4#6 - Rasmus Thornberg - Decision Science and AI between Use Case and Product (Eng)

MetaDAMA - Data Management in the Nordics

«Focusing on the end-result you want, that is where the journey starts.»
Curious about how Decision Science can revolutionize your business? Join us as our guest Rasmus Thornberg from Tetra Pak guides us through his journey of transforming complex ideas into tangible, innovative products.
Aligning AI with business strategies can be a daunting task, especially in conservative industries, but it’s crucial for modern organizations. This episode sheds light on how strategic alignment and adaptability can be game-changers. We dissect the common build-versus-buy dilemma, emphasizing that solutions should focus on value and specific organizational needs. Rasmus's insights bring to life the role of effective communication in bridging the divide between data science and executive decision-making, a vital component in driving meaningful change from the top down.
Learn how to overcome analysis paralysis and foster a learning culture. By focusing on the genuine value added to users, you can ensure that technological barriers don't stall progress. Rasmus shares how to ensure the products you build align perfectly with user needs, creating a winning formula for business transformation.
Here are my key takeaways:
Decision Science

• You need to understand the cost of error of a ML/AI application
• Cost of error limits the usability of AI
• Decision Science is a broader take on Data Science, combining Data Science with Behavioral Science.
• Decision Science covers cognitive choices that lead to decisions.
• Decision Science can just work in close proximity to the end user and the product, something that has been a challenge for many.

From Use Case to product

• Lots of genAI use cases are about personal efficiency, not to improve any specific organizational target.
• Differentiating between genAI and analytical AI can help ton understand what the target is.
• genAI hype has created interest from many. You can use it as a vessel to talk about other things related to AI or even to push Data Governance.
• When selecting use cases, think about adoption and how it will affect the organization at large.
• When planning with a use case, find where uncertainties are and ability for outcomes.
• It’s easy to jump to the HOW, by solving business use cases, but you really need to identify the WHY and WHAT first.
• Analysis-paralysis is a really problem, when it comes to move from ideation to action, or from PoC to operations.
• «Assess your impact all the time.»
• You need to have a feedback loop and concentrate on the decision making, not the outcome.
• A good decision is based on the information you had available before you made a decision, not the outcome of the decision.
• A learning culture is a precondition for better decision making.
• If you correct your actions just one or two steps at a time, you can still go in the wrong direction. Sometimes you need to go back to start and see your entire progress.
• The need for speed can lead to directional constrains in your development of solutions.
• Be aware of measurements and metrics becoming the target.
• When you build a product, you need to set a treshold for when to decommission it.

Strategic connection

• The more abstract you get the higher value you can create, but the risk also gets bigger.
• The biggest value we can gain as companies is to adopt pur business model to new opportunities.
• The more organizations go into a plug-n-play mode, the less risk, but also less value opportunities.
• Industrial organizations live in outdated constrains, especially when it comes to cost for decision making.
• Dont view strategy as a constrain, but rather a direction that can provide flexibility.

Capitole

1. Exploring Decision Science in Data Management (00:00:00)

2. Connecting Data Science to Business Strategy (00:15:09)

3. Continuous Improvement in Data Product Management (00:22:35)

4. Driving Adoption Through Value and Usage (00:32:38)