Where do most SMEs start with AI and what goes wrong?

Small and mid-sized businesses (SMEs) are often attracted to AI's promise of efficiency and insight, but many stumble in their first steps. The journey typically starts with good intentions, but also some common missteps.

How SMEs usually start:

Most begin by buying off-the-shelf AI tools or launching a pilot project focused on automating repetitive tasks: think invoice processing, customer email responses, or basic scheduling. The aim is to free up staff time and unlock data insights without huge investments. However, it's easy to be swayed by "must-have" technology, resulting in AI being implemented because it's trendy rather than to solve a specific, painful problem.

Why early AI projects often go wrong:

Starting with tech, not a business problem: Many SMEs begin with, "We need AI," instead of, "We need to solve X." This leads to tools that don't fit key needs like an unpopular chatbot or an unused dashboard.

Overestimating ease and ROI: AI rollouts can get bogged down by underestimating costs (financial and time), overestimating what “out of the box” solutions can do, or planning for “big bang” transformations instead of quick wins and measurable returns.

Data and integration hurdles: Poor data quality, disconnected systems, and lack of digital readiness can derail even simple projects. If information is scattered or not digitized, AI can’t deliver value.

Lack of staff buy-in: Not involving employees from the start can spark resistance or leave teams unprepared. Collaboration across roles and departments is essential for AI success.

Over-customization: Attempting to build custom AI from scratch mimicking big enterprise approaches can waste resources. SMEs thrive by being pragmatic: using plug-and-play options first, and scaling up only as needed.

Takeaway

SMEs succeed fastest when they anchor AI projects in real business needs, start small, measure results quickly, and look for solutions that fit their existing workflows. At AxionLab, we guide SMBs to spot where AI brings real ROI, help them sidestep common traps, and ensure new tools work hand-in-hand with their teams and data with no buzzwords, no wasted spend, just practical value.