Why Most Companies Scaling AI Agents Still Aren't Seeing Profits
Read 18 min
AI agents are spreading quickly through large organisations, but most companies still cannot point to a profit gain. This strategy explains why the gap exists and how the small group of high performers close it. It is written for executives and department leaders deciding where agents belong and how to scale them responsibly.
AI agents are software that can plan and carry out multi-step tasks. They are moving from experiments into everyday operations, especially at large companies, but the financial results are not keeping up.
Most organisations report no profit impact from AI. Running costs are becoming a constraint, and few companies have mature controls for agents.
This article is for executives and department leaders deciding where agents belong in their business. The biggest takeaway: agents pay off when companies redesign how work gets done around them, measure results against a clear starting point, and control cost and risk from day one.
Estimated reading time: 18 minutes
Key Takeaways
Returns lag adoption. Large companies are scaling AI agents quickly, but the share of organisations reporting any profit impact from AI has barely moved in a year.
Redesign beats add-on. Companies getting real returns redesign workflows around agents. They do not add agents to old processes.
Success can raise costs. Agent costs grow with usage, so a popular rollout can still weaken the business case if nobody tracks the cost of each completed task.
Controls are behind. Only about one in five organisations report mature governance for agents.
Hybrid works best with customers. In customer-facing work, mixed human-and-agent models have held up better than full replacement.
Pilot before you scale. For most businesses, the right move is a structured pilot with clear success thresholds, rather than broad rollout or waiting.
Strategic Challenge: Why Adoption Isn’t Turning Into Profit
Most technology rollouts struggle to get people using the new system. With AI agents, the struggle comes later: getting a return.
What makes an agent different from a chatbot?
A chatbot answers a question. An agent is given a goal, access to tools and data, and some freedom to act on its own. It might update a customer record, process a request or pass work to another system.
That makes agents more useful than chatbots. It also makes them more expensive to run and riskier when they get something wrong.
The evidence of the gap
McKinsey’s State of AI survey, published in August 2026:
80% of respondents said AI had improved their personal productivity.
Only 37% said AI had contributed to their organisation’s operating profit (EBIT). That share is essentially unchanged from a year earlier.
Key insight: Adoption is not the problem. Turning agents into profit is.
Three pressures arriving together
Value. Individual time savings are real. They rarely reach the profit and loss statement unless the work itself is restructured.
Cost. AI services are largely billed by usage. McKinsey found about one in five respondents say running costs have already limited their organisation’s use of AI.
Control. Agents act with permissions delegated by people. Security and governance practices built for human users and traditional software do not fully cover them.
The cost of ignoring these pressures
Gartner forecast in June 2025 that more than 40% of agentic AI projects will be cancelled by the end of 2027. It cited rising costs, unclear business value and weak risk controls.
That is a forecast, not a certainty. But it describes exactly the gaps most organisations still have.
Who This Strategy Is For
Not every organisation faces this decision in the same way. Your size, your processes and your ability to measure results all change the right starting point.
Organisation type
Relevance
Why it matters
Large enterprises
High: act now
Many are already scaling agents. The question is returns, not adoption.
Mid-sized and growing companies
High: pilot first
Evidence shows slower scaling outside large firms. A disciplined approach can set you apart.
Executive teams and finance leaders
High
They own the budget, return and risk decisions that determine whether agents survive.
Department leaders (IT, service operations, supply chain, marketing and sales)
High
These functions show the most reported agent use and value.
Small businesses
Medium: test packaged tools
Independent evidence on small-business returns is limited.
When this strategy may not yet be appropriate
No documented processes or baseline data. Without these, you cannot tell whether an agent improved anything.
Error-sensitive, irreversible work without human approval. If you cannot add a human sign-off step, the risk outweighs the benefit.
No way to track AI costs by use case. Spending will be hard to control.
AI use is still limited to general assistants. Getting individual productivity right first is a sensible step.
Current Landscape: Fast Adoption, Flat Returns
The market has moved past the question “should we try agents?” The harder question is now “why aren’t they paying off?”
Signal
What the evidence shows
Source
Agent scaling at large organisations
40% of respondents from organisations with more than $1 billion in revenue report scaling agents, up from 27% a year earlier
McKinsey, August 2026
Agent scaling at smaller organisations
Flat at 22%
McKinsey, August 2026
Profit impact
37% report any contribution to operating profit, essentially unchanged
McKinsey, August 2026
High performers
About 6% attribute at least 5% of operating profit to AI and report significant value
McKinsey, August 2026
Governance maturity
21% report a mature governance model for agentic AI (surveyed August–September 2025)
Deloitte, State of AI in the Enterprise 2026
AI cost management
98% of FinOps practitioners now manage AI spend, up from 31% in 2024
FinOps Foundation, State of FinOps 2026
Investment plans
60% expect their organisation’s AI investment to rise next year
McKinsey, August 2026
FinOps is the practice of tracking and managing technology spending, originally for cloud services.
