The need and opportunity for professional services and FDEs to deploy agents right now is massive.
Every tech wave offers a new era of consulting and tech services requirements. Moving from analog to digital led to a massive wave in the 90s. Moving from on-prem to cloud did the same in the 2000s. But this is going to be at a scale far greater than the others.
The reason is that agents fundamentally change the underlying workflows of an organization. Unlike most prior eras of technology, where it was a change in medium of the service being delivered (on-prem CRM to cloud CRM), agents rewire the business process itself. And unlike upgrading a tech system, business processes are full of idiosyncrasies.
Every industry will have its own variants, and every department within those industries will have variants as well. Not to mention the bespoke difference between firms. Bringing agents to marketing in CPG will look different from marketing in healthcare. Bringing agents to sales in a B2B software company will look different from a car dealership.
And none of the change is easy technically. You need to first modernize your infrastructure and data and make sure it’s ready for agents; access controls, entitlements, and permissions need to be mapped in a way that works for agents and people; you need to make sure agents have the right context to work with; you need to consistently eval and maintain the agents when there are model upgrades; and you need to drive the change management of the process itself to figure out which parts the people do and what agents do.
That’s an insane amount of technical and domain-specific process work to be done to make this all happen. Huge opportunity for new service providers, as well as internally teams and roles to emerge, to help drive this change.
AI Agents using browsers and computers is a big deal because you effectively have an API for complete knowledge work.
Right now, for AI to execute complex digital work, you have to go through the APIs of a wide array of services. Imagine a workflow where you want to collect data from LinkedIn profiles, cross referenced with web information, and move them to Salesforce records. Or look at data inside of contracts and fill out the corresponding details in an ERP system.
The challenge is that not every relevant service that you want an AI Agent to interact with has comprehensive APIs for every operation, and normalizing data across disparate APIs is often a pain. This means there’s a long tail of use cases that can’t be easily automated by AI.
For these tasks, in an ideal world you’d have AI perform them exactly as a human would. And this is why AI Agents using browsers and computers is very powerful.
Humanoid robots open up robotics to a wide variety of tasks in the physical world, because our world is designed for humans to interact with. Similarly, our digital world was designed for humans as well, and once an Agent can use a browser or computer, you can begin to perform any task that a human can.
This means you could farm out any complex research task that involves navigating or browsing lots of sources or perform read/write operations on any app or website at scale. Importantly, an AI Agent can do this at a speed far faster than a human and in a virtual machine you could parallelize these tasks across hundreds or thousands of instances.
This opens up AI to an even greater set of workflows we can bring automation to, most of which enterprises never even get around to doing today. The future is going to be wild.
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@HL3rd_ Wow, this is awesome! Coincidentally, I’m building a similar enrichment tool to help a friend’s business inbound process. Would to love to hear specifics on how you guys used the enriched data to improve inbound processes :)
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