Enterprise GenAI Strategy
Decide where generative AI actually pays: use case selection, build-versus-buy, platform architecture, cost modeling, and moving beyond stalled pilots.
Course Overview
Most enterprise generative AI programs have the same shape: a scattering of pilots, a lot of enthusiasm, and very little in production. The causes are rarely technical. Use cases get chosen because they demo well rather than because they carry measurable value, cost is discovered after launch rather than modeled before it, and nobody defined what success would look like precisely enough to declare it.
This course is a working session on the decisions that determine whether a GenAI program produces returns. You will apply a use case evaluation framework that weighs value against feasibility and risk, learn to spot the categories that reliably disappoint, and build the business case discipline that separates a pilot worth scaling from one worth killing quickly.
The second half covers architecture and economics at the portfolio level: build versus buy against a market that shifts every quarter, model selection and the case for routing across several, platform choices that avoid lock-in without paying for abstraction you never use, cost modeling that survives production traffic, and the organizational design decisions such as central platform team versus embedded engineers that quietly determine delivery speed.
Duration: 2 days|Delivery: onsite, virtual, or hybrid
Prerequisites
- Familiarity with generative AI capabilities at a conceptual level
- Experience with technology strategy or portfolio decisions
- No hands-on coding required
Who Should Attend
- Technology executives accountable for AI investment returns
- Enterprise architects designing the organization's AI platform
- Product leaders choosing which AI capabilities to build
- Transformation leads whose pilot portfolio has not reached production
Course Outline
- 1Why GenAI pilots stall: the pattern behind pilot purgatory
- 2Use case selection: scoring value, feasibility, and risk consistently
- 3Use case categories that reliably disappoint, and why they keep getting chosen
- 4Business case discipline: baselines, measurable outcomes, and kill criteria
- 5Build versus buy in a market that resets quarterly
- 6Model selection: capability, cost, latency, and data residency as one decision
- 7Multi-model routing: sending each request to the cheapest model that can handle it
- 8Platform architecture: gateways, abstraction layers, and avoiding lock-in without over-engineering
- 9Cost modeling: token economics, cost per outcome, and where budgets actually break
- 10Data strategy: what your proprietary data is worth and how to make it usable
- 11Organizational design: central platform, embedded engineers, or a hybrid
- 12Talent and capability building: hire, train, or partner
- 13Portfolio governance: staged funding, scaling criteria, and killing pilots quickly
- 14Roadmap construction: sequencing capability against readiness
Learning Outcomes
- Score and rank GenAI use cases with a consistent, defensible framework
- Recognize the use case patterns that consume budget without returning value
- Make build-versus-buy decisions that remain sound as the market shifts
- Model true cost per outcome rather than cost per token
- Design a platform architecture that limits lock-in at an acceptable cost
- Choose an operating model that matches your organization's structure
- Build a staged roadmap with explicit scaling and kill criteria
What You Will Build
- A scored and ranked use case portfolio for your organization
- A cost model covering your highest-priority use cases at production volume
- A platform architecture recommendation with an explicit lock-in assessment
- A staged roadmap with funding gates and kill criteria
Frequently Asked Questions
- Why do so many GenAI pilots never reach production?
- Most commonly because the use case was chosen for demonstrability rather than value, so there is no baseline to prove improvement against and no owner willing to fund scaling. The remaining failures are usually cost discovered too late, or data access and governance problems that were deferred rather than solved. The course covers surfacing all three before you build.
- Should we build our own AI platform or buy one?
- It depends on scale, differentiation, and how quickly you need capability. Buying gets you moving and is usually right below a certain volume; building pays off when spend is large enough that margin matters or when the platform itself is differentiating. The course provides the arithmetic and the strategic questions rather than a blanket answer.
- How do we estimate cost before building?
- By modeling cost per outcome rather than cost per token: tokens per interaction, interactions per user, retry and failure rates, and the human review that most designs still require. The course walks through building that model and stress-testing it against realistic adoption.
- Is this course technical?
- It is technically informed but does not require coding. It is aimed at people making investment, architecture, and organizational decisions. Technical leaders get the most out of it when paired with the business stakeholders who control funding.
- Can this be run as a working session for our leadership team?
- Yes, and that is the most common format. We use your actual use case candidates and constraints so the outputs are a real portfolio assessment and roadmap rather than a worked example.
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Contact us to schedule training for your team or inquire about upcoming sessions.