Planning tool · Not a proposal
What should your AI programme cost?
A planning tool for your problem definition and solution exploration phase. Use it to frame the investment, align your buying group, and walk into vendor conversations with the right questions, before anyone has scoped anything.
AI Programme Cost Estimate
Sketch the programme you’re considering
Every field is an assumption, not a commitment, and each figure below updates as you change it. Nothing is sent to us or to anyone else, it stays in your browser and in the link you can copy at the foot of the page.
Used to size the value at stake in each domain you pick, and to show what the programme is worth as a movement in EBITDA. That is $200k per employee at your headcount, which is a normal range for an organisation this size.
The currency toggle sets every figure on this page, converted at static rates. AI platform licensing is billed in USD; professional services are quoted in your delivery partner's local currency.
Organisations run 10–20 process domains. We’d recommend a first programme targets the one or two with the highest value and the best chance of landing. Focused programmes deliver, broad ones stall.
A solution is a system inside the domain, such as customer service automation, demand forecasting or campaign production. The agents are the individual capabilities that make it work, and a solution needs 3 to 5 of them to move one business metric. A full domain transformation lands at 5 to 15 agents in total. If you don’t know yet, leave this alone; it’s the sort of thing a discovery workshop settles in an afternoon.
Year one doesn’t finish this scope, and that’s normal. The rest follows as the programme continues. If you need more live sooner, the lever is fewer domains rather than a faster build.
What your programme costs
A complete AI programme has six budget lines, and they are not all the same kind of money. Three are one-off, two recur every year, and one never leaves your own payroll.
Five sections follow, one for each part of that picture: who you need internally, how the programme runs, what you pay your AI platform, what recurs after go-live, and whether it pays back.
Your Internal Team
The people you need in place, all but one drawn from existing headcount
A programme needs protected time from people who already have full-time jobs. With one exception these are not new hires, but they are not side-of-desk either. Across the roles below the commitment is roughly ~373–533 person-days, worth $256k–$366k of team time at a £500/day fully-loaded rate. Nobody invoices you for it. Budget for it anyway. It is the line that derails delivery.
The second view of the timeline above shows the same roles against the eight phases, if you want to see when each is needed.
| Role | Commitment | What they need to do |
|---|---|---|
Executive sponsor CEO, COO or CIO | ~2 days/month Full programme duration | Champion the programme visibly at board level, approve the announcement and key communications, and remove blockers. When the CEO mentions AI in an all-hands and references their own use of it, that does more for adoption than fifty training sessions. Needed most at Excite and at every Checkpoint gate, where the go/no-go decision is theirs. |
Programme manager Your counterpart to the delivery partner's engagement manager | 12–13 months full-time | Day-to-day coordination, internal navigation and scheduling across the business. The most underestimated requirement on this list: it needs protected time, not a side-of-desk commitment. Heaviest through Define and Design, then steady through the build. |
Process owners One, one per domain you transform | ~1 day/week during Define | The people who own the process being reimagined, they know where the work goes, which exceptions matter, and what 'good' looks like. Their availability in Define is the single biggest predictor of whether the agents you build are the right ones. Not necessarily department heads; whoever owns the process end to end. |
IT / security lead Infrastructure and compliance | 3–4 weeks intensive, then ~1 day/month | AI platform configuration (Claude Enterprise, ChatGPT Enterprise or equivalent), SSO, security controls and data access. Concentrated in Define and Design, with a security review before the Checkpoint gate, deliberately before build spend is committed, not after. Light-touch from Build onwards. |
Champions est. 50–100 people | 1 day training, then ~2 hrs/week Heaviest at Deploy | Not teachers, but facilitators. Their job is to notice when a colleague is struggling with a task an agent can handle, guide them to the right setup, and follow up two days later. Recruit on curiosity and influence; technical skill is not a prerequisite. This is the group that decides whether what you built gets used. |
AI builder est. 1–2 people | Full-time from Design onwards | The one role on this list that is a genuine addition rather than redirected time. Designs human-and-agent workflows, builds the evals that tell you whether an agent is working, and owns the agents after handover. Standard HR job libraries do not have this role yet. You will likely define it from scratch or second a technically curious operator from inside the business. |
Champions, your most important internal asset
5–10% of your workforce, recruited from within. Not teachers, but facilitators. Their job is to notice when a colleague is struggling with a task an agent can handle, guide them to the right setup, and follow up two days later. Training takes a day; ongoing commitment is roughly two hours a week. Recruit on curiosity and influence, not technical skill. This is the group that decides whether what you built gets used, which is why change enablement starts in week one rather than after go-live.
