For the past two years, executives have asked the same question: Which
AI platform should we choose?
According to Niranjan Krishnan, head of AI solutions for
FPT Americas, that’s now the wrong question.
“The technology is no longer the constraint,” Krishnan explained. “Implementation is.”
That shift is creating an entirely new competitive advantage. As companies race to adopt generative AI, many are discovering that access to powerful models is no longer the differentiator. The organizations pulling ahead are the ones with the expertise to integrate AI into existing workflows, connect fragmented systems, and redesign how work gets done.
It’s why the next AI talent war may not be for prompt engineers. It will be for AI architects and orchestrators.
Companies don’t have an AI problem. They have an execution problem.
Just a few years ago, organizations struggled to access advanced AI technology. Today, leading models are widely available through companies such as OpenAI,
Anthropic, Google, and Amazon.
Yet implementation remains elusive.
“Ideas are a dime a dozen. LLMs are pennies a prompt. Execution is still priceless,” Krishnan said.
Successful AI deployments require far more than selecting a model. Organizations need expertise in data architecture, APIs, workflow engineering, knowledge engineering, governance, and security. All working together around a clearly defined business objective.
Too many organizations begin with the tool instead of the business problem.
“The companies seeing the greatest results are solving meaningful business challenges first,” Krishnan stated. “AI becomes the capability, not the strategy.”
That observation reflects what many organizations are experiencing. According to a recent
McKinsey report, while AI adoption continues to grow, relatively few organizations have scaled AI across the enterprise in a way that consistently delivers measurable business value.
Why companies are increasingly outsourcing AI talent
As implementation complexity grows, organizations are looking outside their walls for help.
Major technology companies have noticed.
Microsoft recently announced a $2.5 billion investment to expand AI infrastructure and implementation capabilities, reflecting a broader industry shift toward helping organizations deploy AI, not just purchase it.
Krishnan said many organizations simply don’t have enough experienced professionals who understand both AI technology and business operations.
Instead of trying to hire every specialized role internally, companies are increasingly partnering with external experts who can accelerate implementation while transferring knowledge to internal teams.
“It’s about capacity as much as capability,” he explained.
The most valuable AI role doesn’t exist in many companies yet
When asked which AI skill will become most valuable over the next several years, Krishnan pointed to a role many organizations have yet to define.
Not prompt engineers. Not software developers. AI architects who understand business. These professionals connect data, models, APIs, governance, workflows, and business strategy into a unified operating model.
“The real opportunity is finding experts who can connect and orchestrate all of those pieces to business outcomes,” Krishnan said. “People who are part-architect, part-engineer, and part operator.”
In many ways, he compared today’s AI transformation to the early days of enterprise computing.
Buying computers did not transform businesses overnight. Organizations had to redesign processes around the technology before
productivity gains emerged,
Krishnan believes AI will follow the same pattern.
“The companies that win won’t simply give employees AI tools,” he explained. “They’ll rebuild the operational muscle to complement their new digital brain. In short, they’ll redesign how work gets done.”
For CEOs, CHROs, and technology leaders, that may be the most important AI lesson of all. The competitive advantage isn’t choosing the best model. It’s building the capability to implement it.
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