Why You Need a Portfolio of GenAI Projects (With Business Cases to Match!)

Generative AI (GenAI) has the ability to transform industries, unlock efficiencies, and drive innovation – no doubt. Are organisations struggling to move beyond scattered experiments or uncoordinated deployments? The key is to treat GenAI projects like any other strategic investment – by building a portfolio and backing each initiative with a solid business case, while appreciating the difference that GenAI brings and the rapid evolution of the GenAI market.

What Is It?

A GenAI portfolio is a structured collection of GenAI projects, managed like a capital investment portfolio. It ensures GenAI initiatives align with business strategy, avoid duplication, allow risk to be managed and maximise returns.

A Business Case is the justification for a project. It outlines the problem, the GenAI solution, expected benefits, costs, risks, and success metrics. A well-crafted business case ensures that only the most valuable and feasible GenAI projects receive funding and resources.

Without a portfolio approach, GenAI initiatives often become disconnected experiments with uncertain ROI. Managing GenAI investments systematically helps businesses prioritise high-impact projects and scale GenAI effectively. Having said this, GenAI is a rapidly evolving technology and we must leave room for managed experimentation within the portfolio.

What Does It Mean From a Business Perspective?

Taking a portfolio and business case approach to GenAI projects delivers tangible business advantages (just like any portfolio approach):

Unclaimed Cost Savings: Many businesses underutilise GenAI’s potential to automate processes and reduce expenses. A structured portfolio ensures cost-saving opportunities are identified and acted upon.
Time Optimisation: GenAI driven efficiency gains often go untracked. A portfolio approach ensures savings are measured and leveraged for business growth.
Strategic Alignment: AI projects should serve business goals, not just be tech experiments. A portfolio ensures every GenAI initiative maps to business priorities.
Better Risk Management: GenAI projects come with risks, some are new (bias, compliance, security). A disciplined approach ensures risks are identified, understood and mitigated before deployment.
Maximise ROI: By prioritising GenAI investments based on potential value, businesses can focus on the highest-impact initiatives, maximising returns while still leaving room for experimentation.
Avoiding Redundancy: Without coordination, multiple teams may develop similar AI solutions in silos. A portfolio approach prevents wasted effort and cost.

What Do I Do With It?

If you’re ready to move beyond ad-hoc GenAI experimentation, here are concrete next steps:

Inventory Potential GenAI Projects: Identify areas where AI can drive value. Engage business units to source ideas – drive your portfolio from a road map (work with your EAO).
Develop a Business Case Template: Standardise how GenAI projects are evaluated. Include problem definition, expected benefits, costs, risks, and ROI calculations.
Establish a Cross-Functional AI Review Team: Ensure AI initiatives receive input from business, IT, compliance, and finance. This is where you can deal with the differences that this new technology brings – leave room for experimentation and have early off-ramps to allow for resource re-allocation.
Prioritise AI Investments: Score projects based on strategic fit, feasibility (this is important given the rapid pace of change), and expected ROI. Drop low-impact ideas early but leave room for experimentation with early off-ramps. Work with your investment committee to understand changes that GenAI project assessment drives.
Integrate AI Into Financial Planning: Treat GenAI like any other investment. Secure funding through structured capital allocation.
Monitor AI Performance and Outcomes: Track ROI, user adoption, and efficiency gains. Adjust the portfolio based on real-world results. The difference that GenAI brings is that we will need to adopt new measures for success – for e.g. consider implications of model drift and operational effort to correct.
Refine and Scale: Start small, measure success, and scale up high-performing GenAI initiatives while phasing out underperforming ones. Understand whether the project approach is different – pilot phase, continuous learning and feedback and what that means for your Waterfall, Agile or Hybrid methodologies (work with your PMO).

By implementing a GenAI portfolio with structured business cases, your organisation can transform GenAI from a buzzword into a strategic advantage. The future of GenAI success isn’t about running more pilots – it’s about making smarter investments.

Are you building a GenAI portfolio in your business? Share your thoughts in the comments.

What Is It?

What Does It Mean From a Business Perspective?

What Do I Do With It?

Further Reading