Enterprise leaders say the future of AI won’t be delivered through plug-and-play tools. Instead, they want partners capable of designing and engineering custom AI systems that integrate directly into their business operations.
That’s the central takeaway from new research released by Cognizant, which argues that companies are increasingly turning to what it calls “AI Builders”—IT services firms that design, build, integrate, and operate full-stack AI solutions.
The study, based on a survey of 600 AI decision-makers and interviews with 38 senior executives, suggests that organizations are moving beyond experimentation with generative AI and into the far more complex challenge of embedding AI across enterprise workflows. And when it comes to making that leap, companies appear to trust IT services firms more than software vendors or consultancies.
According to the report, enterprises prioritize custom solutions and flexible engagement models when choosing an AI partner—ranking those factors ahead of pricing and even speed to value.
In other words: enterprises are less concerned with quick deployments and more focused on building AI that actually works inside their business.
The “Messy Middle” of Enterprise AI
Despite massive hype around AI, most companies remain stuck in what Cognizant calls the “messy middle” of adoption.
While organizations have ambitious AI strategies, turning those ambitions into operational systems has proved difficult. Nearly two-thirds (63%) of enterprises report moderate-to-large gaps between their AI ambitions and their current capabilities.
The biggest obstacles are not technical breakthroughs in models—but organizational and operational challenges:
- 33% cite regulatory and compliance concerns
- 31% struggle to demonstrate return on investment
- 27% report shortages of skilled talent
- 27% say their data is not ready for AI deployment
These findings reflect a growing realization across industries: deploying a model is easy; embedding it into production systems—while meeting regulatory, security, and governance requirements—is far harder.
Ravi Kumar S, CEO of Cognizant, framed the challenge bluntly.
“AI success is not about deploying isolated models—it’s about engineering intelligence into the enterprise with purpose-built solutions,” he said in a statement accompanying the research.
That shift is pushing enterprises toward service providers capable of integrating AI deeply into their infrastructure, rather than selling standalone software tools.
Why Off-the-Shelf AI Falls Short
The study highlights a growing backlash against generic AI solutions.
Among the most common reasons enterprises reject potential AI vendors:
- Generic, off-the-shelf AI offerings
- Lack of industry-specific expertise
- Difficulty integrating with existing technology stacks
- Insufficient support and long-term maintenance
Executives interviewed in the study emphasized that most enterprise use cases require heavy customization—something pre-packaged AI tools often struggle to deliver.
A banking executive in the UK described the mismatch between vendor promises and enterprise reality.
“A lot of vendors come in thinking their off-the-shelf solutions will fit our needs,” the executive said. “But often they find that’s not the case—and it takes years and a lot of money to get those systems working.”
That experience reflects a broader trend across enterprise technology: organizations increasingly expect vendors to adapt to their operational complexity rather than forcing standardized software into unique business environments.
AI Budgets Are Growing—Fast
Even as companies wrestle with adoption challenges, AI spending is rising sharply.
The study shows enterprises are treating AI less like an experiment and more like a foundational technology investment.
Key spending trends include:
- 84% of organizations maintain formal AI budgets
- 91% expect AI budgets to grow over the next two years
- Half anticipate double-digit increases in AI spending
- 52% already spend $10 million or more annually on AI initiatives
These figures suggest AI is entering the same category as cloud infrastructure or cybersecurity—long-term strategic investments rather than short-term innovation projects.
The spending trajectory also aligns with broader industry forecasts from analysts who predict enterprise AI spending will continue accelerating as companies shift from pilots to production deployments.
AI Isn’t Replacing Workers—Yet
Another notable finding challenges a common narrative around AI and job displacement.
Enterprise leaders aren’t predicting mass automation. Instead, they expect AI to reshape workflows and augment employees.
Across 13 enterprise functions studied, the highest projected level of full automation is just 20%—in sales.
Even customer service, often considered the most automation-ready domain, shows a similar pattern. While 76% of leaders expect AI to dominate workflows there, only 9% believe those roles will become fully automated.
That suggests companies are leaning toward human-AI collaboration rather than workforce replacement.
In practice, that could mean AI systems handling repetitive tasks while employees focus on higher-value work—an approach that many enterprise AI deployments already follow.
Why IT Services Firms Are Winning Trust
Perhaps the most revealing insight from the research is who enterprises trust to deliver AI transformation.
AI decision-makers ranked IT services firms—Cognizant’s “AI Builders”—highest in their ability to support AI adoption. These firms scored ahead of:
- SaaS providers
- Cloud platforms
- AI model companies
- AI startups
- Management consultancies
According to the study, IT services firms hold a 23% trust advantage over management consultancies when it comes to AI adoption.
Consultancies benefit from strong brand recognition, but they are often viewed as less credible in hands-on implementation.
That distinction mirrors a long-standing dynamic in enterprise IT: strategy firms may define roadmaps, but systems integrators frequently deliver the actual technology.
For AI deployments—where integration with legacy systems, data pipelines, compliance frameworks, and operational processes is critical—the ability to engineer systems may matter more than high-level advisory services.
The Rise of the “AI Builder” Model
Cognizant’s report positions the “AI Builder” model as the next evolution of enterprise technology services.
Rather than selling discrete tools, these providers aim to design and operate complete AI systems—from strategy and development to integration, governance, and ongoing management.
The concept reflects a broader shift in the AI ecosystem.
While foundation models and generative AI platforms continue advancing rapidly, enterprises still need extensive engineering work to adapt those technologies for real-world environments.
Babak Hodjat, Cognizant’s Chief AI Officer, has echoed that view in recent interviews, noting that enterprises remain far from being able to rely on AI “out of the box.”
Instead, organizations must address complex issues around reliability, safety, governance, and integration before AI systems can operate at scale.
The Bigger Industry Shift
Taken together, the findings suggest the AI market is entering a new phase.
The first wave of enterprise AI focused on experimentation—pilots, proof-of-concepts, and early generative AI deployments.
The next wave will likely revolve around large-scale implementation.
And that shift could reshape the competitive landscape of the AI industry. Model providers and AI startups may drive innovation, but services firms capable of integrating those technologies into enterprise systems may capture a growing share of spending.
In other words, the biggest winners in enterprise AI may not be the companies building models—but the ones building everything around them.
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