commit to, that slowdown hits the firms selling AI transformation programmes first and hardest.

Accenture's management has been notably careful in how they frame all of this. On recent earnings calls, the company has positioned AI as a long-term tailwind rather than a current revenue accelerator: a sensible way to avoid over-promising, though it does little to reassure investors who are watching the stock in real time. The unrest in the Middle East, and its ripple effects elsewhere in the corporate world, led to delays in closing deals, as companies postponed their transformations amidst the uncertainty. In the last earnings call, this was cited as the main reason for lowering guidance for the rest of the year.

The data points they have chosen to highlight are, by design, directional rather than absolute. Revenue from their top-10 AI and data ecosystem partners is outpacing overall growth. Bookings from newer AI partners – Anthropic, OpenAI, Databricks, Nvidia, Palantir, Snowflake, Mistral – are on track to more than double versus FY25. However, no absolute figures have been provided, which makes the trajectory hard to independently verify. When we researched partner programs earlier this month, almost every AI vendor proudly listed Accenture as a partner.

If you are an AI services business, Accenture's situation is worth studying carefully, not because it predicts your fate, but because it tells you something about the wider market.