Enterprise technology’s next chapter: Four gen AI shifts that will reshape business technology

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Companies often overestimate the impact of short-term changes in technology and underestimate the effect of long-term changes. This well-known dynamic is particularly relevant for generative AI (gen AI) in enterprise technology. Today’s many bold predictions about its impact on enterprise technology often focus on shorter-term horizons (with immediate focus on efficiency and productivity in two to three use cases) rather than on more forward-looking shifts and implications.

The authors’ recent discussions with tech leaders across industries suggest that four emerging shifts are on the horizon as a result of gen AI, each with implications for how tech leaders will run their organizations. These shifts are:

1. From tools that support teams to AI ‘artisan’ and ‘factory’ teams.  Teams may evolve with two new human–AI patterns of interaction: the “factory (i.e., for predictable, routine processes deploying autonomous gen AI–enabled agents that can collaborate and navigate the work end to end)” and the “artisan (for processes that require human judgment and ingenuity gen AI tools are implemented at scale to serve as assistants, aiding and enhancing the work of experienced software engineers and enterprise technology strategists and executives)”.  One of the challenges leaders may face is effectively blending these approaches to create a fluid, synchronized workflow as tasks move from human to AI and back to human.
2. From application architectures dominating the landscape to predominantly AI agent and data architectures.  IT architectures are also expected to be significantly different, evolving from a traditional application-focused approach to new multiagent architectures where tech leaders oversee hundreds or thousands of distinct gen AI agents that can communicate with one another and the outside world to achieve a common goal.  Tech leaders are expected to deploy these agents within their environments in three primary ways:  A) Super platforms – represent the next generation of third-party business applications—such as collaboration tools, customer relationship management (CRM), or enterprise resource planning (ERP) solutions—with built-in gen AI agents. B)  AI wrappers -these are essentially intermediary platforms that enable enterprise services to communicate and collaborate with third-party services via APIs without exposing their proprietary data. And C) Custom AI agents – internally developed by fine-tuning a pretrained LLM or using retrieval-augmented generation (RAG) with a company’s proprietary data.
3. From a ‘pyramid’ or ‘diamond’ organizational structure to a flatter one, with new workforce development considerations
4. From application- to infrastructure-based cost structures, with increased focus on compute spend

Full adoption of this new enterprise technology operating model is likely a decade away, and success will require more than tooling. It hinges on understanding where to implement factory and artisan patterns, designing an effective agent architecture, and preparing for the many implications on talent, cost, operations, and risk. Starting with a few enterprise technology domains can help leaders build their organizational muscle for operating in these new ways and extrapolate learnings to gain efficiencies as they scale. Given the scale of change, the journey will be challenging, yet the long-term impact will likely be greater than currently perceived.