Consider in-tool AI assistance to extend your champion network
A champion network is the engine of adoption, but it has a "who do I ask?" bottleneck. One fix is to layer AI assistance inside the tools people already use (Slack, Microsoft Teams, Google Chat, or a platform's own integrations) so day-to-day questions get answered in-channel rather than queueing for a person. That lightens the load on champions and extends their span. Worth asking each delivery partner you shortlist what they offer here.
AI Builder, the emerging internal role
As your programme matures from basic adoption into agent-powered workflows, you need someone who sits between IT and the business. Not a developer. Not a trainer. Someone who can translate business processes into the structured context agents need to do real work, and redesign workflows for a world where humans and agents share tasks.
- Connect agents to your data: set up secure integrations across legacy and modern systems so agents have the context they need
- Design human + agent workflows: map where agents take over, where humans step in, and who is accountable for what
- Manage access controls and monitoring: ensure agents operate within the right entitlements, and that you can see and audit what they do
- Build evals: create the tests that tell you whether your agents are doing what you intended
- Keep up with the architecture: the agent landscape is changing faster than any other area of enterprise technology. Someone needs to own this as their full-time job.
This role doesn't exist yet in standard HR job libraries. You will likely need to define it from scratch, or second a technically curious operator from within your business.
How the Programme Runs
What happens when, who is needed, and how much of it lands in year one
Programme timeline
Eight phases. Excite and Define happen once; everything from the Checkpoint gate onwards repeats per solution, which is why a focused first programme runs in months and a broad one runs in years.
Time runs in months. Bar height shows how heavy the work is that month, not just whether the workstream is engaged. Governance is a thin constant thread that spikes at every gate, change enablement starts light in week one and peaks at every deploy. That change enablement runs from the start is deliberate, and it is the single biggest difference between a programme that gets used and one that goes live and dies. Dashed agents land after year one. Fewer domains, not a faster build, is the lever that brings them forward.
External programme team
The roles included in the programme cost. The team scales to your organisation size.
| Role | Commitment | Focus |
|---|---|---|
Programme Director | Full-time for programme duration | Owns the executive sponsor relationship, AI Operating Model, and programme-level decisions. In early-stage programmes, the most senior person on the account. |
AI Platform Specialists (1–2) | 1–2 specialists, full-time from Design onwards | Each specialist owns a cluster of departments, building their AI projects, prompt templates, agents and skills, and custom workflows on your chosen platform. Scales with the number of departments in scope. |
Technical Lead | 3 weeks intensive through Define and Design, then advisory | AI platform configuration (Claude Enterprise, ChatGPT Enterprise, or equivalent), SSO, security controls, and data integrations. Heaviest through Define and Design, with a security review before the Checkpoint gate, and mostly done once the infrastructure is live. |
Change Lead | Full-time | Owns adoption: programme communications, department head briefings, resistance management, and monitoring uptake across the rollout. |
Enablement Leads (2) | 2 leads, full-time from Design onwards | Design and deliver foundation training; train, certify, and quality-control subcontracted facilitators. Set the quality bar for all delivery. |
Domain Coaches | 2–3 days/month per executive | 4–6 coaches, each matched by functional domain, a former CFO for the CFO, former CHRO for the CHRO. White-glove one-to-one coaching, not generic AI training. |
Facilitators | 3–5 people, full-time during rollout | Deliver foundation training sessions. Certified by Enablement Leads before delivery. Scaled to the rollout schedule. |
AI Platform Licensing
What you'll pay your AI platform provider, separate from professional services
Enterprise AI platforms broadly use a two-part billing model: a base seat charge for all users, plus consumption-based usage on top. The more your people use the platform, the higher the usage charges, but unlike traditional SaaS, you're not paying for unused seats at a premium rate. Self-serve plans are billed in USD; sales-assisted plans can accommodate other currencies. Figures here convert with the currency selector above for reference, but the source-of-truth pricing remains USD.
