The Algorithmic Bazaar
The digital commerce industry has spent the last twenty-five or so years optimizing a single, unit of measurement: the session. We built cathedrals of conversion rate optimization (CRO), obsessed over pixel-perfect hero images, and deployed armies of “customer success” bots that were little more than glorified FAQ routers. We tracked users from the moment they landed on the homepage, watched them struggle through navigational hierarchies, and celebrated when 3% of them actually bought something. Anywhere else, a 97% failure rate would be grounds for executive termination. In e-commerce, it was the benchmark for success. We can safely say that the era of the session comes to an end, thanks to conversational and then agentic commerce, which put the “homepage” on life support. What comes more and more into the foreground is the intent, whichis what the session was supposed to help derive. And crucially, the entity expressing that intent is increasingly likely to be a machine, not a human. What we are seeing now is the transition from browser-based commerce, where humans operate interfaces, to agentic Commerce, where AI agents operate APIs. This isn’t just a channel expansion like conversational commerce; it is a fundamental inversion of the retail power dynamic. In the browser era, the retailer controlled the environment. In the agentic era, the customer (or their proxy) controls the context. This is quite similar to what happened in the 2000s with the advent of social media. And it will likely be countered by vendors as fast as the power shift back then, e.g., using GEO instead of SEO. The demise of the search box Since the rise of Google, the...
AI Agents: Finally, a Digital Assistant That Doesn’t Just Sound Smart?
So, the time of agentic AI has come? What does this mean? Not for businesses, but for business users. These days, the main tool in the quiver of every business user in an enterprise is … the web browser. Initially web interfaces to business software and then SaaS software has seen to this. The result? Employees needed to build their workflows around a plethora of different web applications, having open a corresponding number of tabs at any given time. I just counted the ones that I have open: fifty-three. And that doesn’t even count the web browsers that I do not even recognize as such. For example, Apple Calendar, or Microsoft Outlook. Maybe even MS Word … one never knows where there’s a browser these days … Now, with agentic AI moving into the business, these workflows will need to change. How, that heavily depends on the vendors one works with and, of course, the size of the own business. One of the main considerations when moving towards agentic workflows or agent-supported workflows is the orchestration of agents. This becomes especially important when working with different software packages and this is also a core reason for the emergence of protocols like MCP (model context protocol) and A2A (agent to agent) or ACP (Agent Communication Protocol) that are currently developed. Plus, there are a few “agentic” browsers emerging that allow for the orchestration of different agents on the user level. But what is best, what to use – and when? After all, many of the vendors that are already in house, have their own strategies. And many of the buyers,...
Beyond the Hype: Unlocking GenAI ROI in the Enterprise
My past two column articles on CustomerThink dealt with how to determine the return of agentic investments and whether agentic AI delivers at all. The question of ability to deliver is particularly interesting for me, as I am researching measurable results other than cost savings in contained business areas for some months now, and regularly find a very strong focus on customer service and marketing, with customer service functions being best able to report measurable results. This is evidenced by the number of success stories I find, supported by the publication of a recent TEI of Zendesk customer service study. However, most of this is anecdotal evidence, or vendor sponsored/commissioned. And which vendor likes to speak about failures? Similar for buyers who understandably do not like to be in the spotlight with investments that turned out to be less than successful. There hasn’t been too much in depth research on whether generative and/or agentic AI deliver to promise or not. Luckily, there has been at least some research evaluating the capabilities of LLM based AI agents in business environments published this year. CRMArena-Pro by Salesforce Research naturally has a focus on CRM tasks across B2B and B2C scenarios. The authors identified nineteen tasks commonly executed in CRM systems and categorize these tasks in the four business skill categories database querying and numerical computation, information retrieval and reasoning, workflow execution, and policy compliance and includes a confidentiality awareness evaluation. TheAgentCompany on one hand covers a wider area along the business value chain but on the other hand has a narrower focus on software engineering companies. One other main difference between...