Enterprise AI Strategy and Investment Portfolio Management Course – Barcelona, 14–25 Jun 2027

Enterprise AI Strategy and Investment Portfolio Management is a 2-week training course for strategy and corporate planning professionals, portfolio and investment managers, and each participant leaves with an Enterprise AI Investment Blueprint covering strategic intent, portfolio mix and board case. Strategy teams must now decide which AI bets to fund, scale or stop. The course is delivered by Jupiter Prime Training.

Why Enterprise AI Strategy and Investment Portfolio Management Matters Now

Three forces shape this subject. AI spending now competes for the same capital as plants, products and acquisitions. Investment committees expect AI bets to carry hurdle rates, stage gates and exit criteria. Capability gaps in data and talent limit execution pace. Organisations that manage AI as a balanced portfolio compound returns and learn quickly; those that fund projects one by one accumulate pilots, sunk costs and duplicated platforms. The course draws on ISO/IEC 42001 for AI management systems and the NIST AI Risk Management Framework (AI RMF) for portfolio risk.

Who Gains Most from Enterprise AI Strategy and Investment Portfolio Management

• Professionals who draft corporate strategy and long-range plans for executive committees
• Planning and budgeting specialists who allocate capital across competing initiatives
• Portfolio managers who track benefits, dependencies and risk across many initiatives
• Investment and corporate development professionals who assess partnerships and acquisitions
• Transformation office leads who sequence enterprise change and report progress to boards

A corporate strategy office gains most when its planning, portfolio and finance partners attend together and adopt one shared language for AI investment decisions.

Enterprise AI Strategy and Investment Portfolio Management Capabilities

Participants practise the whole cycle from setting enterprise AI ambition to defending a balanced investment portfolio in front of a board.

• Assess competitive position and AI readiness to set enterprise AI ambition — executive committee
• Calculate the size and timing of AI opportunities with option-based valuation — investment committee
• Design a balanced AI portfolio across core, adjacent and transformational bets — chief strategy officer
• Weigh build, buy, partner and acquire routes for AI capabilities — corporate development
• Specify stage gates, value tracking and stop rules for AI investments — portfolio boards
• Communicate a board-ready AI investment case with scenarios and risk appetite — directors and shareholders

Key Enterprise AI Strategy and Investment Portfolio Management Terms

• Enterprise AI strategy — organisation-wide choices about where AI creates advantage and how much to invest.
• AI investment portfolio — the full set of funded AI initiatives managed together for balance of risk, return and timing.
• Three horizons model — a way of balancing investments across core business, emerging adjacencies and transformational options.
• Real options valuation — valuing an investment as a series of choices to expand, pause or abandon as evidence arrives.
• Stage gate — a decision point where an initiative must show agreed evidence before receiving further funding.
• Value realisation tracking — measuring whether funded initiatives deliver the benefits promised in their investment case.

Enterprise AI Strategy and Investment Portfolio Management Day by Day

Day 1: AI as a Strategic Capital Question

• Enterprise AI Ambition Levels — Defend, Extend or Reinvent the Business
• Competitive AI Positioning Map — Where Rivals Are Investing
• AI Spend Baseline Audit — What the Organisation Already Funds
• Board and Investor Expectations — Discipline for AI Capital
• Strategy Office Role in AI — Mandate and Decision Rights

Day 2: Formulating Enterprise AI Strategy

• Strategic Intent Statement for AI — Linking Ambition to Corporate Goals
• Value Pool Mapping — Where AI Shifts Profit in the Industry
• Capability Gap Assessment — Data, Talent, Platforms and Partnerships
• Strategic Choice Cascade — Where to Play and How to Win With AI
• Strategy Hypothesis Testing — Evidence Needed Before Committing Funds

Day 3: Sizing AI Opportunities

• Top-Down and Bottom-Up Opportunity Sizing — Triangulating AI Value
• Unit Economics of AI Initiatives — Cost to Serve and Margin Effects
• Banking and Energy Case Studies — Contrasting AI Value Profiles
• Time-to-Value Curves — When Benefits Realistically Arrive
• Opportunity Long List Construction — Feeding the Portfolio Funnel

Day 4: Prioritising and Valuing AI Bets

• Multi-Criteria Prioritisation Matrix — Value, Feasibility and Strategic Fit
• Real Options Valuation — Pricing the Right to Scale Later
• Net Present Value With Uncertainty Ranges — Testing Hurdle Rates
• Dependency Mapping Between Initiatives — Shared Data and Platforms
• Kill Criteria Design — Agreeing When to Stop Funding

