There’s nothing futuristic about AI – here’s how to use it now

The most useful AI for a community bank is not the version that arrives in three years. It is the version already sitting on top of data the institution owns right now, quietly removing work that used to take weeks. That version is far less glamorous than the headlines, yet far more valuable..Start with the audience. A marketer at a community institution who wants to reach, for instance, small business owners with strong deposit balances and no lending relationship has traditionally had two options. Wait in line behind the one analyst who can write the query or approximate the list by hand and hope it is close. Both cost time the marketer does not have, and both produce a list nobody fully trusts. AI changes the mechanics of that request. The marketer asks the question in plain language, the system translates it into the right query against governed data, and the audience comes back in seconds. The analyst is freed for work that actually requires an analyst. The marketer stops guessing.Then the plan. With AI, teams can build a marketing plan for growing deposit relationships over the next 12 months, for instance, with particular attention to deposits being lost to attrition, and it will return one in minutes. Not a vague outline, but a plan more complete than most marketing teams or data analysts could assemble in weeks of work. The value is not that the machine is clever. It is that the institution gets a serious, data-grounded starting point without tying up its best people for a month to produce it, and the marketer keeps the judgment over what to actually run. But it’s not about replacing these teams; it’s about helping them move quicker and with greater accuracy.Then the frontline. A branch banker does not need a dashboard. A branch banker needs to know which three relationships to call today and why. When the intelligence sits behind a simple daily view, the banker signs in, sees the next best conversation already surfaced, and acts on it. No query, no export, no data science team. The knowledge that used to live in the head of a 30-year veteran becomes something the institution holds and can hand to the next person, which matters more every year as experienced staff retire and newer employees rotate through faster than they used to.None of this requires a semantic model the institution has to build, a data science hire it cannot afford, or a leap of faith about where the technology is headed. It requires clean, connected data and a thin, careful layer of AI on top of it. That order matters. The intelligence is only as good as the relationship data underneath it, which is why the unglamorous work of assembling a complete view of every relationship comes first and the AI comes second.Keith Henkel is founder and CEO of FI Works