Company Research with AI Agents – When Does Automation Pay Off?
Company Research with AI Agents – When Does Automation Pay Off?
According to research, salespeople spend only about one-third of their time on actual selling. A large portion of the remaining time goes to background research: googling pages, browsing LinkedIn profiles, searching for news, and trying to build a complete picture. The same process, the same formula, every time from scratch.
Last week I held a demo at the "AI Agents Innovation Day" training event, where we built an agent-like workflow for exactly this. The sales situation is a good example, but similar needs repeat elsewhere where information must be gathered systematically - in partnership negotiations, competitor analysis, recruitment, board work.
A simple test for identifying suitable agent targets: is the task repetitive, is it boring, and is its outcome verifiable?
Background research fulfills all three. Same formula every time. No creative problem-solving. And the outcome - a company profile - is easy to evaluate: is the information correct or not. The demo combined sources, analysis, and writing into one chain: read → enrich → analyze → write. The same logic works elsewhere where repetitive knowledge work is structurally clear.
It seems that the most common mistake in agent design is not technical but strategic. It's biting off more than you can chew. The most interesting targets aren't found where the hype is strongest, but where people think: this takes time from us every week, but doesn't really require much thinking. Don't start with autonomy - start with clear workflow.
How does your organization identify tasks that are repetitive, boring, and verifiable - exactly those that agents are best suited for?
#ArtificialIntelligenceAgents #AIStrategy #HavuAI #Wakaru
Marko Paananen
AI consultant and builder with 20+ years in digital business development. Helps companies turn AI potential into measurable business value.
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