Why Efficiency Is No Longer Enough: AI Business Model Innovation In 2026
Read 14 min
Most companies have used AI to work faster. Far fewer have used it to sell something new. That gap — between efficiency gains that competitors quickly copy and new revenue that they cannot — is where competitive position will be decided over the next two years. Written for executives, department leaders and transformation teams weighing their next move.
Most organisations use AI to do familiar work faster. A smaller group uses it to sell something new — outcomes instead of software, data products instead of reports, platforms instead of services. Deloitte finds only 34% of organisations are using AI to create new products or reinvent business models, while 74% still describe AI-driven revenue growth as a future ambition. That gap is where competitive position will be decided over the next two years. This article explains the shift, the opportunities, the risks, and what business leaders should do about it.
Estimated reading time: 14 minutes
Key Takeaways
Efficiency gains are now table stakes. The organisations pulling ahead are changing what they sell, not just how fast they produce it.
Pricing is moving from seats to outcomes. Zendesk, Intercom, Salesforce and Decagon already charge for verified results rather than software access.
Proprietary data is the most under-used asset on most balance sheets. Packaged insights and data-powered products are more defensible than raw data feeds.
Transformation failure is the norm, not the exception. BCG puts the share of organisations capturing meaningful AI value at scale at roughly 5%, and describes the work as 30% technology and 70% people and organisation.
Smaller companies have a real structural advantage here — fewer legacy systems, shorter decision chains, and less to unwind.
Outcome-based pricing carries a hidden trap: efficiency gains can flow to the vendor rather than the buyer. Hybrid models with floors and caps are the safer starting point.
The Strategic Challenge
Ask most executive teams what AI has done for the business and you will hear a version of the same answer: the support team handles more tickets, the marketing team produces more content, developers ship a little faster. All real. All useful. And all of it stops at the same ceiling.
That ceiling has a name. Call it the efficiency trap — the point at which AI has been layered onto existing processes but nothing about how the company creates or captures value has actually changed. Deloitte’s 2026 State of AI research puts numbers on it: 34% of organisations are using AI to deeply transform by creating new products and services or reinventing core processes and business models, 30% are redesigning key processes around AI, and 37% are using AI with little or no change to existing processes.
The revenue picture is starker. Deloitte finds that 74% of organisations hope to grow revenue through AI in future, while only 20% are already doing so. That is a very wide gap between intention and result.
Here is the part that should concern boards: the problem is not technical. Nearly every organisation now has access to the same models, and the price of that access keeps falling. The problem is the question being asked. Most companies ask how AI can make existing processes faster. The organisations building durable advantage ask what new value AI can create that was not possible before.
PwC frames the same idea as an 80/20 rule — technology delivers roughly 20% of an initiative’s value, while 80% comes from redesigning the work itself so that agents handle routine tasks and people concentrate on what actually drives impact. If that ratio is even approximately right, most AI budgets are being spent on the smaller half of the problem.
The consequence of ignoring this is not dramatic. Nobody gets disrupted overnight. What happens instead is quieter and harder to reverse: a competitor moves to outcome-based pricing and starts winning deals on risk transfer rather than features. Another packages its operational data into a product and adds a revenue line you cannot match. Your margins are fine. Your growth is not.
Callout — The question that separates the two groups
“How can AI make our existing processes faster?” is an operational question. It produces cost savings that competitors will match within a year.
“What new value can AI create that was not possible before?” is a strategic question. It produces revenue streams that are much harder to copy, because they depend on your data, your workflows and your customer relationships.
Both questions are worth asking. Only one of them belongs on a board agenda.
Who This Strategy Is For
This is not a universal mandate. Business model innovation is expensive in management attention, and attention is the scarcest resource most leadership teams have.
Act on this now if you are:
A software or services business whose customers can point to a measurable result your product produces. You are the most exposed to pricing model change, and the best positioned to lead it.
An established company with unusual proprietary data — transaction records, sensor readings, industry benchmarks, operational history that nobody else holds. That asset is currently sitting idle.
A mid-market company competing against larger rivals. Speed is your advantage and it has a shelf life.
A leadership team already seeing AI efficiency gains plateau. The plateau is the signal that the next move is a different kind of move.
This is not yet the right priority if you are:
Still without basic data hygiene — if you cannot answer simple questions about your own operations reliably, an intelligence engine has nothing to run on.
In the middle of a larger transformation that is already consuming leadership bandwidth.
Operating in a heavily regulated niche where outcome-based contracting or data productisation would create compliance exposure you are not resourced to manage.
A very small team without a single workflow that runs at enough volume to justify redesign.
For that last group, the honest advice is to keep using AI for productivity, keep your data organised, and revisit this in twelve months. There is no prize for being early to a strategy you cannot staff.
The Current Landscape
Three shifts are happening at once, and they reinforce each other.
From AI-enabled to AI-first
The World Economic Forum, working with Kearney, describes a spectrum of AI maturity that is useful mainly because it forces an honest self-assessment.
Level
What it means
AI’s role
AI-enabled
AI added on top of existing processes
A productivity tool
AI-first
The operating model is redesigned around intelligence
A strategic lever
AI-native
The organisation was built around AI from the start
Core infrastructure
Most large organisations are firmly in the first row and describe themselves as being in the second. The difference shows up in specifics — how data is architected, how roles are designed, how value is measured.
Dimension
Traditional enterprise
AI-first enterprise
Deployment model
Point solutions and pilots
Embedded across core processes
Operating model
Human-led, AI-assisted
AI-orchestrated, human-augmented
Data architecture
Siloed, project-specific
Unified, self-reinforcing
Workforce design
Roles designed before AI existed
Roles redesigned around AI
Governance
Reactive, compliance-driven
Embedded and continuous
Value metric
Cost reduction
Decision quality, speed, new revenue
The uncomfortable line in the WEF research: despite more than $250 billion invested in AI globally in 2025, only about a quarter of companies report a transformative impact. A related figure is more diagnostic — roughly 84% of organisations have not redesigned jobs around AI capabilities. You cannot build an AI-first operating model on a workforce structure designed for a pre-AI world.
Pricing is changing underneath the software industry
This is the shift with the most immediate commercial consequence, because it changes how buyers evaluate vendors.
Pricing model
What the customer pays for
Position (Aug 2026)
Seat-based
Access per user
Declining, still the legacy default
Usage-based
API calls, tokens, actions
Roughly 43% share; currently the leading model
Outcome-based
Verified results
Roughly 29%; fastest-growing
Hybrid
Subscription plus usage plus outcome
Growing; becoming the enterprise default
Gartner’s projection is that 40% of enterprise SaaS will shift to outcome-based billing by 2030. Among AI-native SaaS companies, the move to usage-based billing is already close to universal.
Note on these figures: the market-share percentages come from a single industry tracker and one of them reports an eight-point jump in a single month, which is faster than a structural pricing shift usually moves. Treat the direction as well established and the precise numbers as indicative.
Data is becoming a product
The market for data monetisation is real but the sizing is messy — published figures range from under $5 billion to over $50 billion depending on whether “data-as-a-service” is included. What is more useful than the market size is the behaviour: roughly 65% of enterprises pursuing data monetisation report positive ROI, and organisations treating data as a product are reported to generate around 5–10% of annual revenue from those streams.
And smaller companies are moving faster than expected
SMB AI adoption has climbed from roughly 22% in 2024 to about 38% in 2026. Among those using it, 91% report revenue increases, with an average return reported at 5.8x within 14 months of production deployment. Those numbers come from vendor-adjacent research and self-reported surveys, so read them as encouraging rather than precise — but the direction is consistent across multiple sources.
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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.
The Future Signal
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