Cost by user type
Enterprise AI plans use a single unified seat type that covers chat, document collaboration, and developer tooling. The split below is a planning view of how usage varies by user behaviour (what you’ll spend in practice), not a description of any one vendor’s billing model. Pricing shown uses Claude Enterprise as the benchmark; ChatGPT Enterprise and Google Workspace AI fall in a comparable range.
These are starting points, not a claim about your business. People in scope comes from a generic staffing mix, which is wrong for anyone whose shape differs, so if you know the real numbers type them in. Nothing is capped.
| User type | People | Seat | Consumption | All-in monthly | Per year |
|---|---|---|---|---|---|
Everyday user Writing, research, summarising, analysis | 165 | $20–$30 | $30–$70 | $50–$100 | $59k–$139k |
Power user Heavy daily use, long documents, sustained analysis | 4 | $20–$30 | $400–$700 | $420–$730 | $19k–$34k |
Agentic developer Claude Code, agent building, agentic coding tools | 1 | $20–$30 | $1k–$3k | $1k–$3k | $12k–$30k |
Agent runtime Your deployed agents running, billed per agent, not per person | 8 agents | — | $250–$3k | $250–$3k | $24k–$240k |
| Your total | 170 licensed | — | — | $76–$247blended per user | $155k–$503k |
For your organisation
Enterprise pricing is negotiated directly with each platform vendor, none publish full public rate cards. Shortlist your platforms (Anthropic, OpenAI, Google) and get a quote from each before committing budget.
Ongoing Managed Services
The recurring cost line that gets underestimated, or missed from the budget entirely
The change programme builds the foundation. What comes next is less predictable but no less real: every department will surface ideas they want to act on, new starters will need onboarding into AI workflows, and the technology itself will keep evolving. Your internal IT team will be focused on what it should be, keeping infrastructure running, managing internal systems, maintaining the operational baseline. That work doesn't stop, and it leaves little bandwidth for ongoing AI development at the frontier.
Budget for ongoing external AI support. The specific shape will depend on your pace and ambition, but the organisations that sustain AI impact treat it as a capability to keep investing in, not a project to close.
What this covers
Ongoing AI support is delivered through a quota of structured service requests. Different providers call these “Expert Requests”, “Service Credits”, or retained-hours packages. Common examples include:
- Writing a prompt or template for a specific recurring task
- Updating an agent after a process, policy or pricing change
- Installing a connector to a system your agents need to reach
- Building a skill or automated workflow for one team’s process
- Writing an eval that tells you whether an agent is still doing its job
- A short training session as a new department comes on board
- Coaching your internal AI lead or champion network
Bigger pieces of work, a department audit or a roadmap refresh, draw on multiple requests rather than one, or are scoped as a project in their own right.
What your programme implies
At $1.6k–$3.3k per request, depending on who delivers it. A request is one of the pieces of work listed above, delivered and done. The thousandth takes the same work as the first, so expect a flat rate rather than a volume discount.
These ranges come from observed request volumes rather than a published rate card. Of the three lines, the per-head term carries the most uncertainty, because departmental demand depends on how far the agents reach into daily work, so treat the upper end for a large organisation as a planning band rather than a forecast.
