Since investment in AI started ramping up in 2022, you’ve likely seen some version of the following. A ChatGPT-like window shows up inside the user experience, a chatbot nestled in the bottom-right corner of a dashboard. An AI recommendation or summary gets injected into a workflow. AI tooling gets grafted hastily onto legacy enterprise software.
These additions barely augment the user experience. They make little difference to the actual value the product delivers to the user. The result looks like progress, but often isn’t. It’s an AI feature in name, not in substance.
In part, this is an outgrowth of software companies striving to say they’re “AI-enabled.” There is, however, a more insidious pattern at play. Instead of asking, “What are the most cumbersome parts of the user experience and workflow?” some enterprise software companies take the path of least resistance and ask, “What’s the easiest place to add AI?”
As long as those companies keep building AI on top of old workflows, their software will stay constrained by its incumbent qualities. Bolting AI onto an old process doesn’t change the process, it just adds a new layer on top of it.
Not All AI is Built the Same
Not all AI products are alike, and they don’t deliver the same results. Some are relatively prefabricated by AI software providers and can be whitelabeled into other software user flows (think chatbots, recommendation engines, and AI-generated summaries). These are fast to deploy, which is part of their appeal, and that speed is exactly why they’re so common.
Without the right calibration and pre-launch testing, though, these products can generate embarrassing failures. One car dealership saw its AI chatbot offering cars for $1. That kind of failure isn’t rare when AI is deployed without real testing behind it.
More commonly, though, these tools just aren’t relevant to users. They’re window dressing, or even noise-making. With pressure to deliver “AI products,” these low-lift tools are an expedient way to seemingly modernize a product without substantively innovating on its core flows and utility to end users.
Real AI improvements start with many of product development’s long-standing best practices. That means going beyond the existing product to uncover the deeper operational problem that might be solved. It requires concerted engagement with and research of end users. It means understanding where automation can eliminate demanding tasks, rather than just adding a new feature.
None of that is new to product development. What’s new is applying it to AI specifically, instead of skipping straight to deployment.
It also involves understanding AI’s inherent capabilities. In the fleet management space, that may involve approaching inventory as an inherently multivariable environment, one that’s messy to manage through manpower alone and ripe for AI-driven automation. It’s also about understanding how automation can help uplift the average end user, since not all end users are alike. Not all fleet managers, for example, will know to replace a timing belt when replacing a car’s transmission.
By returning to what the end user needs, rather than what’s the easiest AI win, the end user will actually use the AI feature. By extension, they’ll use enterprise software more. The enterprise software improves and the operational environment improves, too.
Why Companies Default to the Easy Path
This isn’t the standard approach for most AI product rollouts. Incumbent enterprise software companies tend to view AI as an incremental improvement rather than as a technology that demands a wholesale overhaul of how their products work. It’s a difficult truth to admit. Unlocking new benefits requires long-term strategic thinking about how AI should change a product, not just supplement it.
That kind of thinking is harder than shipping a chatbot. It takes longer, and it means admitting that the current product has real gaps.
Enterprise software providers stand to benefit from doing this work properly. It brings cultural improvements and an opportunity to reinvigorate engineering and product development teams at enterprise AI companies. It’s also a chance to bring AI into the company itself, injecting it into the inherent multivariability and messiness of engineering and corporate environments. The benefit isn’t limited to the customer-facing product.
Real Value, Not Optics
There’s a need to make sure that the things a product does, once enhanced by AI, are materially better than they were before. Companies cannot conflate marketing messages with actual customer value. A chatbot in the corner of a dashboard is not, on its own, a meaningful improvement.
Enterprise software companies that get this right have a special leg up here. They’re able to deliver outsized, multi-million-dollar savings and results for their clients. That advantage is real, but it only pays off if the work behind it is real too. It’s just a question of executing on that promise well, and substantively, rather than settling for the easiest place to add AI.
To learn how Fleetio embeds AI into fleet maintenance workflows to reduce administrative work, control costs, and keep vehicles ready for service, visit fleetio.com.
