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AI is here to Stay, but how? And what can it be done to get advantages from it?

AI's increase in popularity is astounding, from cars and homes to hospitals and businesses. But what will its future hold for AI?

Two models - Gartner's Hype Cycle and Technology Adoption Lifecycle can give us some clues.

Gartner's Hype Cycle Model details the stages that new technologies go through as they're released to the public, beginning with high hopes and expectations but gradually giving way to reality and disappointment as time passes. Over time, we learn how to use technology in everyday life better.

Meanwhile, their Technology Adoption Lifecycle model shows us how quickly people adopt new tech; first, only risk-takers test out a product before more people adopt it, eventually becoming widely adopted - although with AI already having achieved mainstream use, how long until its total business capacity?

As AI evolves, one model that helps me guess its direction is the Technology S-Curve model. According to it, technological improvements typically begin slowly before picking up momentum before slowing back down over time; AI currently finds itself in its rapid phase with many advances still ahead.

We also need to consider that we could have different scenarios which mainly depend on ethics, technical problems, and regulations. In addition to these points, data might not have the proper quality to be used by ML and AI.

Another issue is understanding how AI even works, which could undermine employee trust in the technology. It can also be hard to find experts in AI. As a last point, the gaps between expectations and what AI does.

It looks like the journey will not be a smooth one. There will be bumps in the road, setbacks, and probably more hype. It is essential to keep an eye on how AI develops and the impacts It can have on different stakeholders and look at the possible solutions to the points above:

From the executives' side, what can be done to mitigate risks and create a less bumped journey?

  • Get everyone on board about AI-( change and culture)

- Vision ( a realistic one) - Data culture!! (It is the new additional element to the organisational culture)

  • Be realistic about what AI can do for your business ( scoping)

  • Use AI to solve specific problems, not just for the sake of it (reject the hype)

  • Managing and cleaning your data à Data quality

  • Train your team or hire specialists to fill the AI skills gap.

  • Address concerns like bias and security to lower AI risks.

Summing up: Activate an AI implementation strategy.

Stay tuned…


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