Driving Intelligent Action – Simplifying AI Decisions for Value Creation

Stop obsessing over decisions - and start obsessing over actions.

I had to tell a team that over analysing is costing them time and value - and possibly even jobs!

With all the hype about AI and data, it is too easy to get caught up in complex models and decision-making processes. But the smartest leaders know how to cut through complexity and focus on driving intelligent action. This newsletter shows you how to simplify your AI decision-making process, align it with your business goals, and create strategies that consistently deliver value.

So, if you’re ready to simplify your AI decisions and start driving real impact, here’s where to begin.

I will also share how to:

1. Download all the cheatsheets and much more.
2. Find out where you stand with the Top Data & AI Leaders Challenge!
3. Get personalised value driven coaching for you or your team.
4. Get my books including the FREE (Anniversary Edition) of "Value Driven Data (the Workbook)".

Enjoy!

Edosa

AI to Replace or Augment Rule-Based Systems – Finding the Right Role for AI

When integrating AI into existing systems, a key question arises: Should AI replace your rule-based systems entirely, or should it augment them? This decision affects how flexible, transparent, and adaptable your processes will become. Here’s how to make the right choice.

What to Do:??

Use AI to replace rule-based systems if you need adaptability and real-time learning for dynamic, evolving tasks. Choose augmentation if you require transparency, human oversight, or if your rule-based systems are already effective at managing structured decision-making.

Why It Matters:??

Rule-based systems provide transparency, control, and consistency, while AI offers flexibility and adaptability. The key is finding a balance where AI can assist in handling exceptions or more complex decisions, while rules manage predictable tasks.

When to Apply:??

- Choose replacement for processes that need to handle complex, evolving scenarios that can’t be covered by predefined rules.??

- Use augmentation when you want AI to enhance structured decision-making without losing control or human oversight.

See the decision tree in the image below for a comprehensive breakdown on deciding between AI replacement or augmentation.

Get a downloadable HighRes of this image and much more by clicking here.

Data Ownership vs. Data Stewardship – Rethinking Control and Collaboration

Once you've decided where AI fits into your systems, the next step is to think about data management. Should data be “owned” by specific departments, or should it be managed through cross-functional stewardship?

What to Do:??

Choose Data Stewardship to foster collaboration and shared accountability across teams, allowing for more integrated insights. Opt for Data Ownership if departmental control is essential for security or operational efficiency.

Why It Matters:??

Data ownership creates silos, limiting the potential for cross-functional innovation. In contrast, Data Stewardship ensures that data flows across the organization, leading to more comprehensive insights and better decision-making.

When to Apply:??

- Use stewardship when data needs to be accessed and used by multiple teams.??

- Stick to ownership when data security, privacy, or highly specific departmental needs take priority.

For more insights, see the contrast image below that highlights the differences between Data Ownership and Data Stewardship.

Get the downloadable HighRes image by clicking here.

AI Hype vs. AI Reality – Tempering Expectations for Long-Term Success

It’s easy to get caught up in the hype surrounding AI, believing it will instantly revolutionize your business. The reality is that AI requires time, iteration, and strategic planning to unlock real value.

What to Do:??

Approach AI with a long-term perspective and avoid expecting immediate, overnight success. Instead, focus on small, measurable wins as you scale your AI capabilities.

Why It Matters:??

Expecting instant results from AI can lead to frustration and poor decisions. AI hype often leads businesses down the wrong path, while AI reality encourages a thoughtful, gradual approach that ensures sustainable value over time.

When to Apply:??

- Adopt AI reality when scaling your AI systems for long-term success.??

- Avoid falling for AI hype that promises quick returns without careful integration or strategic planning.

For a clear breakdown of AI hype vs. AI reality, see the counterintuitive image below.

Get a downloadable HighRes of this image and much more by clicking here.

In Summary:

Driving intelligent action with AI and data requires thoughtful decision-making at every stage. First, decide whether AI should replace or augment existing rule-based systems to ensure flexibility and control. Next, consider whether data ownership or stewardship will provide the best framework for cross-functional collaboration and innovation. Finally, manage expectations by aligning with AI reality - small, measurable wins will add up over time, ultimately leading to sustainable value creation.

This comprehensive approach ensures you can integrate AI and data strategies effectively, without falling for hype or rushing decisions. Download high resolution versions of all above images (and more by clicking here) to help guide yourself and your team through these critical choices.

#1:?Get downloadable cheatsheets, my other newsletters, and much more by clicking here.

#2:?Get your personal score here to find out where you stand in the Top Data & AI Leaders Challenge - also helpful fun as a team challenge or across your network!

#3:?Get personalised coaching for you or your team by clicking here.

#4:?Get my books by clicking here - including "Value Driven Data", "Making Data Work" and the FREE (Anniversary Edition) of "Value Driven Data (the Workbook)".

Best regards,

Edosa