The Business Case
Where hard returns come from, and how to measure them
The organisations seeing real AI returns are tracking growth they couldn't have achieved otherwise, not just hours saved. According to Wharton's 2025 enterprise AI research, 46% of enterprises now formally track AI profitability. The three value levers below are where those returns show up: growth first, then cost.
Better output, per process
Levers 2 and 3 below are efficiency: the same work, done for less. This one is different, and larger. A well-placed agent changes what a process produces: a higher win rate, a lower cost to serve, a better yield. That is why it matters which processes you pick, and it is the part of the business case only you can fill in.
Each domain starts at a different percentage, because a point of improvement on a full top line and a point on a narrow cost base are not the same claim. Revenue-side domains start low, anchored to the capacity uplift the research below supports; cost-side domains start higher, because the base is smaller and a pound of cost removed is a pound of profit. Organisations leaning into this report EBITDA improvements of 20% and up. Move the sliders and watch where your own assumptions land against that.
Every number in this lever is your assumption, not our estimate. We have no way of knowing what a process is worth inside your business. Use it to test whether the programme is worth doing at all, and to compare one domain against another.
You: 10 months – 2.4 years
17 of 20 successful transformations were cash positive inside two years. The faster ones focused on a few high-value domains; the slower ones went enterprise-wide.
You: 1.3×–3.0×
Annual incremental EBITDA divided by one-time investment. The average across the twenty was 3×, so £1 invested returned £3 of EBITDA a year.
Distributions from Rewired (Lamarre, Smaje & Zemmel, 2nd ed., 2026), Exhibit 2.1. Two-thirds of that sample delivered most of their impact from three business domains or fewer, which is the same finding behind the focus guidance higher up this page. Your own figures assume 50% adoption realisation in year one.
Sources: Wharton Human-AI Research & GBK Collective, Accountable Acceleration, October 2025 (budget benchmarks and ROI measurement). Deloitte EMEIA, AI ROI: The Paradox of Rising Investment and Elusive Returns, August–September 2025 (n=1,854, 14 EU/ME countries, payback benchmarks). Google Cloud, ROI of AI 2025 (n=3,466, 24 countries, first use-case ROI). OpenAI, State of Enterprise AI 2025 (productivity gains).
Go deeper: research and benchmarks
Every figure in this calculator is anchored to public research from Deloitte, Google Cloud, OpenAI, Wharton, EY, Barclays and others, grouped by what they support so you can verify any number before approving spend.
Every figure in this calculator is anchored to public research. References below are grouped by what they support, with sample sizes called out so you can judge weight. Forward this section to anyone who wants to verify the numbers before approving spend.
Payback and ROI timelines
- Deloitte EMEIA, AI ROI: The Paradox of Rising Investment and Elusive ReturnsAugust–September 2025 · n=1,854 senior executives · 14 EU/ME countries (incl. UK)Anchors the upper end of our payback range. Finds typical enterprise AI payback at 2–4 years for programme-wide P&L impact.
- Google Cloud, ROI of AI 2025n=3,466 senior leaders · 24 countriesAnchors the optimistic end. Finds 74% of executives see ROI on at least one use case within 12 months.
Productivity gains and revenue uplift
- OpenAI, State of Enterprise AI 20259,000 workers · ~100 enterprisesAnchors the 1–3% revenue uplift options in Lever 1. Finds 40–60 minutes/day saved per worker on average, equivalent to ~1.5–2.5% capacity uplift across the workforce.
Enterprise AI spending levels
- Menlo Ventures, 2025: The State of Generative AI in the EnterpriseDecember 2025Macro spend benchmark. Enterprise AI investment hit $37B in 2025, tripling year-on-year. Validates that the budget envelopes assumed by the calculator sit within the credible range of how enterprises are now spending.
