HFS Research and Rackspace survey links AI returns to accountability

Research from HFS Research, conducted with Rackspace, indicates that most enterprises have not reached the returns they anticipated from artificial intelligence spending. Based on responses from 502 senior AI decision makers, the study identifies fragmented responsibility as the central obstacle, spanning how deployed AI performs, what it costs, and how it is controlled.

The difficulty grows as organizations extend AI across several business functions. Companies have funded models, platforms, infrastructure, and services, yet frequently no single party runs those components together as one system. Only 33% of enterprises have met or exceeded the business case for their most significant AI program, and 96% report significant governance gaps.

Reported gains concentrate in efficiency. 58% of respondents cite workforce productivity improvements and 42% cite measurable cost reductions, compared with 28% citing better customer experience and 21% citing measurable revenue growth. Returns also differ by deployment stage, with 37% of single-function deployers and 56% of organizations with AI embedded in standard operations meeting or exceeding their business case.

To address this shortfall, HFS proposes a Services-as-Software™ (SaS) Operator, which may take the form of an internal team, a group of existing providers under one remit, or a managed services partner. This operator is accountable for day-to-day performance, economics, and governance of AI once it is live. It coordinates the technology and partners already in place, while the enterprise retains its business context, policy, decision rights, and legal accountability.

Survey responses also indicate demand for this arrangement. 61% prefer one accountable partner across AI models, platforms, and infrastructure, and 63% recognize the proposed operator role as meaningfully different from what they buy today. 73% say they would pay a premium over raw infrastructure for a fully managed AI service with governance accountability.

“Enterprise AI has an accountability problem that nobody in the industry wants to admit,” said Phil Fersht, Founder and CEO of HFS Research. “Turning AI investment into measurable business value requires clear operational accountability,” said Gajen Kandiah, CEO, Rackspace. Providers must demonstrate clear operational responsibilities and measurable outcomes to earn that premium.