When AI Becomes Part of the Investment Thesis

Every investment thesis is built on assumptions.

Historically, those assumptions have centred on market growth, customer demand, pricing power, competitive positioning and management execution. Commercial Evaluation and Commercial Due Diligence have evolved to test whether those assumptions are sufficiently credible to support an investment decision.

Increasingly, another assumption is appearing alongside them: that artificial intelligence will create measurable commercial value.

Across venture capital, private equity and public markets, AI is now routinely presented as a source of future value creation. Investment cases increasingly assume that AI will improve productivity, expand operating margins, automate workflows, accelerate software development, enhance customer service or increase sales efficiency. These expectations influence financial forecasts, valuation models and ultimately investment decisions.

As AI becomes embedded within investment theses, those assumptions deserve to be underwritten with the same discipline as every other driver of expected returns. In other words, they require commercial underwriting rather than commercial optimism.

This represents an important evolution in investment research. While traditional Commercial Evaluation and Commercial Due Diligence continue to assess the commercial fundamentals of a business, investors increasingly need to evaluate whether AI-enabled value creation assumptions are equally credible. The question is no longer only whether a company operates in an attractive market or possesses a compelling product. Increasingly, investors must also determine whether the AI-enabled improvements underpinning expected returns are commercially realistic.

AI Is Becoming Part of the Investment Thesis

Only a few years ago, AI was often discussed as a future opportunity or a differentiating product feature. Today it frequently appears as a core driver of value creation.

Private equity firms describe AI-enabled operational improvements across portfolio companies. Venture-backed businesses highlight automation as a path to faster scaling with fewer employees. Public companies discuss AI-driven productivity improvements during earnings calls. Strategic plans increasingly incorporate AI as a contributor to revenue growth, operating leverage and margin expansion.

This shift is increasingly visible across investment committees, fundraising presentations, annual reports and corporate strategy discussions. AI is no longer presented solely as a product feature or technological capability. It is increasingly positioned as a driver of future earnings, margin expansion and enterprise value. As these expectations become embedded within investment cases, their commercial credibility becomes directly relevant to investment decisions.

These developments are not surprising. AI has become capable of supporting a wide range of commercial activities, from software development and customer support to marketing, financial analysis and internal operations.

As a result, AI assumptions are increasingly becoming financial assumptions.

As these assumptions increasingly influence investment committee discussions, valuation models and expected returns, they warrant the same level of independent scrutiny as any other material investment assumption.

When an investment case projects lower operating costs because of automation or higher profitability through productivity improvements, those expectations directly influence projected cash flows, earnings and valuation. In other words, AI is no longer simply a technology discussion; it has become part of the economic case for investment.

Whether those benefits are ultimately achieved can materially influence investment returns.

Like any other commercial assumption, these expectations deserve careful scrutiny.

From Technology Assumptions to Financial Assumptions

Investment research has always distinguished between possibilities and probabilities.

A growing market does not automatically produce sustainable growth. A differentiated product does not guarantee customer adoption. A capable management team does not eliminate execution risk.

The same discipline should apply to AI.

Many investment cases implicitly assume that AI deployment will generate measurable commercial benefits. Productivity gains, cost reductions and operating leverage are often presented as expected outcomes rather than hypotheses requiring validation.

Realizing those outcomes depends on far more than access to AI technology. It requires appropriate data, business processes, organizational readiness, employee adoption, governance and successful execution. Weakness in any of these areas can materially reduce the commercial benefits expected within an investment case.

The commercial question is therefore not whether AI is capable of creating value in principle. It is whether the specific assumptions embedded within a particular investment case are sufficiently credible to support capital allocation.

A New Dimension of Investment Risk

As AI becomes increasingly embedded within investment theses, it also introduces new sources of execution risk.

Among the most common challenges are:

• productivity improvements that prove materially lower than expected

• AI initiatives that remain confined to pilot projects without achieving enterprise-wide adoption.

• weak or fragmented data foundations that limit practical deployment

• hidden implementation, integration or governance costs that reduce expected returns

• organizational resistance that slows adoption across business functions

• margin expansion assumptions that fail to translate into measurable financial performance

None of these risks suggest that AI lacks commercial value. Rather, they illustrate that the path from technological capability to realized economic benefit is rarely automatic.

