How to make your employees first-class data & AI adopters?

Nine months ago, I took on a new role: Business Lead data & AI after onboarding some cool data projects, it is time for phase 2.

Data platforms

What we have learned so far is that data platforms and intelligent enterprises are within reach. It is just a matter of ticking all the boxes:

• Clarify strategy and/or process focus by defining a north star challenge;
• Start and end with the customer internal or external focus. Stay human, even with system optimisation;
• Define product or service mindset and hyper focus on one thing and create success;
• Transform your (IT) delivery. IT is everywhere, so prepare a delivery board;
• Push boundaries but stay culture pragmatic by respect existing learning curve;
• Use an ecosystem and connect with others to create or accelerate other points of view.

Humanisation

Yet even if all boxes above are ticked, one factor remains key for success: humanisation.

I believe this humanisation factor will become a driving factor in the sixth wave of Schumpeter’s innovation cycle, that is about mature digitization (data & AI) and clean tech.

This is a visualization of the history of innovation cycles | World Economic Forum (weforum.org)

Why humanisation is a driving factor?

1. The gap between existing technology and understanding/usage by end-users is becoming bigger and bigger. We can only close this gap by involvement.?
2. Data & AI challenges are complex and need human creativity.
3. AI development will become more and more non-IT/end-user based.

How can you provide or create this humanisation??

Interesting reading AI on the Front Lines (mit.edu)

1. “Increase end-user benefits” If a technology strategy exists, we should add an adoption/humanisation strategy. ?It creates?a viewpoint where technology investment is balanced with adoption investment. What if my data & AI benefits for end-users was tracked such as Strava? What could be the learnings?
2. “Reduce end-user labor” Leverage existing data & AI solutions with our most valuable resource creativity. How many tools are already in place? What is the usage?
3. “Protect end-user autonomy” Part of your adoption strategy is a teaching part for the so called low-code platforms in the matter of data & AI. How much end-user involvement is in today’s data & AI projects?

What are the results of humanisation??

1. Higher usage of existing data & AI investment because of ‘understanding’ and ‘focus’. Can be a personal/team plan for every stakeholder on how to overcome the challenges of this data & AI complexity.
2. New solutions by combining business acknowledge with data & AI toolbox.
3. When having all data and underlying technology we tend to overcome so called ‘analysis paralysis’. By effectually collaborate with humanisation factor we can reduce the overload. interesting reading: Gartner Says 70% of Organizations Will Shift Their Focus From Big to Small and Wide Data By 2025?

Most organisations may have found the “golden egg” in the matter of data & AI. But remind yourself that the goose (your organisation and systems ) should have “humanisation” attention to hold or create more value.

Balance value, adoption and technology

At the end of the day, my team and I are still data experts but, we are on a mission to approach the market based on the humanisation vision above. What must result in a healthy balance between value, adoption and technology in the matter of data & AI projects.?

If you want to challenge me or recognize the same challenges, feel free to pm me. [email protected]

Looking forward to the next nine months to ramp-up our mission to?turn data & AI into value.