AI Is Already Undermining Expertise-Driven Businesses: Lessons from Gartner’s Stock Plunge
Melvine Manchau
Senior Strategy & Technology Executive | AI & Digital Transformation Leader | Former Salesforce Director | Driving Growth & Innovation in Financial Services | C-Suite Advisor | Product & Program Leadership
August 16, 2025
A recent cliff dive in Gartner’s stock price – a nearly 28% single-day plunge, its worst drop since 1999 – has jolted the business world. This wipeout capped a loss of about half of Gartner’s market value since the start of 2025. Why? Because Gartner’s sudden nosedive is more than a one-off market reaction – it’s a warning for any company whose business model depends on monetizing human expertise.
The uncomfortable truth is that AI is already eating into such companies’ revenue, often invisibly. Clients and consumers are increasingly self-serving with AI chatbots to get insights instead of paying for traditional expert advice.
In Gartner’s case, investors are waking up to this reality – and you can bet they’re asking which company is next.
Gartner’s “AskGartner” Moment and the AI Threat to Old Models
Gartner is a global leader in research and advisory services, long able to charge premium prices for access to its analysts, reports, and data. Yet even Gartner was not safe from the AI tsunami. The company’s Q2 2025 results actually met Wall Street estimates, but its future outlook spooked investors. Total contract value – a key health metric – grew at only around 4–5% year-over-year, a sharp slowdown . That deceleration, coupled with a slight cut to Gartner’s 2025 revenue guidance, sent a shock through the market, erasing nearly a third of the stock’s value in a week. The root concern? Growth in Gartner’s core research segment is stalling, and “trends in AI and the broader market are severely eating into Gartner’s business model”
Gartner’s leadership is not blind to this shift. They quickly rolled out a new AI-powered tool called “AskGartner,” pitching it as a way for clients to query Gartner’s trove of research using an AI assistant. It’s a smart idea – essentially an attempt to fight fire with fire – but many fear it’s too little, too late. The reality is that upstart, AI-first intelligence platforms and even in-house AI solutions are vying to replace what Gartner sells. Why wait days for an analyst’s report or pay for an expensive seat license when a custom-trained chatbot might answer your question in seconds? Gartner’s own investors are doubting its strategic response: the stock’s **50% collapse in 2025 reflects deep “investor doubts over its AI strategy” and cultural inertia. In other words, the market suspects Gartner’s old paywall-and-analyst model may not keep up in an AI-driven world.
Notably, Gartner’s plight is part of a bigger pattern across the services industry. The IT services sector overall is facing “persistent weakness as clients cut contracts due to efficiencies enabled by AI” For example, Tata Consultancy Services, a major IT consulting firm, recently announced a 3% workforce reduction (~12,000 jobs) as clients scale back traditional projects. The message is loud and clear: when routine work can be automated or knowledge can be accessed via AI, clients rethink why they should pay legacy providers big money. Gartner’s downfall is a cautionary tale for any firm selling expertise – from research companies to consultancies – that you cannot compete in a new world with the same old business model.
How AI Erodes Traditional “Expertise” Business Models
If your business model revolves around selling human expertise – be it through billable hours, proprietary reports, or subscription-based insights – AI is already chipping away at it, whether you realize it or not. The fundamental dynamic is this: AI lowers the barriers to knowledge and automates knowledge-based work. That means customers or internal stakeholders can get answers, analysis, and content from AI tools without engaging an expert every time. As one former PwC partner bluntly put it, clients will start to ask, “Why should I pay consultants big money to give me an answer I can get instantaneously from a tool?” This shift is happening across multiple domains:
Professional Engineering & Design: Engineering firms traditionally bill by the hour for custom designs, simulations, and reports. Now, AI-driven generative design software can produce viable design alternatives in a fraction of the time, and advanced simulation AI can test scenarios faster than any human. An engineer who might have spent days optimizing a component can use AI tools to get 80% of the work done in minutes, then just fine-tune the results. Fewer billable hours are needed for the same output. The efficiency gains are great for clients, but they threaten the old revenue model of engineering services.
