What does a real AI programme cost?
A planning tool for the problem definition and solution exploration phase. Use it to frame the investment, align your internal group, and walk into vendor conversations with the right questions — before anyone has scoped anything.
AI Programme Cost Estimate
Four budget lines make up a complete AI programme. The sections below break each one down in detail — this card gives you the full picture at a glance.
Your Internal Team
The people you need in place — most drawn from existing headcount
Running an AI programme requires protected time from people who already have full-time jobs. These roles aren't new hires — with one exception — but they need real commitment, not a side-of-desk addition. The total time commitment across key roles and the Guide cohort is approximately ~70–100 person-days, equivalent to $110k–$162k of imputed internal team time at a £500/day fully-loaded rate. This isn't an external invoice — it's existing team time redirected — but if you don't budget for it, it's the line item that most often derails delivery.
| 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 programme announcement and key communications. Remove blockers. When the CEO mentions AI in an all-hands and references their own use of it, it does more for adoption than 50 training sessions. |
Programme Manager Internal counterpart to the programme director | 2–3 months full-time | Day-to-day coordination, internal navigation, and department scheduling. Your counterpart to the external Programme Director. Often the most underestimated resource requirement — this role needs protected time, not a side-of-desk commitment. |
IT / Security Lead Infrastructure and compliance | 3–4 weeks intensive, then ~1 day/month | Handles AI platform configuration (Claude Enterprise, ChatGPT Enterprise, or equivalent), SSO setup, security controls, and data integration priorities during Phase 1. Your delivery partner's programme tooling — for surfacing, scoring, and prioritising AI use cases — is also configured at this stage. Light-touch involvement after setup — roughly one day per month. |
Internal Comms Your existing comms team | ~2–3 days/week during rollout | Distributes programme communications through your established channels. The external programme team writes the content, messaging framework, and key announcements — your team handles distribution, localisation, and internal channel management. |
Department Heads est. 6–10 people | ~1 day each | Each receives a briefing before their team is activated and provides input into workflow mapping. No sustained involvement required — just availability for a half-day each, spaced across the programme. |
AI Guides — your most important internal asset
5–10% of your workforce, recruited from within. These are not teachers — they are facilitators. Their job is to notice when a colleague is struggling with a task where AI could help, guide them to the right setup, and follow up 48 hours later. Guide training takes 1 day. Ongoing commitment is roughly 2 hours per week during rollout. Recruited on curiosity and influence — technical skill is not a prerequisite.
Consider in-tool AI assistance to scale the Guide network
A 5–10% Guide cohort is the engine of adoption — but it has a "who do I ask?" bottleneck. Many programmes layer in AI assistance inside the tools people already use (Slack, Microsoft Teams, Google Chat, or via your platform's own integrations) so day-to-day questions get answered in-channel. This lightens the load on Guides and extends their span. Worth asking each shortlisted delivery partner 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 most HR job libraries. You will likely need to define it from scratch — or second a technically curious operator from within your business.
Change Enablement Programme
A structured, one-off programme to take your organisation from pre-AI to post-AI
Programme phases
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, typically the most senior person on the account. |
AI Platform Specialists (1–2) | 1–2 specialists, full-time Phases 1–3 | Each specialist owns a cluster of departments — building their AI projects, prompt templates, custom GPTs or skills, and custom workflows on your chosen platform. Scales with the number of departments in scope. |
Technical Lead | 3 weeks intensive (Phase 1), then advisory | AI platform configuration (Claude Enterprise, ChatGPT Enterprise, or equivalent), SSO, security controls, and data integrations. Heaviest involvement during Phase 1 setup — 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 during Phases 2–3 | 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. Most vendors bill self-serve 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 for most platforms.
Cost by user type
Most 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 actually 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.
| User type | Base seat | Typical consumption | All-in monthly | Who this covers |
|---|---|---|---|---|
| Chat / collaboration user | $20 | ~$23 | ~$43 | Most employees — daily AI use for writing, research, analysis |
| Developer / power user | $20 | ~$100 | ~$120 | Developers and AI Builders using agentic coding tools (Claude Code, Cursor, GitHub Copilot, equivalents) |
| Your blended estimate | $20 | $25–$45 | $45–$65 | Assumes 85% Chat users, 15% builders/power users |
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
A recurring cost line most organisations underestimate — or forget to budget for entirely
The change programme builds the foundation. What comes next is less predictable but just as 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 some form of 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 typically covers
Ongoing AI support is usually delivered through a quota of structured service requests — different providers call these "Expert Requests", "Service Credits", or simply retained-hours packages. Common examples include:
- Building a custom GPT, Claude skill, or automated workflow for a specific team or process
- Running targeted training sessions as new departments come on board
- On-site office hours — your people bring real problems, an expert works through them live
- Joining client-facing sessions to help co-design AI-powered services
- Regular coaching for your internal AI lead or Guide champion network
- Strategic reviews, department audits, and roadmap planning as the programme matures
Indicative cost by tier
Figures are indicative. Actual scope and cost depend on pace of deployment, team size, and how actively your organisation builds on the initial programme foundation.
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 typically show up: growth first, then cost.
Based on Levers 2 and 3 only by default — Lever 1 (revenue acceleration) varies too much by organisation, so the headline range stays grounded in hard cash savings. The faster end assumes high adoption and the upper estimates on both savings levers; the slower end assumes conservative adoption and the lower estimates.
Add your revenue assumptions in Lever 1 above to see a second payback figure that includes growth contribution.
Assumes 50% Year 1 adoption realisation (savings build through the year rather than landing on day one). For context: Deloitte EMEIA (n=1,854 senior executives, 2025) puts typical enterprise AI payback at 2–4 years for programme-wide P&L impact. Google Cloud ROI of AI 2025 (n=3,466) finds 74% of executives see ROI on at least one use case within 12 months.
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 actually 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 the four 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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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 — 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.