Let me tell you about my recent podiatrist appointment. No, not the medical bit (which is boring), but the podiatrist’s use of generative AI. Like me, he’s not the youngest, but his enthusiasm for the solution he’s using was palpable. It’s a transcription system that makes him both more efficient (no more typing up of consultation notes) and effective (he can concentrate fully on the consultation, and be sure not to miss any details from the discussion). He showed me the result from our discussion at the end, and it was impressive – not a single correction needed; and it wasn’t just notes – the output is a well-structured document. And no, I wasn’t surprised by how good it was: The system he uses has been developed by clinicians for clinicians, and work on the model(s) has been going on for five years.
Why am I telling you this? Because we’ve reached a point with genAI where we’re oscillating between breathless hype about all the productivity gains and other benefits the technology is bringing, and downbeat headlines about how it’s not delivering the expected results, let alone yield a positive return on investment. My little opening story demonstrates (I hope) that the negative messages need to be taken with as much salt (not just a pinch, a whole bucket full) as those promising too-good-to-be true results.
Get A Reality Check At Forrester’s Technology & Innovation Summit EMEA 2024
This situation leaves many decision makers wondering on what basis they should make their technology choices and investments – believe the naysayers, and adopt a wait-and-see attitude? Or go full steam ahead? During my keynote at Forrester’s forthcoming Tech & Innovation Summit EMEA in London, 9 – 11 October, I’ll
- Provide a stock-take of where we are with genAI today,
- Share insights into the characteristics of successful genAI initiatives in the enterprise and highlight the most common mistakes,
- Provide guidance based on these insights and learnings, and
- Highlight why we need to take a strategic approach if we want to get the most out of genAI. And of course
- Explain why I’ve called my talk “Generative AI As A Forcing Function For Change”.
