Surprisingly Useful AI Article Enhancements
By 2028, agentic AI will handle at least 15 per cent of daily work decisions, with approximately one-third of enterprise software featuring autonomous capabilities through agentic AI. This is as per a recent study by Gartner. However, it also predicts that over 40 per cent of agentic AI projects could be cancelled by end of 2027 due to high costs, technical friction or misaligned business expectations. Enterprises must move past the era of speculative pilots and embrace a disciplined model that delivers measurable, at-scale results.
Build AI factories, not silos
While experimentation has been broad over the last couple of years, only a few initiatives have achieved systemic impact. Early programs often delivered localised improvements but remained disconnected from broader operational priorities. To bridge this gap, enterprises are pivoting to “AI factory” models by standardising development, deployment and governance. By leveraging shared platforms, reusable assets and common controls, organisations reduce duplication and accelerate the extension of AI capabilities across the enterprise.
This factory-led approach shifts the burden of maintenance from manual oversight to automated, consistent protocols. When organisations treat AI like a manufacturing process, they gain the ability to replicate success across different business units, ensuring that a solution developed for one function can be adapted for another with minimal friction.
Harness ecosystems to scale intelligence
An AI-first frontier enterprise needs an ecosystem extending beyond the internal organisational chart. As AI becomes central to software engineering, new platforms help teams embed automation and governance directly into workflows. These tools are game changers for scaling; however, governance and executive stewardship remains a primary concern.
Scaling governed AI across the software development life cycle (SDLC) requires a strategic ecosystem of specialised tools acting as force multipliers. Developers are increasingly turning to platforms like Cursor to weave agentic intelligence into coding routines, helping them execute tasks autonomously and navigate security requirements without losing creative momentum. Similarly, frameworks from Cognition allow teams to move beyond AI experimentation into deployment, ensuring agents remain deeply rooted in business logic.
However, challenges arise when these models leave the lab and hit fragmented operational pipelines. This is where solutions like Harness change the day-to-day reality for delivery teams, making automated governance a seamless part of the process. By embedding these capabilities, enterprises bridge the gap between isolated, experimental AI pilots and a functional AI factory. This pragmatic approach to innovation empowers teams to clear the path to production, replacing technical uncertainty with the confidence that AI systems are repeatable, secure and ready for real-world application.
Redesign work around intelligent systems
While external ecosystems accelerate AI into production, operationalising these tools requires a deep internal evolution. The emergence of AI-first Global Capability Centres (GCCs) reflects a fundamental shift in enterprise operating models. Centres originally built for execution are now leading the charge in developing and embedding AI. This transformation is unfolding in sectors such as aviation, utilities and energy, where AI agents now perform tasks ranging from complex resource scheduling to predictive infrastructure maintenance.
In mining, this shift is particularly visible. By moving from isolated applications to AI-factory models, companies are transforming ore body modelling and autonomous operations. These systems improve yield and operational resilience by processing vast datasets in real time, enabling smarter decision-making in resource-constrained environments. By integrating high-fidelity sensors with centralised AI, mining firms can predict potential equipment breakdowns and optimise extraction processes.
Ultimately, the shift to an AI-first enterprise is less about the technology itself and more about how organisations manage the human-machine collaboration. Companies that standardise their internal processes while leveraging external ecosystems will move faster. Those that treat AI as a factory-grade asset, rather than a collection of experiments, will define the next decade of excellence.
Raja Shah is EVP and Industry head, Global markets at Infosys
Last Updated on August 20, 2026 by Raja Shah
