Five AI mistakes companies can't afford to make

Artificial intelligence has become the centrepiece of boardroom conversations. Companies are rolling out copilots, AI assistants and autonomous agents at an unprecedented pace, hoping they will transform productivity and competitiveness. But according to Satish Viswanathan, former Managing Director at Accenture and former Global Data & AI Lead for the Travel & Hospitality business, many organisations are making a fundamental mistake. They are deploying AI into yesterday's ways of working instead of redesigning how work itself gets done.

"The biggest mistake companies and employees are making is that they are doing more AI without redesigning the core cognition of the enterprise," he tells Business Today.

In his view, AI is not simply another technology implementation. It changes who thinks, who acts, who reviews, who learns and ultimately who remains accountable. Unless organisations redesign those relationships, AI risks becoming an additional layer of work instead of a source of competitive advantage.

Mistake 1: Confusing Productivity with Business Value

Many companies mistake faster tasks for better business performance. But faster work does not automatically translate into better business outcomes.

Viswanathan argues that an employee producing a report in 20 minutes instead of three hours creates value only if that saved time is redirected towards higher-value work, bottlenecks elsewhere in the process are removed, and the final business outcome improves. Faster work at one point in a process does not automatically produce a better business result.

Without redesigning workflows, faster tasks don't necessarily create better business outcomes.

Mistake 2: Automating Work Without Redesigning Review

AI may generate content instantly, but every output still needs to be checked for accuracy, context and completeness. According to Viswanathan, many organisations have automated execution while leaving verification almost entirely manual. As a result, the work doesn't disappear. It simply shifts from producing content to reviewing, correcting, documenting and governing AI-generated outputs. Without redesigning review processes, much of AI's promised productivity can be lost.

Mistake 3: Removing Work That Builds Future Leaders  

This may be AI's least discussed long-term risk. Companies focus on removing work without protecting the development of judgment. Junior professionals traditionally learn through research, drafting, analysis, repetition and exposure to mistakes.  If AI performs all those activities, companies may unintentionally eliminate the apprenticeship through which future experts, managers and leaders are developed. In becoming more efficient today, organisations could weaken the human judgment they will depend on tomorrow.

Mistake 4: Losing Sight of Accountability  

AI introduces multiple actors into a single decision. A model may generate a recommendation, an AI agent may execute it, a manager may approve it and a technology vendor may provide the underlying system. But when something goes wrong, accountability cannot remain distributed across the technology chain. Viswanathan argues that every AI-enabled workflow must clearly define who has the authority to intervene, who reviews AI-generated work and, ultimately, who owns the consequences.

Mistake 5: Believing That Learning AI Tools Is Enough  

Employees are making an equally important mistake.Some focus on protecting the tasks they perform today. Others believe that learning a few AI tools will secure their careers.

Neither approach, Viswanathan argues, is sufficient.

As AI makes routine execution increasingly abundant, professionals need to rethink where their value will come from. Their advantage will lie less in performing repetitive tasks and more in exercising judgment, solving complex problems, making decisions and working effectively alongside intelligent systems.

The Real AI Transition

For Viswanathan, organisations are framing AI as an automation journey when it is actually a cognitive and organisational transformation.

Instead of asking only what AI can automate, leaders need to rethink where AI should act, where human judgment must remain central, who reviews machine-generated work, how productivity gains translate into business value, and who ultimately remains accountable.

"The objective is not to make the enterprise do more AI," he says. "It is to redesign the enterprise so that it thinks, decides, acts and learns better."