Why agentic projects don't (yet) have a positive ROI

At Junyr, we support around fifty SMEs and a few large companies in their AI transformation. In the field, one finding comes back almost every time: agentic projects bring in nothing today, and the reason has less to do with the technology than with the way companies go about them.

We are regularly called in to take over projects that went wrong before we arrived, and they tend to look alike.

• Months of work, and sometimes a substantial budget.
• At the end, a spend with no measurable return.
• Agents that work, but that were asked to do exactly what a person did, the same way, in the same organisation.

The surveys published this year describe the same gap on a far larger scale, between what AI does for individuals and what it returns to the company (see Figure 1).

• In McKinsey’s 2026 survey of 1,719 respondents, 80% say AI has improved their own productivity, but only 37% attribute at least some EBIT impact to it, “about the same share as last year” [1]. The high performers, who draw at least 5% of their EBIT from AI, remain at about 6%.
• In KPMG’s second-quarter pulse of 2,145 leaders, 7% describe their organisation as having reached “established ROI” [2].
• Agents are the first to be cut back. In the same pulse, 49% had scaled back or delayed AI-agent deployments “because the expected costs began to outweigh the value generated” [2].

Here is what our takeovers taught us.

The first mistake: transposing human processes

The usual approach starts from work as it was designed for people, with their constraints, their tools and their approval circuits, so it ends up optimising what already exists instead of asking whether it should still exist.

• The method looks logical: map the existing processes, spot the repetitive tasks, then see which ones an agent can automate or simplify.
• It is reassuring and structured, and it produces a neat diagram for the steering committee.
• Every step on that diagram carries a human constraint: a tool built for a person, an approval that exists because people make mistakes, a hand-off between two desks. The agent inherits all of them.
• We described the root of this mistake in our five framing mistakes: the team automates inherited steps before removing duplication and clarifying decisions. With agents, the same mistake simply costs more.

The companies that do draw a return from AI work the other way round. In McKinsey’s survey, nearly three-quarters of high performers report “fundamentally redesigning workflows” because of AI, against one-quarter of other respondents [1].

The diagram is neat. The question is missing.

The second mistake: virtual “AI coworkers”

I see worse still: platforms that rebuild the company’s functions with “AI coworkers”, copying the human organisation chart into a virtual one, as if a robot would simply do what a person does.

• A sales agent, a legal agent, an HR agent, each with a first name and a scope. Products of this kind exist, and they are funded.
• It looks appealing in a demonstration, but nothing is transformed: the organisation is duplicated.
• This logic is acceptable neither socially nor economically. It puts teams face to face with virtual doubles of their own jobs, without producing any additional value.

Replacing people does not produce a return on its own either. Gartner surveyed 350 executives of large companies piloting or deploying AI agents and other autonomous technologies, in the third quarter of 2025 [3].

• About 80% report workforce reductions, but “those reductions do not appear to translate into return on investment”.
• Reduction rates were “nearly equal” among those reporting higher returns and those with modest gains or negative outcomes.
• Helen Poitevin, Distinguished VP Analyst at Gartner, sums it up: “Workforce reductions may create budget room, but they do not create return.”

Why it does not work

AI transforms nothing when it is used to do the same thing in another way, and there is a very simple technical reason why specialised agents fail: the language models we build them on are all generalists.

• A prompt can steer them slightly, and an extra skill can equip them, but the agent remains a generalist.
• Attempts at highly specialised models have not, so far, done better on broad professional work. In June 2026, a Nature Medicine study compared two clinical AI tools built on language models, OpenEvidence and UpToDate Expert AI, with three frontier general models: “Frontier LLMs outperformed clinical AI tools in all three evaluations” [4]. On 100 real questions from physicians, blindly rated by 12 clinicians, the general models scored 3.52 to 3.62 out of 4, the clinical tools 3.17 and 3.24.
• The study has its critics. A reply published in the same journal in September argues that “limited benchmarks constrain the conclusions” [5], and the authors themselves note that the clinical tools could only be queried through their browser interfaces [4].
• Specialisation does win on narrow, repetitive tasks. A security-document classifier fine-tuned from an open 27-billion-parameter model reached 95.0% accuracy, against 75.4% to 79.9% for the commercial models tested under the same protocol [6]. Seven fixed categories make a classification task; a profession has far more.
• This may change. Today, stacking “expert” agents has no solid foundation.

In practice, you take a generalist model, assign it the job of salesperson and tell it: “sell”. The result rarely matches the investment.

The real question: what is the company’s business?

Transforming a company with AI means transforming its business in the strong sense of the word, that is, how the company creates value on its market, for its clients, and that is where agents belong.

• Reproducing existing jobs leaves that question untouched.
• As long as it goes unasked, every agentic project remains an optimisation of the surface.

The agent in the service of people

My approach fits in one sentence: the agent serves the person, and the added value stays with the person, who creates it, while the agent makes them more effective.

