There are three ways to becoming an “expert”.
I observe on my LinkedIn a flood of so called experts (mainly in AI, as this is my bubble).
1. Those who built their voice internally within an organisation, most likely have been part of building a AI-powered product and understand the power and consequences of applying powerful tech to solve tough problems.
2. Those who act more of an aggregator, or curator, but haven’t build a software/hardware product themselves but want to ride on a trending wave. You know, the “1000-AI-apps-you-should-know-about” kinds.
3. Those who start from scratch, building a tech startup to solve a problem they experienced or believe needs to be solved; eventually incorporating some of the AI tech in their product’s stack (when it makes sense to do so).
Whom should you listen to?
Whom should you trust, when you’re thinking of implementing AI in your workplace? Probably not those who are the “loudest” on social media (don’t mind me, I’m just a messenger 😅). Those people know how social media algorithms work, and are well aware that often it’s not the quality, well-researched content what’s prioritised on your timeline.
Be aware of a snowball effect – that liked people are even more liked and followed, not for the content relevance per se, but because the numbers promise quality as IF they were verified by the others. That’s the whole genius about reviews and track record of something. You’re more likely to book a hotel or buy a product with many reviews than one with little. Verifying quality is tiring and time consuming, and people don’t want to do that, so they use numbers and stars as a shortcut.
People who are highly diligent and make significant contributions tend to work quietly and away from the spotlight. They usually are also bad in promoting their achievements, so it’s hard to spot them.
So how to find them? You need to consider multiple factors;
- Firstly, assess their track record and credentials. Look for evidence of their expertise, such as successfully developed AI products – case studies, or testimonials from reputable sources. Secondly, consider their understanding of your specific industry or domain. Expertise in AI alone might not be sufficient, if they lack insights into your industry’s unique challenges and requirements.
However, be aware that often certain skills or solutions are transferable and can be applied to smilingly completely different industry with an equally great success. E.g, when we’ve started offering Untrite decision intelligence solutions, we were focusing on manufacturing and real estate industries, and only through insistence of our colleagues who noticed problems similar and wider application of our solution, we started offering our product for law enforcement. - Evaluate transparency and ethical considerations of their AI implementations. Trustworthy experts should prioritise responsible AI practices, including fairness, accountability, transparency, and privacy, especially now, that it’s difficult to predict the outcomes of building large training models.
- While it’s important to be cautious about “loud” thought leaders, some genuinely provide valuable insights. Look for individuals who consistently share well-researched content, back their claims with evidence (sources), and promote responsible AI practices. Verify their credentials, reputation first.
Ultimately, you should always seek a diverse range of opinions and consult multiple people to gain a well-rounded perspective, not only those who are well-established and most visible.