Just as investors evaluate customer concentration, competitive dynamics or pricing resilience before underwriting future cash flows, AI-enabled value creation should also be assessed through a commercial lens.

Where Traditional Commercial Due Diligence Ends

Traditional Commercial Due Diligence remains essential.

Institutional investors continue to require rigorous analysis of market attractiveness, customer demand, competitive positioning, commercial scalability, pricing dynamics and management capability. These factors remain fundamental drivers of investment performance.

However, the increasing prominence of AI introduces an additional analytical dimension.

When future returns depend partly on assumptions about automation, productivity improvements or AI-enabled operating leverage, those assumptions deserve explicit evaluation alongside the broader commercial analysis.

This should not be viewed as replacing traditional Commercial Due Diligence. Rather, it represents a natural extension of established investment research disciplines.

Investment teams have always sought to understand whether projected outcomes are commercially achievable. AI-related assumptions increasingly warrant the same level of attention.

Commercial Underwriting of AI-Enabled Value Creation

In this context, commercial underwriting refers to the independent evaluation of whether the assumptions underpinning expected value creation are commercially credible, economically meaningful and realistically executable. Applied to AI, it means testing whether projected productivity improvements, operating leverage and margin expansion are likely to translate into sustainable financial performance.

Commercial underwriting does not attempt to determine whether an AI model is technically impressive.

Instead it asks whether that technology changes the economics of the business enough to justify the assumptions embedded within the investment case.

As AI becomes embedded within investment theses, investors will increasingly need to evaluate questions such as:

• Does the proposed AI use case meaningfully influence the company's economics?

• Are the expected productivity improvements measurable and commercially relevant?

• Can the organization realistically implement AI at the required scale?

• Is the business operationally prepared to support adoption?

• Will expected financial benefits outweigh implementation and ongoing operating costs?

• Are projected improvements likely to be durable rather than temporary?

These are not technology consulting questions.

They are investment questions.

Their purpose is not to determine which AI platform should be implemented or how systems should be integrated. Instead, they seek to assess whether the commercial assumptions supporting an investment case are sufficiently credible to justify capital deployment.

This distinction is increasingly important. As AI becomes more deeply integrated into corporate strategy, understanding the commercial credibility of AI-enabled value creation may become as important as evaluating market growth, customer demand or competitive advantage.

The Future of Investment Research

Commercial Due Diligence has continually evolved alongside changes in how businesses create value.

Globalization expanded the importance of supply chains. Digital transformation increased the significance of software and recurring revenue models. Platform businesses reshaped competitive dynamics and scalability.

Artificial intelligence represents another such evolution.

The challenge for investors is no longer understanding what AI can do. The technology is advancing rapidly, and examples of successful deployment continue to grow across industries.

The more important question is whether the expected commercial outcomes can realistically be achieved within the context of a specific investment.

As AI increasingly becomes part of the investment thesis rather than simply part of the product, investment research will need to evolve accordingly.

The role of investment research has never been to predict the future with certainty. It has always been to distinguish robust commercial assumptions from optimistic narratives. As AI becomes an increasingly important driver of investment returns, applying that discipline to AI-enabled value creation will become another essential part of independent investment research. AI will undoubtedly reshape how businesses create value. The responsibility of investment research is to determine which AI-enabled value creation assumptions deserve to be believed—and which do not.

Related Articles

For readers interested in the broader evolution of investment research and commercial evaluation, the following resources provide additional context:

• Commercial Evaluation Comes Before Commercial Due Diligence – Why commercial underwriting begins before formal due diligence?

• Commercial Due Diligence & Investment Research Library – Institutional research examples across Commercial Evaluation, Expanded Evaluation and Commercial Due Diligence.

• What Makes AI Infrastructure Valuable in Investment Research? – How AI supports research workflows and analytical productivity.

• AI Concierge for Investors – The role of AI in supporting investment professionals while preserving independent judgment.

• Investment Committee Briefs – Including Garba AI (Focused Screening), Databricks (Expanded Evaluation) and, upon publication, Palantir (Commercial Due Diligence).