Legal Services: Much of a law firm’s routine work – case law research, contract drafting, document review – can be largely automated by AI now. In fact, by mid-2024, nearly 80% of law firms reported adopting some form of AI for tasks like research, drafting, and summarization. Tools like legal chatbots and AI-assisted research platforms can crank out first drafts of briefs or scour thousands of documents for relevant precedents in seconds. This “fundamentally challenges how legal expertise is delivered, billed, and valued”. Clients may balk at paying high hourly rates for junior attorneys to do work that an AI does faster and cheaper. Law firms are realizing they must either move up the value chain (e.g. focus on high-level advisory, strategy, courtroom advocacy) or risk revenue declines as AI handles the grunt work.
Accounting & Audit: Accounting firms, including the Big Four, have embraced AI to automate audits, tax prep, and compliance checking. Structured, data-heavy tasks in audit and tax are on track to be 50% automated within 3–5 years, with some AI solutions already handling 90% of the audit process in trials. That means fewer staff needed to comb through ledgers or verify transactions. A former Big Four partner notes that unless these firms “become far more specialized,” they’ll face trouble. The traditional model of large teams of accountants billing thousands of hours to clients is under siege. AI can flag anomalies, generate financial reports, and even learn over time to improve its accuracy, delivering a huge productivity boost. Companies might start questioning massive audit fees if much of the work is automated – or they’ll expect far more value beyond what a machine can do.
Management Consulting & Advisory: Consultancies thrive on producing strategy decks, market analyses, process improvements, etc. Now, GPT-4 and other generative AI can produce passable strategy memos, industry overviews, and PowerPoint outlines with remarkable speed. They might not capture every nuance, but they often deliver the first 80% of insight, which consultants would traditionally spend weeks on. Already, many consulting firms are integrating AI into their workflow – and so are clients. When a client’s internal team can ask a chatbot to “summarize the top 5 trends in our industry and suggest growth initiatives,” and get a decent answer, it lowers the perceived value of paying outside consultants for a similar initial analysis. In one survey, six out of ten enterprises said they plan to replace some or all of their professional services (like consultants) with AI within the next 3–5 years. The old consulting model of flying in a team of MBAs for weeks might soon be reserved only for the most complex, high-stakes problems. For more generic projects, AI-driven tools are encroaching fast.
Crucially, it’s not that AI completely replaces human experts – but it does handle a large portion of their work. A recent OpenAI study found that 80% of U.S. workers could have at least 10% of their job tasks affected by GPT-type AI, and nearly 20% of workers might see 50% of their tasks impacted. Knowledge workers are already embracing this: 75% of them now use AI tools in daily operations often to draft reports, analyze data, or generate ideas. In practical terms, AI can deliver the bulk (say, the first draft or initial analysis – the “80% output”) of many expertise-based services faster and cheaper than a human. The human expert then adds the remaining 20% of finesse, domain judgment, and personalization. But from a revenue standpoint, if clients only need to pay for that last 20%, that’s a big hit to companies used to charging for the full 100%. As one AI observer quipped, “AI is a tireless but unlicensed intern” – it works cheaply around the clock, but you still need the seasoned professional to oversee it. Companies that monetize expert work by the hour or by deliverable are thus seeing pricing pressure and volume declines.
The question every firm in these sectors must ask is: How much of a revenue hit can we survive if AI erodes 30%, 40%, or more of the demand for traditional human-produced output? For Gartner, the answer was evidently “not that much” – hence the stock’s freefall when growth faltered. Others should heed the warning.
The Hidden Opportunity: Unlocking Internal Expertise with AI
It’s not just external consulting or service revenue at stake. Every organization, even if it doesn’t sell expertise as its product, has a wealth of internal human expertise – and that, too, is being underutilized (or disrupted) by AI, depending on how you react. Think about your own company: you likely have experienced engineers, analysts, salespeople, etc., who have deep knowledge gained over years. Yet too often, their insights “get buried in emails, scattered across reports, or locked away in their heads”. Valuable knowledge sits siloed in disparate teams or in documents nobody reads.