Where agents are being used
Adoption is concentrated. McKinsey respondents most often report scaling agents in IT, knowledge management and software engineering.
It also varies by industry:
Retail and consumer goods companies lean towards marketing and sales.
Manufacturers lean towards supply chain and production.
Who is making the decisions
Buying power is shifting. Mayfield’s 2026 survey of senior technology executives found that line-of-business leaders were the largest group influencing AI tool adoption (46%), ahead of CIOs and CTOs (38% each).
That means agent decisions are increasingly made by business leaders, not only IT.
How software budgets are changing
32% of McKinsey respondents said their organisation had decided against buying at least one software product. Their reason: they could build it in-house with AI coding tools.
Read adoption figures carefully
Survey numbers vary widely because “using,” “piloting” and “scaling” mean different things. Vendor-published surveys often report very high adoption. Independent surveys with clear definitions are a better guide for planning.
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The change creates real openings, but only for buyers who prepare.
You can pay less for work you were already buying. Customer support is the most developed category. Resolution rates are measurable, several vendors compete openly, and published prices range from roughly $0.40 to $2.00 per resolution. If you handle high ticket volumes and have decent help content, the maths often works in your favour.
Your renewal is more negotiable than it looks. Procurement platform Tropic, drawing on more than $15 billion of software spend, found that structured negotiation cuts a vendor’s opening ask by around 55%. The final price still tends to land about 12% above the old baseline — the increase is negotiable, not avoidable.
Timing alone is worth money. The same data shows businesses starting renewal conversations six months ahead save considerably more than those starting a month out.
Unused licences become bargaining chips. If AI has taken over a workflow, the licences attached to it are still being paid for. Find them before your vendor’s account manager does and they become leverage. Find them afterwards and they are simply money you wasted.
You can trade commitment for protection. Vendors part-way through changing their pricing want reference customers. They will often agree to price locks, waivers on mandatory AI bundles, hard spending ceilings and credit rollover in exchange for a volume commitment. That is a very different conversation from asking for a discount.
Future Signal Tip
Before your next renewal, ask the vendor to show you the return on their AI features using your usage data — not a case study from another company. If they cannot produce it, you have just found your strongest negotiating point. If they can, you have learned something useful either way.
Strategic Risks
Every one of these risks has already caught out a well-run company.
Spending runs away before value arrives. Uber encouraged staff to use AI coding tools heavily and ranked usage on internal leaderboards. It exhausted its entire 2026 budget for those tools in roughly four months. Bloomberg reported the fix in June: a cap of $1,500 per employee per month, per tool, with a usage dashboard and a process for requesting more.
You pay for AI features you never use. Vendors bundle AI into existing products and charge for it whether or not anyone touches it. Tropic’s contract data puts these increases at 20–37%, against a historical 3–9% annual uplift. CFO Dive reported the same finding: prices are rising faster than the value buyers can see.
Comparison becomes almost impossible. Credit-based pricing is not comparable between vendors. A credit at one company is not a credit at another, which quietly removes your ability to benchmark.
Prepaid credits can expire. Salesforce Flex Credits must be used before the order period ends, with no rollover. Testing in a sandbox consumes them too. Money you have already paid can simply evaporate.
The vendor writes the definition of success. Fin’s documentation describes a conversation as resolved when the agent answers and the customer either confirms it helped or leaves without asking anything further. A frustrated customer who gives up can bill identically to a happy one.
Your current pricing is not permanent. HubSpot’s April 2026 change applied to every customer with no opt-out and no grandfathered tier.
Acquisitions reset the terms. Salesforce signed an agreement to buy Fin for around $3.6 billion in June 2026, expected to close in its 2027 financial year. Roadmap, packaging and pricing can all shift after a deal closes.
Efficiency gains often disappear. Time saved tends to be absorbed by employees as a better working week rather than converted into capacity you can redeploy. It is a real effect, and almost nobody models it.
Decision Box
Act now if: you have a renewal within twelve months, you already run AI agents without spending caps, or your support and sales workflows have visibly changed.
Pilot first if: you are considering outcome-priced tools but have no baseline data yet. Start with one measurable workflow, ideally customer support.
Prepare if: your contracts run beyond eighteen months. Use the time to audit unused licences and build measurement.
Monitor if: your software spend is small and fixed, and you have no AI rollout planned this year. Revisit before your next contract.
Whatever you choose, do not sign a consumption-based contract without a written definition of what counts as billable.
Implementation Roadmap
A practical sequence for the next six to twelve months.
