Artificial intelligence is no longer arriving in the workplace. It is already there—quietly embedded in dashboards, routing systems, forecasts and decision-support tools. The question facing employers in 2026 is no longer whether to adopt AI, but whether their people know how to use it well.
For Jitendra Srivastava, chief executive of Triton Logistics & Maritime, an Abrao Group company, the next phase of the AI skills debate has little to do with specialised technical roles. Instead, it centres on judgement, context and operational understanding—capabilities that cannot be automated away.
“By 2026, AI will be part of everyday work, not a specialised function. What matters most is understanding when and where it should be applied,” Srivastava said. “In logistics, the people who understand the flow of cargo, the pace of disruptions, and the importance of timing will be the ones who use AI most effectively. Technology will be valuable only when paired with operational wisdom.”
That perspective reflects a growing tension across industries that have moved quickly to deploy AI tools but are still working out how to embed them into real-world decision-making. In logistics, the margin for error is narrow. Delays, misreads or poor assumptions can cascade through supply chains with immediate financial and reputational consequences.
As a result, Srivastava sees data quality—not algorithmic sophistication—as the real differentiator in the year ahead. “Data quality will be the core differentiator. In our industry, even small delays or inaccuracies can create real-world consequences,” he said. “The ability to design AI systems that reflect the nuances of real operations will be crucial.”
What organisations will need, he argued, are people who can translate messy business realities into technology frameworks—and then test those frameworks against experience. “We will need people who can convert business challenges into technology solutions and who are comfortable testing AI output against practical experience.”
The real gap is confidence, not capability
The most serious gap, however, may not show up on a skills inventory at all. Srivastava believes many organisations will have access to AI tools long before they develop the confidence to rely on them appropriately.
“The bigger gap won’t be technical, it will be understanding right,” he said. “Many organisations will have the tools but may not yet have the confidence to use them correctly.”
In logistics, that confidence is inseparable from judgement. “The most important capability will be judgment,” Srivastava added. “Someone must always be able to look at an AI recommendation and ask a simple question: does this align with what’s unfolding in the market?”
How quickly teams develop that confidence, he noted, depends largely on whether employees are treated as passive recipients of technology or active contributors to its design.
“Preparation improves when people feel included in the process,” he said. “I’ve seen teams adapt faster when they’re part of shaping the solution.”
He pointed to warehouse operations as a case in point. “Warehouse staff helping outline what an inventory system should monitor tend to embrace it more than teams who receive it pre-built,” Srivastava said. “When people feel heard, change becomes easier.”
This shift in how AI is used is also reshaping roles across organisations. Rather than eliminating jobs, Srivastava expects AI to redefine their purpose.
“Roles will not disappear, but their purpose will shift,” he said. “Analysts will evolve from preparing reports to validating insights. Supervisors may transition into guiding AI-enabled processes.”
Routine work will shrink, he believes, while responsibilities that require coordination and interpretation will expand. “In logistics, it will become less about managing movement and more about managing intelligence,” he said.
Why learning strategies must change in 2026
These changes have direct implications for learning and development strategies in 2026. Srivastava cautioned against generic AI training programmes that promise transformation but deliver little relevance.
“Development must be relevant to roles,” he said. “The training needed for a EXIM manager is not the same as what a data scientist requires.”
What works better, he argued, is learning that is tightly connected to daily work. “Short, focused learning that connects directly to daily problems is more effective than long theoretical programs,” he said. “Building talent from within—especially those who already understand the business, will be a strong advantage.”
At the leadership level, the challenge will be resisting the temptation to treat AI as a replacement for human thinking.
“Technology is most powerful when it supports human judgment,” Srivastava said. “AI can process information quickly, but it cannot understand context or intent.”
In logistics, he added, meaning often matters more than mechanics. “Knowing why something matters is often more important than knowing how it happens,” he said. Leaders, he believes, must position AI as a partner—not a proxy—for decision-making.
Ultimately, what will separate organisations that navigate the AI transition well from those that struggle will not be speed, but discipline.
“Success will come from steady progress, not rapid transitions,” Srivastava said.
Organisations that focus on clarity and process before chasing scale will extract more value from AI over time. “Measuring improvement in decision-making speed or operational accuracy will sustain momentum,” he said. “The most successful teams will be the ones that treat AI as an opportunity to rethink how people can work smarter and with greater purpose.”