Forrester Analyst Brian Hopkins: The 'Trust Tax' of AI Budgets and the Three Stages of Return
Forrester analyst Brian Hopkins points out that almost all growth in AI spending comes from infrastructure and computing power, while the costs for testing, guardrails, governance, and identity verification introduced by agentic AI constitute an unquantified "trust tax." He argues that if enterprises measure AI returns solely by productivity, they risk missing two larger value stages—engagement and transformation—and the full financial impact of this strategic divergence likely will not become visible until 2028 to 2029.
In a Q&A session, Forrester analyst Brian Hopkins proposed an explanatory framework for why enterprise AI budgets continue to swell while returns remain ambiguous. Forrester forecasts that AI will account for approximately 20% of business technology spending in 2027, but Hopkins emphasized that this figure includes infrastructure investments by technology vendors; it does not mean that the average enterprise will devote 20% of its own IT budget to AI.
Unpredictable costs are one of the core issues. Traditional software procurement allows organizations to estimate annual expenses and budget accordingly, but generative AI has introduced billing models based on tokens, usage charges, and credit limits, while agent costs fluctuate with task complexity and required computing power. Hopkins further notes that agentic AI brings an additional hidden expense: companies often expect an agent to directly automate a process, but in reality, they must also test and red-team it, add guardrails and monitoring, maintain a centralized registry, conduct governance reviews, and verify its identity when it interacts with customers or partners. He refers to these extra burdens as the "trust tax" and acknowledges that the industry is still trying to determine how much organizations should expect to spend.
Regarding return measurement, Hopkins distinguishes three time horizons: productivity (short-term), engagement (medium-term), and transformation (long-term). Productivity is the easiest to quantify—for example, whether AI shortens the time employees spend writing customer emails or accelerates ticket resolution—but its impact on the profit and loss statement ranks low. Engagement involves improving how customers and employees interact with the business, such as helping customers obtain answers and complete transactions more easily, thereby increasing transaction frequency and satisfaction. Transformation means redesigning business processes, accelerating product delivery, creating new products, or entering markets ahead of competitors; it carries higher risk but can establish a lasting competitive advantage. Hopkins believes that enterprises focusing only on productivity risk missing the greater opportunities in engagement and transformation, and capturing these benefits requires cross-departmental coordination, shared priorities, and aligned goals.