What comes after agentic AI? This powerful new technology will change everything

Ten years from now, it will be clear that the primary ways we use generative AI circa 2025—rapidly crafting content based on simple instructions and open-ended interactions—were merely building blocks of a technology that will increasingly be built into far more impactful forms.

The real economic effect will come as different modes of generative AI are combined with traditional software logic to drive expensive activities like project management, medical diagnosis, and insurance claims processing in increasingly automated ways. 

In my consulting work helping the world’s largest companies design and implement AI solutions, I’m finding that most organizations are still struggling to get substantial value from generative AI applications. As impressive and satisfying as they are, their inherent unpredictability makes it difficult to integrate into the kind of highly standardized business processes that drive the economy.

Agentic vs. Interpretive

Agentic AI, which has been getting tremendous attention in recent months for its potential to accomplish business tasks with little human guidance, has similar limitations. Agents are evolving to assist with singular tasks such as building websites quickly, but their workflows and outputs will remain too variable for large organizations with high-volume processes that need to be predictable and reliable.

However, the same enormous AI models that power today’s best-known AI tools are increasingly being deployed in another, more economically transformative way, which I call “interpretive AI.” And that is what’s likely to be the real driver of the AI revolution over the long term.

Unlike generative and agentic AI, interpretive AI lets computers understand messy, complex, and unstructured information and interpret it in predictable, defined ways. Using much of the same IT infrastructure, the emerging technology can power large organizations’ complex processes without requiring human intervention at each step.

Use cases

Some interpretive AI applications are already in use. For example, doctors are saving significant time by using interpretive AI tools to listen to conversations with patients and fill in information on their electronic health record interfaces to track care and facilitate billing. In the near future, the technology could determine fault in auto accidents based on police reports written in any of thousands of different formats, or process video recorded from a laptop screen as someone edits a presentation to provide teammates with an automated update on work completed. The applications are wide-ranging and span all manner of industries.

Based on estimates for areas such as coding and marketing where generative AI is most applicable, interpretive AI could unlock 20% to 40% productivity gains for the half of GDP that comes from large corporations. First, though, they must commit to developing a comprehensive, long-term strategy involving multiple business functions and careful experimentation, and change entrenched processes and work culture norms that slow its adoption. Done right, the obstacles are surmountable—and the payoff could be massive.

A different application of generative AI models

One of the most basic drivers of economic growth is the ongoing effort to standardize and scale up a particular process, making it faster, cheaper, and more reliable. Think of factory assembly lines enabling mass production, or the internet’s codification of computer communication protocols for use across disparate networks.

Generative AI has been, on the whole, disappointing when it comes to automation. For example, many firms have tried to use generative AI chatbots to reduce the time their human resources staff spends answering employees’ questions about internal policies. However, the open-ended output from such systems requires human review, rendering the labor savings modest at best. The technology seems to inherit much of the unpredictability of humans along with its ability to mimic their creative and reasoning skills.

Agentic AI promises to do complicated work autonomously, with smart AI agents developing and executing plans for achieving goals step-by-step, on the fly. But again, even when agents become smart enough to help a typical knowledge worker be more productive, their outputs will be quite variable.

Enter interpretive AI. For the first time, computers can usefully process the meaning of human language, with all its nuance and unspoken context, thanks to the unprecedentedly large models developed by firms like Open AI and Google. Interpretive AI is the mechanism for using the models to exploit this revolutionary advance.

Until now, computers’ ability to capture, store, aggregate, summarize, and evaluate a large organization’s activities were limited to those that were easy to quantify with data. Interpretive AI can quickly and precisely execute these functions for many other important activities, at a vast scale and at minimal marginal cost. For instance, no longer will businesses need manual processes to monitor and manage levels of activity and progress in knowledge-worker tasks such as coding a feature into a software solution or developing a set of customer-specific outreach strategies, which usually require dedicated middle management staff to collect information.

Companies can make productivity gains by using interpretive AI for a range of other previously hard-to-measure employee issues as well, including the tone and quality of their interactions with customers, their cultural norms in the workplace, and their compliance with office policies and behavioral expectations.

Transforming the management of knowledge work

The use of interpretive AI will enable the widespread transformations that unlock newly efficient ways of working at large organizations (which are responsible for organizing and producing most of the world’s goods and services). It will dramatically reduce the need for extensive, costly, slow-moving, and unenjoyable middle management work to coordinate complex interrelated programs of activities across teams and disciplines.

Even better, it can efficiently understand operationally vital but opaque aspects of how work happens, such as the decades’ worth of legacy code and data that make even minor technology process changes time-consuming and challenging for any long-lived enterprise.

Of course, interpretive AI is not mutually exclusive with generative and agentic AI—again, it’s simply a different way to use the powerful models that power those technologies. A decidedly unsexy way, certainly, but for businesses looking for ways to maximize the economic impact of AI over the next few years, it’s just the unsexy they need.