Cognizant Research Shows Plug-and-Play AI is a Myth

• 11 Mar 2026
• TM Team
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 Cognizant released new research showing that companies pursuing AI adoption overwhelmingly prefer IT services firms - such as "AI Builder" firms, a new services model defined by designing and building custom, full stack AI solutions - to deliver real enterprise value from AI.

The research, based on a quantitative study of 600 AI decision makers and qualitative interviews with 38 senior executives, finds that organisations rank custom solutions and flexible engagement models as the most important factor when selecting an AI partner, ahead of pricing and time to value. Pricing and proven AI case studies remain important, but rank below capabilities that enable AI to be embedded directly into business operations and value chains.

Cognizant AI

At the same time, enterprises cite generic, off-the-shelf AI solutions as a leading reason to reject an AI provider, along with lack of industry-specific expertise, inability to integrate into existing technology stacks, and inadequate support and maintenance. According to the research, the top three challenges organisations face in enterprise AI adoption are regulatory and compliance concerns, difficulty demonstrating return on investment and lack of clear AI strategy and vision.

"AI success is not about deploying isolated models—it's about engineering intelligence into the enterprise with purpose-built solutions," said Ravi Kumar S, CEO of Cognizant. "The most trusted path to an AI future is working with an AI Builder—one that brings deep industry context, systems engineering expertise, and operational accountability. At Cognizant, we focus on building the bridge from AI experimentation to measurable enterprise value."

Key findings from the study include:

Enterprises face a "messy middle" in scaling AI: AI builders can create the bridge to enterprise value -- solving complex, real-world problems:

· 63% of enterprises report moderate-to-large gaps between their AI ambitions and current capabilities.

· The biggest barriers to scaling AI are operational and organisational:

1.   33% cite regulatory and compliance challenges

2.   31% struggle to demonstrate ROI

3.   27% report shortages in talent

4.   27% report inadequate data readiness

AI investment is long-term, not experimental: Enterprises are committing sustained capital to AI, signalling long-term infrastructure building rather than speculative investment:

· 84% of enterprises maintain formal AI budgets

· 91% expect AI budgets to grow in the next two years

· 50% anticipate double-digit increases in AI budgets over the next two years

· 52% are already investing $10M or more annually on AI initiatives

AI is augmenting human workforces, not replacing them: Enterprise leaders are not forecasting workforce collapse, they're forecasting redesign of workflows for human-AI collaboration.

· Across 13 enterprise functions, the highest expected level of full automation is only 20% (in sales)

· Even in customer service, where 76% of leaders expect workflows to become AI-dominant, only 9% believe they will be fully automated.

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In qualitative interviews conducted as part of the research, enterprise leaders said "out‑of‑the‑box" AI is inadequate; they want tailored solutions AI builders can develop and tune.

A Vice President in the UK banking sector shared, "A lot of vendors come in thinking that the off-the-shelf solutions they have would fit our needs, but often enough they find that that's not the case. And it takes them a number of years, more than they planned, and a lot of money, both from us … to get that software working. And these are not just AI software."

A US-based insurance industry CIO stated, "It depends on where I'm inserting this particular ingredient in our value. And so sometimes I want a builder and an engineer, sometimes I want an integrator, sometimes I want an activator. Because they're playing more of a coordinating function—a weaving, stitching-together  function."

Together, these research insights underscore a clear shift in enterprise expectations: from experimenting with AI tools to partnering with AI Builders that can design, build, integrate, and operate AI systems at scale— in alignment with client governance, security, and risk‑management frameworks and with lasting business impact.

These findings align with recent remarks by Babak Hodjat, Chief AI Officer at Cognizant, who noted that enterprises are far from being able to rely on AI "out of the box." In interviews with Fortune and Reuters, Hodjat emphasized that while agentic and generative AI systems are advancing rapidly, organizations still need significant help engineering, integrating, governing, and operating these systems in ways that support client safety, reliability and governance requirements within complex enterprise environments.

AI decision makers rated IT services firms like AI builders highest in their ability to assist with their AI adoption (ahead of SaaS providers, cloud providers, AI model companies, AI start-ups and management consultancies). The research also finds that IT services firms are trusted across the AI adoption lifecycle—especially in ongoing management of AI-enabled systems, but also in AI strategy, custom AI solution development, increasing organizational productivity and scaling AI across the enterprise. IT services firms have a 23% trust advantage over management consultancies in AI adoption. While management consultancies benefit from strong brand recognition, they are seen as less credible in hands-on AI implementation.