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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.

Other People are Reading

The Ashington-based business runs 34 shops across the North East as well as online betting services

Directors at Northumberland bookmaker Chisholm have described a “disappointing” year after falling to a loss amid increased costs and a need to update its gaming machines. The Ashington-based business, which runs 34 shops across the North East as well as online betting services, has published accounts for the year ended April 2025, citing “out of date” gaming machines which were seeing customers fall away in search of newer models, as one factor for the fall in finances.

Since then, it said investments had been made in newer machines, but turnover for the year fell from £29.99m to £27.1m, while the previous year’s operating profit of £542,773 was converted to a loss of £257,553. The company highlighted how increases in employers’ National Insurance contributions, introduced in April 2025, had impacted the business, adding that “further increases in taxes present an ongoing risk to the business”.

Directors said they would continue to focus on careful cost control, and that shops it operated would be “kept under constant review regarding future viability”.

A report within the accounts said: “The directors are disappointed with the results for the year, the poor performance being due to a number of factors. During the year became clear that the performance of the gaming machines in the shops was declining.

“The reason for the decline was found to be that over time the model of machines installed had become out of date and customers were leaving to use newer models in competing locations. Post year end all gaming machines have been replaced with newer models and performance has improved substantially.

“Media rights costs during 2024 and 2025 increased by a total of 30%. This, together with an above inflation increase in the National Minimum Wage increased costs across the business. Throughout the year it was noticed that many ordinary customers baulked at providing ‘know your customer’ information and were lost to the business.

“This is surprising as often the information requested is no more onerous than that required to register with a supermarket loyalty card. The hope is that these customers will return when they find KYC requests are now universally implemented among retail betting operators.

“Recruitment continues to be problematic for all high street businesses and the betting industry is no different. The focus of the business continues to be high volume, low stake turnover which is appropriate for the geographical locations that the business operates in.”

During the year, Chisholm made contributions totalling £100 to a national charity focused primarily on research and education programmes in order to combat problem gambling in the UK.

It added that changes to legislation and regulation continues to present a risk to the business, saying: “A balance must be in place between regulations intended to protect the small minority of individuals who experience problems with gambling and the rights of the ordinary gambler to conveniently place a bet via legal means.

“The company has a strong focus on encouraging responsible gambling by its customers and ensures all staff are aware of their responsibilities. We continue to be concerned that there is a risk that regulatory action designed to improve standards online goes further and unnecessarily impacts the retail industry.”

Some professional investors look at the Dow industrials with a bit of suspicion since the largest companies by market value aren’t given the biggest weight in the index, as is the case for other benchmarks like the S&P 500 and Nasdaq composite.

The top five stocks by market cap in the 30-stock Dow are Nvidia, Apple, Microsoft, Amazon and Walmart. They would represent 69% of the index if it were market-cap weighted.

As it stands under the Dow’s methodology of giving more weight to higher priced stocks, the top five are Goldman Sachs, Caterpillar, Microsoft, Home Depot and Amgen. They make up about 35% of the index.

Home Depot has a weighting of about 4.7% in the blue-chip average, according to Dow Jones Market Data. If the Dow were sorted by market cap, its weighting would be only 1.7%.

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