- Wharton Human-AI Research & GBK Collective, Accountable Acceleration: Gen AI Fast-Tracks into the EnterpriseOctober 2025Anchors Lever 3 (vendor consolidation). Documents that enterprises are funding AI budgets by cutting outside services, up 7 percentage points year-on-year. Also the source for the 46% profitability-tracking figure.
- EY, AI Pulse Survey 2025n=500 US senior leadersProvides budget benchmark context. 88% of mid-to-large organisations now spend more than 5% of IT budget on AI.
- Barclays / Opinium, Business Prosperity Index, Q2 2025n=1,000 UK decision-makersUK-specific spend benchmark. Large UK firms (250+ employees) average £400k/year on AI; 68% plan to increase next year.
Implementation and change management
- MIT Media Lab, Project NANDA, The GenAI Divide: State of AI in Business 2025Jul 2025Anchors why change management is essential. Finds that 95% of generative AI pilots are failing to reach production. Used to justify the complexity multiplier when no dedicated change manager is in place.
- McKinsey & Company, The State of AI in 2025: Agents, Innovation, and TransformationNovember 2025Supports the broader change-management framing. Finds that workflow redesign and dedicated AI leadership, not tooling, are the strongest predictors of organisations capturing AI value.
- BCG, AI at Work 2025: Momentum Builds, but Gaps RemainJune 2025Reinforces the complexity multiplier on the employee side. Documents persistent gaps between employee AI usage and organisational AI value, pointing to training, governance, and change support as the deciding factors.
- Bain & Company, Executive Survey: AI Moves from Pilots to Production2025Supports the ongoing-programme model rather than one-shot deployment. Finds organisations reaching production AI value invest meaningfully in delivery and change capabilities, not just tooling.
Calculator assumptions reflect public research as of early 2026. Where multiple benchmarks point in the same direction, we have chosen the more conservative reading. Where research is split (e.g. payback timelines), we surface both as separate anchors rather than averaging.
A planning tool, not a proposal
The figures in this tool are indicative ranges based on typical programme parameters for organisations of your size. We haven't spoken to you, we haven't scoped your requirements, and nothing here constitutes a quote, from any delivery partner or AI platform vendor.
AI programme costs vary significantly based on your specific situation, what's already in place, and how ambitiously you want to move. The numbers here are a starting point for an internal conversation, not a number to put in a budget.
Share it with your buying group (CEO, CFO, CIO) before anyone has formed a strong view. It's most useful when it opens up the conversation, not closes it.
- Frame all six budget lines before vendor meetings
- Align your internal group on realistic scale and investment shape
- Prepare the questions that matter before scoping begins
- Identify which value lever is most material to your business case
When you're ready for actual numbers, speak to our team ↗
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APQC Process Classification Framework (PCF) is an open standard developed by APQC, a nonprofit that promotes benchmarking and best practices worldwide. The PCF is intended to facilitate organizational improvement through process management and benchmarking, regardless of industry, size, or geography. To download the full PCF or industry-specific versions of the PCF, as well as associated measures and benchmarking, please visit www.apqc.org/pcf.
Process domains follow the APQC Process Classification Framework (PCF) Cross-Industry, version 8.0. Programme shape and phase durations informed by Rewired (Lamarre, Smaje & Zemmel, 2nd ed., 2026) alongside Kowalah's own delivery methodology.
Indicative estimates based on typical programme parameters. Actual costs and timelines vary based on detailed scoping, organisational complexity, and your choice of AI platform and delivery partner. Professional services figures are exclusive of VAT. AI platform licensing figures use Claude Enterprise pricing as a benchmark (base seats plus consumption, billed in USD); ChatGPT Enterprise and Google Workspace AI sit in a comparable range, so get a real quote from each platform you shortlist. Managed services figures assume a post-programme ongoing retainer. Business case figures (Section 5) are illustrative ranges based on stated assumptions, not projections. Budget benchmarks referenced from: Wharton Human-AI Research & GBK Collective, Accountable Acceleration, October 2025.