Enterprise AI Value Planning Training Course: Portfolio, Capability and Change

Programme Summary

Enterprise AI value planning is the subject of this one-week training course for senior executives and heads of function at strategic level, ending in an AI value plan for one business unit. Many organisations run AI pilots without a shared view of where value lies or who owns the change. The programme moves from value mapping to portfolio choice, capability, governance and adoption, using cases from banking and health. Mars Training Centre UK delivers this programme on enterprise AI value planning to mixed-organisation cohorts.

The Business Case

AI spending often grows faster than the evidence that it pays back. Pilots multiply in separate functions and leaders cannot say which deserve funding next. A planned portfolio ties each initiative to a measurable outcome, a named owner and an exit point.

• Investment Focus: Improves as initiatives are ranked on value, feasibility and risk before funding.
• Delivery Pace: Rises as capability gaps and dependencies are found before launch.
• Risk Exposure: Falls as governance duties and escalation routes are set at portfolio level.

Principles and Evidence Base

• ISO/IEC 42001 AI Management System: Frames policy, roles, risk treatment and review, applied in Sessions 3 and 4.
• NIST AI RMF: Structures the govern, map, measure and manage functions for AI risk, applied in Sessions 2 and 4.
• Value Driver Logic: Traces each initiative to the revenue, cost or risk measure it moves, applied in Sessions 1 and 2.
• Adoption Readiness Logic: Tests whether people, process and incentives can absorb a change, applied in Sessions 4 and 5.

Intended Participants

• Executives who sponsor or approve AI investment on behalf of their organisation.
• Heads of function who must decide where AI is applied in their own area.
• Strategy and transformation leaders who build and sequence the change agenda.
• Finance and risk leaders who challenge business cases.
• Technology leaders who turn priorities into capability plans.

Typical settings include banks, hospitals, utilities, logistics operators and public bodies.

Learning Outcomes

1. Participants will be able to construct a value map that links AI opportunities to measurable business outcomes.
2. Participants will be able to prioritise a portfolio of initiatives using value, feasibility and risk criteria.
3. Participants will be able to diagnose capability gaps in data, talent, platforms and funding.
4. Participants will be able to evaluate governance arrangements against ISO/IEC 42001 and NIST AI RMF.
5. Participants will be able to justify a sequenced investment case to a board or executive committee.
6. Participants will be able to design an adoption plan that assigns owners, milestones and review points.

Programme Structure

Day 1: Enterprise AI Value Planning: Mapping Where Value Sits

Where could AI change the economics of this organisation, and how would anyone know?

• Value Chain Opportunity Map
• Outcome and Indicator Tree
• Use Case Long List Template
• Baseline and Benefit Logic Sheet
• Stakeholder Ownership Matrix

Day 2: Portfolio Choice and Investment Sequencing

Which initiatives are funded first, and which are stopped or deferred?

• Value and Feasibility Scoring Grid
• Risk Screen Based on NIST AI RMF Map Function
• Dependency and Sequencing Chart
• Stage-Gate Funding Rules
• Build, Buy or Partner Decision Table

Day 3: Capability and Operating Model

What data, talent, platforms and structures must exist before initiatives can scale?

• Data Readiness Assessment
• Talent and Skills Gap Profile
• Platform and Vendor Dependency Review
• Central and Federated Team Design
• Funding and Cost Allocation Model

Day 4: Governance, Risk and Adoption

Who decides, who is accountable and how does the organisation absorb the change?

• AI Management System Role Map from ISO/IEC 42001
• Risk Appetite Statement Drafting
• Board Reporting Pack Design
• Change Impact and Readiness Survey
• Resistance and Incentive Analysis

Day 5: AI Value Plan for One Business Unit

Do the earlier tools combine into one plan that an executive committee could approve?

• Applying the Session 1 Outcome and Indicator Tree
• Testing the Session 2 Scoring Grid
• Reviewing the Session 3 Capability Profile
• Checking the Session 4 Risk Appetite Statement
• Completing the AI Value Plan

Methodology

The programme uses a structured exercise on a prepared case of a mid-sized organisation with a long list of AI ideas, cost estimates, data constraints and risk notes. Participants score, sequence and challenge the portfolio in small groups, then apply the same tools to a business unit from their own work.

• Suggested activity: Ranking twelve initiatives and defending the funding cut-off.
• Suggested activity: Mapping capability gaps for the top three initiatives.
• Suggested activity: Presenting a short investment case to a simulated executive committee.

Capstone Output

Each participant completes an AI value plan for one business unit. It holds a value map, a ranked portfolio with funding stages, a capability gap list, a governance and risk appetite note and an adoption plan with owners and review points. The plan is used at work to brief the executive team and guide budget discussions.

Frequently Asked Questions

Is enterprise AI value planning training suitable for executives without a technical background?

Yes. Enterprise AI value planning is taught through business cases and decision tools. It assumes responsibility for budgets or strategy, but no programming experience.

Which references does enterprise AI value planning training rest on?

Enterprise AI value planning training rests on ISO/IEC 42001 for AI management systems and NIST AI RMF for risk.

How is the plan from enterprise AI value planning used at work?

You use the AI value plan to brief your executive team, support funding decisions, assign owners and set review points for each initiative in the portfolio.

How does enterprise AI value planning differ from AI governance training?

Enterprise AI value planning training decides where AI is worth investing and how to sequence it, while AI governance training concentrates on policies, controls and assurance for systems already in use.