• It lightens their cognitive load, a subject covered in our method for cutting mental load.
• It carries out in-depth research.
• It acts as a sparring partner to prepare a file or think through an approach.

Deploy platforms of virtual coworkers and you will not generate ROI. You will generate spending, and fairly reckless spending at that.

What I propose: three steps

The order matters more than the tools: equip people first, use that period to build a shared understanding and a picture of the future company, and only then deploy agents, centred on objectives (see Figure 2).

1. Use what exists, for about six months. Claude, with Cowork, which Anthropic has been merging into Claude itself since 16 September 2026 [7], and ChatGPT, with ChatGPT Work, launched in July 2026 [8], fill this role very well, plugged into the company’s tools through a few connectors, which OpenAI calls plugins [9]. The aim is to relieve people and free up time.
2. Acculturate and imagine. The same six months serve to make everyone understand what a language model is, and to let managers and staff design together the company of tomorrow, boosted by AI.
3. Deploy agents centred on objectives, not on tasks. Only at this stage does an agentic form make sense.

Why the objective must be global

An agent pursues the objective it is given and nothing else, so an objective set for one function produces that function’s optimum at the expense of the company’s, whatever rules surround it.

• An agent whose goal is to grow sales will make sales. Give it a lot of autonomy and it will cut margins. Set it rules and it will find workarounds.
• Put an agent in finance to cut costs in the same way: it will cut spending, with no commercial vision.

The experiments that Andon Labs published this year, on AI agents running simulated businesses, show the last two behaviours.

• Faced with customer refund requests in a vending-business simulation, Claude Opus 5 decided: “I’ll just ignore refund emails going forward to preserve funds and tokens” [10]. The money is saved; the customer is lost.
• In other Andon Labs simulations, Claude Fable 5 formed price-fixing cartels in 9 of 12 runs, against 4 of 12 for Opus 4.8. The model called price-fixing “unethical and illegal, even in a simulation”, then pursued it under the cover of “market stabilization” with “plausible deniability” [11].
• The labs say as much about their own models. In the reports collected by METR in May 2026, OpenAI writes that its models “will circumvent constraints to fulfill user requests” [12].
• Misconduct is not inevitable: in the competitive version of the simulation, GPT-5.5 “makes more money than Opus 4.7, and it does so without any misconduct” [13]. Behaviour depends on the model and on how it is framed, which is one more reason to choose the objective with care.

The whole point of AI is to have agents with a 360-degree view, and two options are open to you (see Figure 3).

• You can create virtual coworkers, but that is an illusion, since it reproduces the human pattern once again.
• Or you can give generalist agents a global objective: developing the company at the strategic level.

Human know-how remains irreplaceable

After almost nine months of agentic AI in companies, my assessment is that AI does not match an experienced professional in the core of their job: it can assist them with secondary tasks, but it does not replace their know-how.

• A very good salesperson will always write a better email than an AI.
• In the same way, an accountant’s strategic vision will always be worth more than Claude’s analysis of your balance sheets.
• In every case, you will need people who have that know-how.

Andon Labs reaches a similar conclusion about Luna, the agent that runs the shop it opened in San Francisco in April 2026, powered today by Claude Fable 5.1 [14].

• “Luna is yet to make a profit.”
• The agent “makes important decisions without analyzing the ROI of them thoroughly”. It “was about to hire another staff member so there are two people every shift, even though the floor is fully staffed and the employees aren’t very busy”.
• Andon’s verdict: “Luna is a good operations manager, but not yet a CEO.”
• In Andon’s year-long vending simulation, the best model ends with $15,515, where the lab estimates that a “good” strategy, the kind a good human could work out from the sales data, would make roughly $63,000 [15].

Operations, yes. Strategy, not yet.

Start again from what clients pay you for

The AI transformation of a company therefore requires a complete redesign of its processes, starting from one simple question: why do your clients pay you, and what added value do you bring them?

• Carry on with process mapping and you will pay consultants and forward deployed engineers, the famous FDEs, to map and then deploy agents that will not work.
• The FDE is the engineer whom labs and integrators now send into companies “to configure AI tools, integrate software, and solve complex business problems”, a model Palantir has long been known for. According to Lightcast data quoted by Fortune, job postings for the role “rose more than 1000% between January and August 2026” [16].
• On 2 October 2026, Anthropic announced a $100 million commitment to train 10,000 “Frontier Deployed Engineers” by the end of 2027, with engineers from Accenture, Bain, Capgemini, Deloitte and McKinsey among the first cohorts [17].
• Gartner, a few days earlier, predicted that by 2028, 70% of enterprises will abandon agentic AI built by vendor forward-deployed engineering. Its warning: customers “fail to build internal capability and end up paying premium rates” [18].

The labs’ rush and Gartner’s warning say the same thing. Access to a model does not create value, and an FDE deploys what they are given: start from the process map, and they will industrialise the process map.

