For much of the last era of technology innovation, firsthand knowledge and analytical intelligence moved apart. The people closest to the front line understood the customer, while increasingly sophisticated tools concentrated intelligence in specialist and supervisory layers.
A teller at a bank or a nurse in a hospital worked most closely with the customer or patient, but the insights built on those interactions accrued elsewhere. An analyst in the corporate back office would use that knowledge to identify an opportunity, assess risk or make a recommendation. Over time, we created pockets of analytical value that the people on the front line could not put to use.
That structure made sense when advanced tools and expertise were expensive to distribute and dependent on specialized systems. Easily accessible through natural language, AI can now bring firsthand knowledge and analytical intelligence back together, putting advanced reasoning and decision support directly into the hands of front-line workers and allowing their roles to shift toward greater judgment and accountability.
Equally important, when the people who already understand the customer or patient can act on these insights in the moment, decisions can be made faster, problems can be solved closer to where they occur and new sources of growth can emerge, expanding the value business itself can create.
Access to greater capability does not automatically make the role more valuable, however. Value follows control: who has the authority to act on that intelligence, who is accountable for the outcome and who shares in the economic gain. In a business, much of that control is embedded in how work is designed. Put AI behind the worker but leave the same narrow responsibilities, approval paths and escalation rules in place, and the worker may be more informed without being able to contribute more.
The real opportunity for business, therefore, is to reshape roles around what that greater capability now makes possible, in a way that both the business and the worker benefit.
Routine information gathering, analysis and execution can shift to AI, while human responsibilities move toward judgment, accountability and ownership of outcomes. Going back to the example of the bank teller, in a redesigned role, AI could help gather a customer’s broader financial context and potential needs in real time while the teller would have the authority to offer a rate, within a set band, or restructure a payment schedule with performance measured based on the quality of the customer outcome rather than the number of transactions completed.
Similarly, for the nurse, AI could bring together the patient history, surface deeper risk signals and make relevant clinical information available at the bedside while the nurse, equipped with these insights, could respond sooner within established protocols and answer for the result.
Placing knowledge and analytical tools in the hands of frontline workers has implications for how organizations structure and develop their workforce, as well. If AI moves capability downward, I believe organizational pyramids can become broader. More people can become productive sooner, and the path to expertise can shorten. One of our research studies looked at 18,000 tasks across nearly 1,000 occupations and found that 93% of jobs have some degree of exposure to AI. The tasks underneath jobs are changing, and the practical question is how quickly we can help people move into the new task mix. That is why learning must become part of the AI infrastructure stack itself.
At Cognizant, we are testing this thesis directly. This year, we are hiring over 20,000 graduates at the bottom of the pyramid, as well as repurposing existing talent and building AI capability into the workforce. The goal is to expand what people can contribute and redesign work around that broader potential as the technology advances.
This is the distinction I’d make: If AI simply helps someone perform the same work faster, the business captures an efficiency gain. But redesigning work around greater human capability can change the economics of the role more fundamentally, creating lasting value through better decisions, stronger relationships, and greater capacity for growth.
That is the outcome worth aiming for. AI can make businesses more valuable by making people more capable, and workers can share more fully in the gains they help generate. Done well, greater capability at the front lines can produce greater business value, higher wages and ultimately broader shared prosperity.
About the author
Ravi Kumar S
Ravi Kumar S is the CEO of Cognizant and Chairman of the U.S. Chamber's AI Working Group