STAGE 1 STAGE 2 STAGE 3 STAGE 4 STAGE 5
Assess → Control → Measure → Renegotiate → Reallocate
Weeks 1-4 Weeks 4-10 Weeks 6-14 Months 3-9 Months 6-18
List every Set spending Record where Open renewals Retire unused
tool by how caps and you started 6 months tools; move
it charges usage limits before you early, armed savings into a
you per team deploy AI with evidence managed budget
Stage 1 — Assess what you actually have. List every tool by pricing model: per seat, per usage, credits, per outcome, or hybrid. Flag every renewal falling in the next twelve months. Identify licences attached to workflows that AI has already taken over.
Stage 2 — Put controls in place before you need them. Set spending limits per person, per team and per tool, with a clear route to request more. Give budget owners and end users the same usage dashboard. Decide which approvals are needed for which type of work — reviewing a contract and running an autonomous investigation cost very different amounts.
Stage 3 — Record your starting point. For each AI use case, capture what the work costs today, how long it takes, how much of it there is, and how often it goes wrong. Agree in advance which financial line this work is expected to move, and name one person accountable for it. A baseline taken after deployment is worthless.
Stage 4 — Renegotiate from evidence. Start six months out. Ask explicitly and in writing for your existing pricing. Then negotiate the terms that actually protect you — price locks, no forced AI bundles, hard spending ceilings, credit rollover, exemption for testing environments, and a written definition of a billable outcome with a process for disputing one.
Stage 5 — Reallocate deliberately. Retire the tools your audit uncovered. Move the savings into a managed AI budget rather than letting them disappear into general costs. Review quarterly: what does each outcome cost, is that cost stable, and has any saved time actually been redeployed?
Common Mistakes
Treating the AI increase as part of a routine renewal. Vendors prefer this, because one number is harder to challenge than two. Price the base platform and the AI capability as separate decisions.
Signing before defining the billable event. This is the most expensive mistake available, and it happens because the definition sits in documentation rather than in the contract. Ask for it in writing, including what happens with partial outcomes and how you dispute a charge.
Rolling out AI tools with no spending limits. Uber is the well-documented version of a very common failure. Enthusiastic adoption plus per-use billing plus no caps equals a budget gone in a third of the year. Caps are not a brake on adoption — they are what makes wide adoption survivable.
Measuring the wrong thing. Counting logins, prompts or hours saved feels like progress and proves nothing. Boards are now asking which line of the profit and loss account changed. Very few organisations can answer.
Assuming outcome pricing is automatically cheaper. It is cheaper when your outcomes are simple and your content is good. It is expensive when your queries are complex, because you pay for every one the agent handles.
Confusing outcome pricing with transparent pricing. Fin publishes $0.99 on its website. Sierra publishes nothing at all, and independent estimates put a first year in the low hundreds of thousands. Both charge per outcome.
Success Metrics
Measure a small number of things properly rather than everything badly.
What to track
Why it matters
A reasonable target
Cost per outcome, and its spread
Averages hide the expensive cases that break your budget
Stable month to month, with the spread narrowing
Renewal increase vs the vendor’s opening ask
Tells you whether your negotiation worked
Around 55% reduction from the ask, landing near 12% above your old price
Share of software spend that is variable
Shows how much of your budget you can no longer forecast
Known and deliberate, not accidental
Consumption forecast accuracy
Early warning for the next budget overrun
Within 10% of plan each month
Prepaid credits expiring unused
Straightforward waste
Zero
Use cases with a recorded baseline
You cannot prove a return without one
100% before spending is approved
Verified resolution rate
Headline rates come from the vendor’s own definition
Sample and check resolved items yourself
Reopened cases after a billed resolution
The clearest sign your outcome definition is wrong
Falling over time
If you run a software business yourself, add one more: the share of new revenue that does not depend on your customers’ headcount. ServiceNow’s disclosed figure of 50% is a useful benchmark.
Future Outlook
What is already confirmed. Gartner expects consumption-based pricing to account for more than 35% of new corporate legal technology spending by 2028, and warned general counsel on 3 September 2026 that unprepared teams face budget shocks. Deloitte’s 2026 predictions describe the same move toward hybrid pricing across business software generally, and expect as many as 75% of companies to invest in AI agents by the end of this year. Both Deloitte and EY have published formal accounting guidance on how to recognise revenue from outcome-based AI pricing — a good sign that this is now a permanent structural change rather than an experiment.
What the industry expects. Hybrid pricing continues to spread as the default. Consolidation continues, with large platforms buying AI-native capability. The buyer’s negotiating window stays open for several more renewal cycles, since Gartner’s exposure estimate runs to 2030 rather than next year.
Our own reading, offered as interpretation rather than fact. Expect arguments about what counts as a billable outcome to become a visible commercial issue during 2027, as per-resolution billing reaches scale. Expect standard contract language for measuring outcomes to emerge, in the way that service-level agreements standardised for cloud hosting. And expect at least one vendor to retreat from pure outcome pricing back toward hybrid, because a flat fee per outcome only works while the mix of easy and difficult cases stays stable.
