Enterprises shun plug-and-play AI for tailored builds

Cognizant has published research arguing that plug-and-play artificial intelligence products fail to meet most enterprise needs. Instead, companies are turning to IT services firms to design and build tailored systems.

The study surveyed 600 AI decision makers and interviewed 38 senior executives. It found that buyers prioritise custom solutions and flexible engagement models when choosing an AI partner-ranking those ahead of pricing and speed of delivery.

Organisations still value competitive pricing and proven AI case studies, but the research suggests these now sit behind demands for work that embeds AI into business operations and value chains.

Partner selection

The findings point to a market focused on integration work and ongoing operational support rather than standalone tools. Off-the-shelf AI offerings were a leading reason to reject a provider, alongside weak industry expertise, limited ability to integrate with existing technology stacks, and insufficient support and maintenance.

Enterprises also reported persistent obstacles to adoption. Regulatory and compliance concerns ranked as the top challenge, followed closely by difficulty demonstrating return on investment and a lack of clear AI strategy and vision.

Cognizant described these pressures as a gap between aspiration and execution, particularly as companies move from pilots to scaled use. "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.

Kumar said a services-led approach can move organisations from experimentation to production systems. "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," he said.

The "messy middle"

The research describes a "messy middle" in which companies struggle to scale AI across functions, data environments, and operating models. It reported that 63% of enterprises see moderate-to-large gaps between their AI ambitions and current capabilities.

The biggest barriers were operational and organisational. Regulatory and compliance challenges were cited by 33% of respondents, while 31% said they struggle to demonstrate ROI. Talent shortages and inadequate data readiness were each cited by 27%.

The study also suggests enterprise spending is shifting from experimentation to longer-term investment. It found that 84% of enterprises have formal AI budgets, 91% expect those budgets to grow over the next two years, and 50% anticipate double-digit increases.

More than half of respondents said they already invest USD $10 million or more a year in AI initiatives. The research framed this as evidence of sustained infrastructure build-out rather than short-term projects.

Workforce impact

The study also examined AI and workforce planning, reporting that executives expect workflow redesign and new forms of human-AI collaboration rather than widespread job replacement.

Across 13 enterprise functions, respondents said the highest expected level of full automation was 20%, associated with sales. Even in customer service-where 76% of leaders expect workflows to become AI-dominant-only 9% predicted full automation.

Qualitative interviews described the costs and delays that can arise when vendors try to retrofit generic software into complex environments.

One interviewee from a UK bank described a mismatch between vendor assumptions and enterprise requirements.

"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 those software working. And these are not just AI software," said a Vice President in the UK banking sector.

Another interviewee said enterprises may need different roles from partners depending on where AI sits in a process.

"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," said a US-based insurance industry CIO.

Services advantage

In the survey, AI decision makers rated IT services firms highest for helping with AI adoption. They ranked them ahead of software-as-a-service providers, cloud providers, AI model companies, AI startups, and management consultancies.

The research also suggests enterprises see services firms as relevant throughout the lifecycle, including AI strategy, custom development, and scaling across the organisation. Ongoing management of AI-enabled systems was highlighted as a particular strength.

The study found that IT services firms had a 23% trust advantage over management consultancies in AI adoption. It argued that consultancies benefit from brand recognition, but many buyers see them as less credible in hands-on AI implementation.

Cognizant also linked the findings to comments by Babak Hodjat, its Chief AI Officer, about the limits of relying on AI "out of the box" in complex enterprise environments.

Kumar positioned Cognizant within the report's emerging "AI Builder" category. "At Cognizant, we focus on building the bridge from AI experimentation to measurable enterprise value," he said.