Is Your Company Focused on the Future?

McKinsey says that companies are making some progress with AI, but most have a way to go. In some areas, like healthcare, the stakes are particularly high when AI could be presenting a recommendation for patient care. In financial services, regulators may need to know why an organization is making particular decisions—on lending, for example. But explainability can present another risk: lack of AI adoption, leading to wasted investment and the risk of falling behind the competition. (McKinsey, 2020)

Do You Suck at AI?

So let me ask you, do you suck at AI? That might feel like a rude or brash way to ask about your readiness for Artificial Intelligence, but leadership worldwide is experiencing it in different ways. Wouldn’t it be great to know that your organization can go confidently in the direction of its dreams rather than suck at creating new pathways to success utilizing this powerful technology?

I have opened the doors of discussion this month with John Lindsey, President of Incite Logix and purveyor of knowledge in the “Don’t Suck at AI” movement. Technology can be your greatest ally, or it can ruin you, depending on your ability to know which roads to go down. My colleagues and I are not creating new data scientists, we’re creating excellent AI navigators.

AI is Pervasive

The State of AI is everywhere. It starts very simply and can become extremely complex, such as in deep learning. It’s why I wrote The Executive’s How-to-Guide to Automation as a primer for those who want to go from observer to a high-performing actor.

Those of you who are AI practitioners understand how difficult it is to demystify it for our clients. No one buys what they don’t understand, whether it has monetary or intrinsic costs associated with it. The journey we wish to illuminate is:

• What AI is?
• How AI works?
• Where can we use AI?
• Who is at the Wheel of the AI Bus?

How To Know Who’s Right for AI

A high AI failure rate is an indicator of a failed strategy. Whatever you are trying to mitigate, improve, or optimize in your business has to be anchored to a business problem. Knowing which technology to attack that business problem with is yet another skill level. It’s not just about the technology. Technology doesn’t solve problems by itself. There must be a skilled driver.

AI requires a high-level skill set. It is not a transferable skill per se with an IT or technology administrator. It requires passion in its use. It requires programming and math skills. It’s certainly not a do-it-yourself endeavor. It can be a learned technology, but the technology is most successful when entrusted to the right drivers.

Mimicking Human Logic and Reasoning

Human logic and reasoning are the very basis for AI applications. The foundation of every automated process starts with a once human action or activity. A recorded number of actions that make up a process are documented – or even worse, not documented. In there, your vulnerabilities lie. How are you documenting your most important – and now you know, your most vulnerable processes (humans and a clipboard)? Think about what manual data collection is left to digitize in your organization.

Raise Your Hand if You Love Spreadsheets!

I should have asked the question, “Raise your hand if you use spreadsheets”. I would see every hand in the room go up. The next question is about loving them; yeah, not so much. Those spreadsheet people, these are the magicians that give us our trillion-celled reports and pivot tables. Know now that this is a dangerous process for decision-making. Again, another reason why I am compelled to share AI with you, meeting you at any entry point. What you don’t know can create an unsustainable black hole of information that is miscalculated, with compromised data, vulnerable to a hack, and simply misleading.

Let Me Show You What I Mean

When was the last time a spreadsheet spoke to you? When you looked at it did it reveal a well-lit and visible path leading to the answer? Can you speak to a spreadsheet and have it understand your command, interpret and implement the command? You cannot. Exactly.

Visualization of Data is a Whole New Body of Science

Image: https://www.tableau.com/learn/articles/interactive-map-and-data-visualization-examples

“The commonality between science and art is in trying to see profoundly – to develop strategies of seeing and showing.” – Edward Tufte

In Edward’s world, PowerPoints are blasphemous.

In Tufte’s ethos of information design, “clear and precise seeing becomes as one with clear and precise thinking.” We need such clarity and precision more than ever. (Rosenberg, 2011)

Now That You Can See Your Data

Be ready for the unexpected. Create the sandbox for your model. Prepare your iterations of observable and implied conditions. What if we break the known laws of physics and take the StarTrek Enterprise to its limit of warp speed 9.95? Do we want to try that with the actual Enterprise, or with a duplicate model? Scotty is going to vote for that sandbox model because “he’s giving her all she’s got Captain!” Are you willing to push your resources to uncharted limits? Models are the answer for every stakeholder.

This is the beginning of knowing about things that are bigger than us. Simple science tells us that we create the problem, then the model, work backward to find the contributing elements, and then create the experiments. Take the results of those experiments and create beautiful representations that speak to us without words, in a language that can be understood by all who visualize that solution with you – no translation needed. This is the beauty of AI and automation.

Discover More in the “Don’t Suck at AI” Podcast

Join John Lindsey and me for a hypothetical journey with data science as we attempt to run our real-world “Enterprise” problems to Warp 10 using AI. We’re taking you where no man has gone before!

Apple Podcast: 

https://podcasts.apple.com/us/podcast/episode-1-dont-suck-at-ai-george-danner-ceo-business/id1569236799?i=1000523913776

Works Cited

Balakrishnan, T., Chui, M., Hall, B., & Henke, N. (2020, August). The state of AI in 2020. McKinsey & Company. https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/global-survey-the-state-of-ai-in-2020.

Rosenberg, S. (2011, September 26). The Data Artist. Salon. https://www.salon.com/1997/03/10/tufte970310/.