Business
Infrastructure Challenges Stall Enterprise Adoption
Forrester research reveals critical disconnect as organizations chase AI innovation while core IT capabilities lag behind
Key takeaways
- Infrastructure trumps innovation: AI success depends more on mature IT foundations (governance, data quality, security) than on advanced models, yet most organizations are investing in the latter while neglecting the former.
- The pilot-to-production gap is widening: Weak controls and fragmented systems that remain hidden during small-scale experiments are causing failed deployments, cost overruns, and reputational damage as AI initiatives scale.
- Conventional security fails AI: Traditional access controls cannot address AI-specific threats like prompt manipulation and model drift, requiring new frameworks and continuous monitoring for safe deployment.
The artificial intelligence gold rush is colliding with an uncomfortable reality: most enterprises lack the foundational infrastructure needed to deploy AI safely and effectively at scale, according to new research from Forrester that challenges the industry’s “build first, worry later” approach.
In a stark assessment published in The CIO’s Guide To AI Readiness, the research firm warns that AI transformation initiatives are systematically outpacing the IT maturity required to support them, creating a widening gap that threatens to turn promising pilots into expensive failures.
“AI transformation is only as strong as the IT capabilities supporting it,” said Frederic Giron, VP and senior research director at Forrester, whose findings suggest that the determinant of AI success lies not in cutting-edge models but in decidedly unglamorous infrastructure work.
The Infrastructure Deficit
The report identifies a pattern playing out across industries: organizations rushing to adopt generative AI and other advanced capabilities while overlooking critical gaps in governance frameworks, data quality standards, architectural readiness, and operational discipline. These deficiencies remain hidden during small-scale experimentation but surface rapidly when systems move toward production deployment.
The consequences extend beyond technical hiccups. Weak controls amplify reputational risk, particularly in customer-facing applications or regulated sectors where accountability standards are non-negotiable. Failed proofs of concept, cost overruns, and operational disruptions are becoming common symptoms of what Forrester characterizes as a fundamental mismatch between ambition and readiness.
“Many firms overestimate the benefits of newer AI models while underestimating the work required to run them reliably in production,” the report states, shifting focus from model selection (the glamorous front end of AI) to the operational readiness that determines whether systems actually work.
Governance: Beyond Pilot Oversight
Forrester’s research places particular emphasis on governance, arguing that steering committees and pilot-stage oversight are insufficient for enterprise-scale deployment. Instead, the firm advocates for enterprise-wide discipline that connects AI initiatives to business strategy, risk tolerance, and operational capacity.
The recommended approach includes ongoing performance measurement, risk scorecards, escalation protocols, and transparent incident management. These structures are designed to ensure that AI systems remain aligned with organizational objectives rather than drifting into unmanaged risk territory. For regulated industries facing heightened compliance scrutiny, these governance practices also function as mechanisms for maintaining stakeholder trust when AI systems make consequential decisions.
Security Beyond Conventional Controls
Traditional security frameworks, the report argues, are ill-equipped for AI-specific threats. Prompt manipulation, model drift, and unsafe autonomous agent behavior represent attack vectors that conventional access controls and monitoring systems weren’t designed to address.
Forrester references its AEGIS framework as a model for governing AI-specific security, identity, and risk domains. The approach emphasizes continuous monitoring, policy-as-code implementation, identity controls tailored to AI agents, and real-time observability. This is particularly critical for organizations deploying customer-facing AI services where reliability directly impacts operations.
The Data Quality Imperative
AI systems, as Forrester notes, function as mirrors of the data used to build and operate them. The research calls for strengthened practices in data lineage tracking, metadata quality management, and role-based access control. These are foundational capabilities that many organizations treat as secondary concerns until data-related failures surface in production.
Platform modernization represents another pressure point. Technologies including lakehouses, vector databases, and knowledge graphs have gained prominence as organizations attempt to support generative AI use cases requiring internal information retrieval and context management. Yet many enterprises, particularly in the Asia Pacific region, continue operating siloed legacy systems that constrain their ability to scale AI responsibly across business units.
The Human Factor
Beyond technology and security, the report frames workforce readiness as essential for sustained AI adoption. CIOs require teams capable of cross-functional collaboration and adaptation as AI becomes embedded in daily workflows.
AI literacy levels and clearer delineation of human-AI collaboration roles emerge as factors influencing both adoption rates and resistance patterns. Organizations must plan for operational shifts, including exception handling procedures, accountability structures for AI-generated outcomes, and validation processes for staff reviewing AI outputs.
Resisting the Hype Cycle
“CIOs must resist the gravitational pull of AI hype and instead focus on the one factor that consistently determines AI success: the maturity of their IT foundations,” Giron stated, delivering what amounts to a direct challenge to the prevailing narrative that AI transformation begins with model selection.
Forrester’s framework positions AI readiness as the convergence of five capabilities: governance, security, data management, architecture modernization, and workforce planning. The research suggests that as AI initiatives transition from pilots to broader deployment, organizations will increasingly confront these foundational requirements, whether by choice or through the costly lessons of failed implementations.
The message represents a sobering counterpoint to the prevailing enthusiasm surrounding AI capabilities: the technology may be ready, but for many enterprises, the infrastructure isn’t.