Day 5: Designing the Portfolio Mix

• Three Horizons Model for AI — Core, Adjacent and Transformational Bets
• Risk-Return Portfolio Balancing — Avoiding Concentration in One Domain
• Enabling Platform Investments — Funding Shared Data Foundations
• Capacity and Talent Constraints — Pacing the Portfolio Realistically
• Portfolio Heat Map — Visualising Balance for Executives

Day 6: Sourcing AI Capability

• Build, Buy, Partner or Acquire Decision Tree — Capability Sourcing Routes
• AI Vendor and Platform Due Diligence — Lock-In and Exit Terms
• Strategic Partnerships and Ecosystems — Sharing Risk and Data
• Acquisition Screening for AI Capabilities — Talent and Intellectual Property
• Total Cost of Ownership Modelling — Licences, Compute and Operations

Day 7: Scenario Planning and Risk Appetite

• Scenario Planning for AI Futures — Regulation, Talent and Technology Shifts
• Stress Testing the Portfolio — Downside Cases and Resilience
• AI Risk Appetite Statement — Limits for Portfolio Exposure
• NIST AI RMF Portfolio View — Aggregating Risk Across Initiatives
• ISO/IEC 42001 Management System Links — Controls Expected by Boards

Day 8: Portfolio Governance and Value Tracking

• AI Investment Committee Charter — Membership, Cadence and Authority
• Stage-Gate Funding Model — Releasing Capital Against Evidence
• Value Realisation Tracking Framework — Leading and Lagging Benefit Measures
• Portfolio Rebalancing Reviews — Reallocating From Weak to Strong Bets
• Benefits Ownership — Holding Business Units to Promised Returns

Day 9: Capability Roadmaps and Execution Pacing

• Enterprise AI Capability Roadmap — Sequencing Data, Talent and Platforms
• Operating Model Options for AI — Central, Federated or Hybrid Delivery
• Talent Investment Plan — Hiring, Reskilling and Partner Capacity
• Integration With Annual Planning — Embedding AI in Budget Cycles
• Progress Reporting to Boards — Portfolio Dashboards and Narratives

Day 10: Practising the Enterprise AI Investment Blueprint

• Strategy Formulation Sprint — Applying Intent, Value Pools and Choices
• Portfolio Construction Exercise — Applying Prioritisation and Horizons
• Board Investment Case Simulation — Defending Scenarios and Risk Appetite
• Peer Investment Committee Review — Challenging Stage Gates and Kill Criteria
• Enterprise AI Investment Blueprint — Final Version for Executive Approval

Applying Enterprise AI Strategy and Investment Portfolio Management After the Course

• Run an AI spend baseline audit and map current initiatives onto the three horizons heat map for the executive committee.
• Propose an AI investment committee charter with stage-gate criteria and a first quarterly portfolio rebalancing review.

Enterprise AI Strategy and Investment Portfolio Management Questions (FAQ)

What is enterprise AI strategy and investment portfolio management and why does it matter now?

Enterprise AI strategy and investment portfolio management is setting organisation-wide AI ambition and funding AI initiatives as a balanced, governed portfolio. It matters now because AI spending competes for capital, and boards expect the same discipline applied to other strategic investments.

Which roles gain most from enterprise AI strategy and investment portfolio management training?

Corporate strategy and planning professionals gain most, together with portfolio, investment and corporate development managers.

How does enterprise AI strategy and investment portfolio management differ from a general AI for business course?

It works at enterprise and capital-allocation level rather than function by function. A general AI for business course explores use cases in marketing, operations or finance; this enterprise AI strategy and investment portfolio management course teaches valuation, portfolio balance, stage gates and board cases.

What do you bring back from enterprise AI strategy and investment portfolio management training?

You bring back an investment blueprint for enterprise AI. It holds a strategic intent statement, a balanced portfolio, stage gates and a board-ready investment case.

Enterprise AI Strategy and Investment Portfolio Management: The Way Forward

The Enterprise AI Investment Blueprint first informs which AI initiatives receive funding in the next planning cycle and which are paused or stopped. Strategy offices, investment committees and finance partners use it to allocate capital and track benefits. After the first rebalancing review, the valuation assumptions and kill criteria deserve another look, since real benefit data shows which bets deserve more capital.