Jack Lampka

What is AI? … No, really, what is it?

Is artificial intelligence all about ChatGPT? Or Arnold Schwarzenegger’s Terminator? Or those recommendations from your favorite online retailer that you have been getting for over 25 years? There is a lot of hype about AI today and even more FOMO (fear of missing out). In this talk, AI will be demystified with a little history of how we got here, what AI is good for for companies and people, and what AI could do but shouldn’t.

Key takeaways:

AI and data literacy for everyone

Every successful data-driven organization needs data-minded employees, especially those in non-technical functions. A data & AI literacy program can lead to the desired data & AI mindset among non-technical employees … as long as this program is built bottom-up based on the current mindset level. Only then will you have a chance that all employees will embrace data & AI.

Key takeaways:

• People are the biggest driver of AI adoption in any organization
• Data mindset of users will make or break your success with AI
• Bottom-up data & AI literacy program is needed to improve the mindset

The success factors for AI adoption in your organization

70% of AI projects fail. Why? Could it be that the “people” aspect is being ignored, including people creating AI products for internal customers? The proven marketing framework of 5 Ps of product marketing can be used to internally market AI products, addressing the 5 Ps: product, price, place, promotion, and people … all of those elements essential for acceptance of AI in any organization.

Key takeaways:

• People are the biggest driver of AI adoption in any organization
• This includes people developing AI solutions
• The framework of 5 Ps of product marketing enables data teams to market their products.

Data and AI strategy

What is data strategy? Is it about building a data lake, hiring AI consultants, or training employees to use dashboards? Or is it about defining a plan for the organization to become data-driven and using data & AI for business decisions? This talk will define what strategy is and – using pragmatic examples – will describe the required building blocks of a successful data & AI strategy.

Key takeaways:

• Data strategy is a plan
• Choose what not to do
• Always start with business needs

Why do you need a data village to improve AI adoption?

Many companies who are facing AI & big data for the first time are thinking that hiring data scientists will do the trick and make them become a successful data-driven company. It won’t. It takes a data village, also known as the data analytics value chain, to build efficient & effective data products, and to attract & retain talent. These data villages are crucial in developing useful AI solutions.

Key takeaways:

• It’s not only data scientists you need to successfully implement AI in your organization
• Data teams differ from AI product to AI product and evolve over time