Before AI, tapping into this internal expertise at scale was hard. You might not even know who in the company has the answer you need, let alone how to extract it quickly. As a result, employees waste time reinventing the wheel or making suboptimal decisions because they can’t easily access collective wisdom. This is essentially a knowledge management problem, and AI can be the solution. Modern AI-powered knowledge bases and assistants can ingest your company’s documents, emails, wikis – all the places that hard-earned knowledge resides – and allow anyone to query it in natural language. Imagine an internal chatbot that knows “the last 10 strategy decks, all the client proposals, and Bob in accounting’s brain” – and can surface the right insight on demand. Suddenly, the expertise of your internal experts is amplified and available 24×7 to the whole organization.
This is a massive opportunity. If you don’t sell expertise externally, you might think you’re safe from AI disruption – but you still face the risk of inefficiency if you don’t use AI to leverage your internal smarts. Worse, if your competitors do harness their internal knowledge with AI and you don’t, they’ll make better decisions faster. Research shows companies that effectively connect employees to internal expertise and information operate much faster and smarter than those stuck in silos. In essence, failing to use AI internally is like leaving money (or productivity) on the table.
Here’s a simple litmus test: Do people at your company often say things like, “If only we knew what our experts know” or “We have that info somewhere but I can’t find it”? If yes, then your organization’s knowledge is under-leveraged. AI can change that by turning your internal experts’ know-how into a constantly accessible tool. One example approach is building “expert finder” systems – e.g., using AI to identify who in the company is knowledgeable on a given topic based on their work and communications. Another is deploying chatbots fine-tuned on your company’s data so any employee can ask a question and get an answer that reflects your collective institutional memory. When companies do this, they often find they can make decisions in days instead of weeks, avoid repeating mistakes, and innovate faster. Your smartest resource is likely already on your payroll – AI just helps you unlock it.
Adapting and Thriving: Partnering with AI (Not Competing Against It)
The sky isn’t falling – unless you refuse to adapt. The lesson from Gartner’s scare (and similar stories in other industries) isn’t that “AI will kill all expertise businesses.” It’s that businesses must reimagine how they deliver and monetize expertise. The future belongs to those who learn to partner with AI, combining human wisdom with AI’s speed and scale. Here are some principles to embrace:
Scale your expertise without scaling headcount (or cutting it). Instead of viewing AI as a threat to jobs, view it as a force multiplier for your best people. What if your top engineer or analyst could effectively work on 5 projects at once because an AI handles the repetitive 80% of each task? You could serve more clients or tackle more internal projects without linear hiring. You don’t necessarily need to cut people – you need to give each person a bigger impact. Some forward-thinking professional firms are already doing this, deploying internal AI assistants to support each consultant or lawyer, thereby increasing their throughput. This approach lets you grow the business (take on more work, serve more customers) rather than shrink costs. It’s the classic “work smarter, not harder,” turbocharged by AI.
Deliver 24×7 value that evolves as conditions change. Traditional expert output is often static or one-off – think a PDF report or a point-in-time advice call. In contrast, AI-driven solutions can be always-on and updated continuously. For instance, instead of a consulting report that sits on a shelf, you might provide a live dashboard or AI tool that clients can query anytime for the latest insights (with your firm’s expertise baked in). If market conditions change overnight, the AI can incorporate new data and adjust the recommendations by morning. This moves you toward a “productized” service model that provides ongoing value, not just a deliverable that depreciates the moment it’s delivered. Clients will pay for agile, always-current insight more than for a static snapshot. In Gartner’s case, imagine if instead of selling expensive annual subscriptions to reports, they offered an AI portal where clients could ask any question and get Gartner-validated answers on the fly (which is presumably the idea behind AskGartner, if fully realized). That’s the kind of 24×7 value an AI-powered model can deliver.