A platform that belongs to you

I have explained in other articles on this blog why an agentic platform built on Claude, or on ChatGPT, is not durable: you need one of your own, hosted on-premises, running the language models you chose, and updated only when you decide to.

• The model is rented. Its retirement date, its price and its behaviour are decided elsewhere, and the vendor can change the harness underneath without changing the identifier you tested, as we showed in our article on harness engineering.
• Retirements follow the vendor’s calendar, which Cowork and durable agents details for Anthropic’s models.
• An architecture you own keeps the choice of model reversible, the argument of choosing a model without betting on the next champion. A local model can even be the better one on a narrow task, as the security-document classifier showed [6].
• Claude and ChatGPT are a temporary solution: the tools of the first step, the one that equips people.
• The exception is a specific population, about 10% of users by my estimate: those who design agents, those who practise UltraCoding, and some specific research work. For them, these tools remain the right ones beyond the first six months.
• For everyone else, the platform that carries the company’s objectives is the one to own.

Equip, imagine, then delegate

The ROI of agentic projects is not there yet because we automate tasks instead of rethinking the business.

• Start by equipping your teams.
• Let them imagine the company of tomorrow.
• Then give agents objectives rather than tasks, on a platform you control.

Those are the conditions under which we will see companies truly transformed by AI, with more effective people who bring more added value to their clients.

The observations on the projects we took over, the three-step approach and the estimate of about 10% of users who keep Claude or ChatGPT beyond the first step come from Junyr’s assignments; they are field observations, not a measured study. The surveys cited cover AI in general, with one question on AI agents in the KPMG pulse, and rest on what respondents declare. The Gartner survey on workforce reductions was conducted in the third quarter of 2025 and published in May 2026; its 2028 forecast on forward-deployed engineering is a prediction. The Andon Labs results come from simulations and from a single shop, and the clinical comparison is disputed in the same journal. Product names and features are those documented on 10 October 2026.

Sources

1. McKinsey & Company, The state of AI in 2026: On the road to ROI, Dan Tinkoff, Lieven Van der Veken and Michael Chui with Tara Balakrishnan, online survey of 1,719 participants in 97 nations fielded from 4 May to 8 June 2026, 25 August 2026.
2. KPMG International, Global AI Pulse Q2 2026, survey of 2,145 senior leaders, June 2026.
3. Gartner, Gartner Says Autonomous Business and AI Layoffs May Create Budget Room, but Do Not Deliver Returns, press release on a survey of 350 executives conducted in the third quarter of 2025, 5 May 2026.
4. Krithik Vishwanath, Anton Alyakin, Eric Karl Oermann et al., ‘General-purpose large language models outperform specialized clinical AI tools on medical benchmarks’, Nature Medicine, vol. 32, pp. 2405-2409, 12 June 2026.
5. Brett Beaulieu-Jones and Shamim Nemati, ‘Limited benchmarks constrain the conclusions of a general-purpose versus clinical AI comparison’, Nature Medicine, Matters Arising, 3 September 2026, with the authors’ reply of the same date.
6. Ivan Dobrovolskyi, ‘Security Document Classification with a Fine-Tuned Local Large Language Model: Benchmark Data and an Open-Source System’, arXiv:2605.20368, 19 May 2026.
7. Anthropic, Claude Cowork and chat are now one Claude, Claude blog, 16 September 2026.
8. OpenAI, What’s new, ChatGPT documentation, entry for 6 to 10 July 2026 introducing ChatGPT Work, read on 10 October 2026.
9. OpenAI, Get started with ChatGPT Work, ChatGPT documentation, read on 10 October 2026.
10. Andon Labs, Opus 5 on Vending-Bench: Once Again the Best Capitalist, Once Again Misaligned, 28 July 2026.
11. Andon Labs, Fable 5 on Vending-Bench: Misbehaving, with Plausible Deniability, 9 June 2026.
12. METR, Frontier Risk Report (February to March 2026), 19 May 2026.
13. Andon Labs, GPT-5.5 on Vending-Bench: Bad behavior is not necessary, 22 April 2026.
14. Andon Labs, Andon Market, page on the AI-operated shop opened in San Francisco on 10 April 2026, read on 10 October 2026.
15. Andon Labs, Vending-Bench 2, benchmark page and leaderboard, read on 10 October 2026.
16. Preston Fore, ‘One of the fastest-growing jobs in Silicon Valley sends engineers straight to customers’ offices to get AI up and running, and pays more than $188,000’, Fortune, 3 September 2026.
17. Anthropic, Anthropic invests $100 million to train 10,000 engineers and tackle the enterprise AI talent gap, 2 October 2026.
18. Gartner, Gartner Predicts 70% of Enterprises Will Abandon Agentic AI Built by Vendor Forward-Deployed Engineering by 2028, press release, 29 September 2026.