Although business leaders are already partnering with alternative service providers other than Infosys, the company’s strategy of delineating the focus areas needed to implement AI offers significant value. The six areas described provide practical reference points that can be used in any organization to plan projects or perhaps monitor and evaluate ongoing implementation efforts.
Among these, data preparation is central. AI systems depend on the quality and consistency of data. That’s why investment in data platforms, data governance, and model-supporting engineering practices are the central principle upon which AI initiatives are built.
Integrating AI into workflows means that it is sometimes necessary to rethink how employees work. Leaders need to be aware of how AI agents and employees interact and measure performance improvements. Changes can be made both to the technologies deployed and to the working methods that have existed until now. In the latter case, retraining and education of the employees concerned will be necessary, with the resulting costs.
The issue of legacy systems requires careful attention as many organizations operate in complex domains that limit the agility needed for AI to improve operations. AI tools themselves can help analyze existing dependencies and even plan modernization, implemented, ideally, in multiple stages or separate sprints.
Physical operations increasingly intersect with digital systems. For businesses with physical products, such as in manufacturing or logistics, integrating AI into devices and equipment can improve device monitoring and responsiveness. This will require coordination between IT, OT, engineering and operational teams, and business line leaders will need to be particularly consulted.
Governance should accompany any scale of AI implementation. Risk assessment, security testing, formulation of security policies, and design of AI-specific guardrails should be established from the start. Regulatory scrutiny of AI is intensifying, particularly in industries dealing with sensitive data, and legal sanctions apply if data is lost or mishandled, regardless of its source (AI or otherwise) within the enterprise. Clear accountability structures and documentation reduce these operational and reputational risks.
Taken together, these areas indicate that AI implementation is organizational rather than purely technical. Success depends on leadership alignment, sustained investment, and a realistic assessment of potential capacity gaps. Claims of rapid transformation should be treated with caution, and lasting results are more likely when strategy, data, process design, modernization, operational integration and governance are addressed in parallel.
(Image source: “Infosys, Bangalore, India” by theqspeaks is licensed under CC BY-NC-SA 2.0.)
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