Turn one-and-done engagements into “tools” clients (or teams) reuse. Old model: a consultant works for a month, delivers a PDF or slide deck, and that’s the end until the client pays for another project. New model: capture that month of work into a tool or knowledge base that the client can keep using. This is what we mean by “Services as Software.” Instead of just handing over a service deliverable, you embed the service into a software-like solution. For example, if you’re an accounting firm that built a custom forecasting model for a client, turn it into a user-friendly app (with AI for updates) that the client can run themselves every quarter. If you’re an HR consultancy that creates a new hiring process for a company, deliver it as an interactive AI-driven guide or a small system, not just a document. By doing so, you provide lasting value and potentially a subscription-based revenue stream, rather than a one-off fee. Services-as-Software “eliminates the B.S.” – blending automation, AI-driven decision-making, and outcome-based delivery. It’s about moving from a pure human labor model to a scalable solution model, where your expert insights are baked into something the client can use repeatedly. Not only does this delight clients, it also defends your business from pure-AI competitors because you’re effectively becoming a tech-enabled firm yourself.
We’re already seeing this transformation. A recent HFS Research study found that 60% of enterprises are looking to procure what used to be human-delivered services as technology or AI-powered offerings. They want the outcomes, not necessarily the human hours. The lines between services and software are blurring, and by 2030 the expectation is that “service provision [will be] performed by AI, not people” for a large chunk of standard work. That doesn’t mean people become irrelevant; it means people will be focusing on the higher-level, creative, strategic parts while AI handles the routine execution. Companies that embrace this “services as software” paradigm now will lead the pack, and those that cling to the old ways risk becoming the next Gartner – facing a stock cliff dive or a swift loss of market relevance.
The Upside of the Upside-Down World
AI is indeed turning the value creation model upside down. Tasks that used to be high-value billable work are becoming automated commodities, and new high-value tasks are emerging in their place. This can be scary – but it’s also a huge opportunity. The work that experts do is shifting: AI will take over some tasks, and humans will excel at others that AI can’t do (at least not yet). For example, AI can crank through data and options, while a human expert excels at understanding context, exercising judgment, and building trust and empathy (things a machine can’t replicate). The organizations that thrive will be those that redefine roles and workflows to maximize this new partnership. My team has gone through each “old school” expert work habit and mapped it to a new interwoven human–AI workflow. In every case, the hybrid approach beats either humans or AI alone.
The takeaway for leaders is: don’t fight the AI wave – ride it. You can’t effectively compete in a new world with an outdated business model. If you try, you might survive a bit longer, but you’ll likely slowly lose ground (or quickly, as Gartner did). On the other hand, those who proactively reinvent their services with AI can not only avoid disaster, but actually unlock new growth. They can serve more customers, tackle new problems, and create innovative offerings that were impossible in the purely human-based model.
The time to act is now. Even if your revenue isn’t hurting yet, don’t be complacent – often the impact of technology is felt suddenly, as many print media or Blockbuster Video executives learned too late. Avoid your own stock cliff dive by taking the initiative. Start pilots with AI, retrain your teams to work alongside these tools, and rethink how you package and price your expertise. If you do this, you won’t just survive the AI disruption – you’ll use it as a springboard to leap ahead of competitors stuck in the past.
Reach out if you want help making this transition. We’ve helped organizations turn “oh no, AI is a threat” into “aha, AI is our edge.” The future of expertise is one where human insight plus AI capability delivers unprecedented value. Those who get it right will not only protect their business – they’ll redefine their industry on their own terms. Don’t wait for the market to punish you to make the change. Gartner’s pain can be your gain – use this moment to ask, how can we harness the tech that’s disrupting us, and make it the key to our future growth?
In summary: Partner with AI, reinvent your expertise delivery, and scale your impact – or watch AI commoditize your business from the sidelines. The choice is obvious. The work we do as experts must change, so let’s change it on our terms. Embrace the upside-down and find the upside in it. Your clients, your employees, and yes, your shareholders will thank you.
Sources:
Gartner/Motley Fool analysis nasdaq.com nasdaq.com;
Stocktwits news on AI impact stocktwits.comstocktwits.com;
AInvest investor commentary ainvest.comainvest.com;
HFS Research on AI replacing services horsesforsources.com;
PwC/BI insight on consulting and AI goingconcern.com;
Cicerai legal AI report cicerai.comcicerai.com;
Northern Light on internal knowledge northernlight.com;
V7 Labs on AI adoption v7labs.com;
OpenAI workforce impact studyvice.com;
Terzo “services as software” perspective terzo.ai.
