Agentic AI: Focus on Outcomes, Not Just Activity

What happened

Industry leaders Mike Olson from Basis Technologies and Emily Costello from PHD are advocating for a shift in focus regarding agentic AI, moving beyond its potential to merely automate tasks towards ensuring it delivers tangible, measurable business outcomes. Their perspective, shared at a recent industry event, highlights a growing concern that the current discourse around AI agents often prioritises their capacity for independent operation over their actual effectiveness in achieving strategic objectives. The discussion underscored the necessity for AI agents to demonstrate clear value by executing tasks efficiently and accurately, rather than simply appearing busy. This viewpoint suggests a critical need for businesses to implement robust evaluation frameworks that assess the performance of AI agents against predefined key performance indicators, ensuring their deployment genuinely contributes to organisational goals.

Why it matters

For business leaders, AI practitioners, and policymakers, this emphasis on demonstrable value from agentic AI is crucial for responsible AI implementation and strategic planning. Focusing on outcomes rather than just activity aligns with principles of AI governance, which demand accountability and transparent performance metrics for AI systems. Without clear evidence of efficacy, investments in agentic AI risk becoming speculative, potentially leading to resource misallocation and a erosion of trust in AI's transformative potential. From an ethical standpoint, ensuring AI agents "work" rather than just "look busy" is about more than just return on investment; it's about the responsible deployment of powerful technology. Ineffective or misdirected AI agents could lead to unintended consequences, perpetuate biases through inefficient processes, or undermine human oversight by creating an illusion of productivity. Therefore, a strategic approach requires rigorous validation and a commitment to integrating AI agents only where they can demonstrably enhance operations and contribute positively to business and societal value.

The Institute take

The industry's growing call for agentic AI to deliver concrete results, rather than just exhibit activity, is a vital recalibration. While the allure of autonomous AI agents is powerful, many leaders are overlooking the fundamental governance challenge: how do we define, measure, and assure "work" in an AI context?

Responsible AI leaders must move beyond the fascination with AI's autonomy to establish clear, auditable performance metrics and accountability frameworks for every agentic system. The true value of AI agents lies not in their ability to act independently, but in their verifiable contribution to strategic objectives, demanding a proactive approach to defining success and mitigating the risks of perceived, rather than actual, productivity.