The Future Signal
The stock market spent early 2026 asking the wrong question. It asked whether AI would destroy software companies. The more useful question was always what software companies would start charging for instead.
The answer turned out to be unglamorous. Not a revolution in pay-for-results, but a steady move to a base fee plus a meter. That is closer to how you buy electricity than how you bought software.
The strategic implication runs deeper than procurement. When you paid per person, your software cost was a function of your hiring decisions — visible, predictable and set once a year. When you pay per unit of work, your software cost becomes a function of your operations, changing daily, driven by decisions made by people who have never seen a contract.
Very few businesses are set up for that. Most finance functions can tell you the headcount to the person and cannot tell you what a support ticket costs to resolve.
Leaders should focus on two things: knowing what a unit of work costs before they sign anything that bills by the unit, and making sure someone owns that number. What can safely be ignored is the noise about whether software is finished as an industry. It is not. It is changing what it sells, and the businesses that understand what they are buying will pay less for it.
What Businesses Should Do Next
Take these in order. The first three cost nothing but time.
List your software by how it charges you. Per seat, per usage, credits, per outcome, hybrid. Most businesses have never done this and are surprised by the result.
Mark every renewal in the next twelve months. Anything more than six months away goes to the top of the list, because that is where the savings are.
Find your unused licences. Look specifically at workflows where AI has changed how the work gets done.
Put spending caps on any AI tool billed by use — per person and per tool, with a way to request more. Do this before the first surprise, not after.
Record a baseline for every AI use case before you deploy: current cost, current time, current volume, current error rate.
Ask for the outcome definition in writing on any consumption or outcome-priced contract, including partial outcomes and how you dispute a charge.
Name an owner for each AI use case — someone accountable for a specific financial result, not a project sponsor.
Review quarterly. Cost per outcome, forecast accuracy, and whether saved time has actually been redeployed.
Pre-Renewal Checklist
Work through this before your next software contract conversation.
I know how this vendor charges — per seat, per use, per credit, or per outcome
I know what happens to unused credits at the end of the term
I have the definition of a billable outcome in writing
I know how partial or disputed outcomes are handled
I have asked for my existing pricing explicitly, in writing
I have asked for a spending ceiling to be written into the contract
I have checked whether testing environments consume paid usage
I have identified licences we no longer need
I have asked the vendor for return-on-investment evidence based on our own usage
I started this conversation at least six months before renewal
Frequently Asked Questions
Is per-seat pricing actually finished? No. It remains a good fit for software that holds your records and manages permissions — your CRM, finance and HR systems. What is fading is per-seat pricing for tools that simply perform tasks. Most vendors are moving to a mix of both rather than abandoning seats.
Will outcome-based pricing save my business money? Sometimes. It works well when your work is high-volume and reasonably simple, and when you have good documentation for an AI agent to draw on. It works badly when your cases are complex, because you pay for every one the agent handles. Model your real volumes against each pricing option before you decide.
What is the single most important thing to negotiate? The definition of a billable outcome. The headline price matters far less than what triggers a charge. Get it in the contract, not just the documentation.
Our vendor is raising prices for AI features we do not use. Is that normal? It is common. Vendors bundle AI into existing products and price accordingly. It is also negotiable — procurement data shows structured pushback substantially reduces the opening ask.
How do I stop AI spending running away? Caps and visibility, installed before the rollout. Set a monthly limit per person and per tool, with a documented way to request more, and give people a dashboard showing what they are spending.
Should a small business worry about this? Less urgently, but the same principles apply and the outcome-priced tools are often the most accessible ones. A support agent at under a dollar per resolution can be genuinely affordable for a small team — as long as you understand what triggers the charge.
Can we prove a return on AI investment? Only if you recorded a baseline before deploying. This is the most common gap. Very few organisations can currently connect AI spending to a change in profit, which is exactly why AI budgets are coming under scrutiny.
Our vendor is being acquired. What should we do? Treat your current terms as current rather than permanent. Ask your account team what is guaranteed through your renewal period, in writing, and get it before the deal closes.
Conclusion
The challenge is straightforward to state and awkward to solve. Software used to charge you for people. It is starting to charge you for work. Your budgeting, your contracts and your approval processes were all built for the first model.
The recommended approach is not to resist the change or rush into it, but to prepare properly for it. Know how each of your tools charges you. Put spending limits in place before your first overrun rather than after. Record where you started before you deploy anything. Then negotiate from evidence, six months early.
The biggest takeaway is that the money is not saved in the negotiation. It is saved in the preparation that makes the negotiation possible.
The next logical step is the smallest one: spend an hour listing your software by how it bills you, and mark every renewal falling in the next twelve months. Almost every business that does this finds something it did not expect — and finding it before your vendor does is worth more than any discount you will be offered.
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