Recently, many businesses have started discussing AI adoption. But for a business owner, the more practical questions may be: with so much already needing attention every day, what can AI actually help with? How much will it cost? And where should we start?
My advice is to hold off on choosing tools. First, look at the work that happens repeatedly in your company but keeps getting stuck because of staffing, information, or handoffs.
First, find out where the time goes
For example, after receiving a customer inquiry, a sales representative may need to look up product specifications, prices, and previous correspondence, then check with several departments before replying. AI can help summarize the inquiry, find relevant content in designated sources, and produce a reply draft with sources for the representative to review before sending.
A similar approach can help organize meeting notes, summarize customer complaints, find internal procedures, or work through large volumes of documents. But every company has different bottlenecks: if work is waiting for a manager's approval, simply using AI to write drafts faster may not shorten the overall wait.
Choose one task and try it on a small scale
Start with a task that happens every week, has a relatively clear process, and has someone willing to take responsibility for it. Common customer inquiries are one example. First, record how long it currently takes from receiving a question to completing the reply, how much time colleagues spend searching and revising, and the common errors.
Then run a pilot with a small set of cases you are authorized to use, removing unnecessary sensitive information. Let AI propose drafts, retain human review, and check the results using cases that were not used to refine the setup. When comparing results, count not only drafting speed but also the time spent checking, revising, and handing work over.
If common questions already have fixed answers, well-organized templates or a clear knowledge base may be enough. If questions vary widely and require combining information from different sources, then consider adding AI. Tools should fit the work, rather than making the work fit the tools.
How do you decide whether to continue?
Before the pilot, agree on a few criteria with the colleagues responsible:
- How much human handling time is actually saved from the start of a task to its completion?
- Is response quality consistent, and have errors or rework increased?
- What are the costs of tools, maintenance, and staff learning?
- What must a person confirm, and what data must not be sent to external services?
Even if time is freed up, that does not mean the company immediately saves the same amount in cash. It depends on whether that time can support a greater volume of service, work that was previously out of reach, or a better customer experience.
AI may also help a company explore new services, organize customer needs, or support product ideas. Whether these directions are viable depends on your customers, data, and actual market validation. A tool demonstration alone is not enough to conclude that it will generate revenue.
For me, the value of AI application planning lies in working together to clarify the task to improve, the data available, and the risks to address, then planning a small pilot and how to evaluate it. These decisions help you determine what to do first, how far to take it, and whether further investment is worthwhile.
If you are considering AI applications for your company, feel free to get in touch about one task you most want to improve. A brief description of who does it, how it works today, and where it most often gets stuck is enough to start the conversation. You do not need a technical proposal, customer lists, or other confidential information.