AI Leadership: Decision Making Steps for AI Assessment (Pt 1 of 4)

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A step-by-step guide for making AI adoption, AI development, and AI leadership strategies for CEOs, organizational leadership, and business owners.

In Pt 1, I discuss AI related decision making steps for business leaders. In Pt. 2, I talk about how to create quality content or data to feed AI tools. In Pt. 3, I discuss developing leadership and people for the AI Age.  In Pt 4, I will explain experience design (XD), after listing what AI can NOT do.

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To listen as a podcast

The AI tidal wave is gathering momentum, getting close to the shore, and will soon hit the land.

The speed at which AI is developing is astounding: Just a few years ago, IoT, SaaS, and Cloud computing were trendy tech words. Beginning with the launch of ChatGPT on Nov. 30, 2022, we are now hearing new terms such as “large language models (LLM)” (the datasets that work to feed generative AI), “AI hallucinations”, “ambient intelligence” (mind-reading by AI), “brain privacy” (concerns for AI’s knowing too much about what we think)… 

Many comfort zones are being stretched. Many corporate leaders and employees are feeling some discomfort to say the least, even anxiety about the unknown ahead.

What jobs will be replaced? What work will vanish? What careers will thrive in the age of AI?

At a LinkedIn Live webinar by Chief, on “Executives in the Age of AI: Leveraging AI for a Competitive Edge” on Dec 7, 2023, Panelist Salima Lin from IBM said: “64% of the 6,000 enterprise CEOs (we surveyed) are feeling immense pressure to adopt generative AI much faster than they are doing today; however, most of them don’t even have a consistent approach to generative AI.” 

“CEOs we talked to believe that almost 50% of the jobs within their organizations are going to be impacted by generative AI in the years to come, but most of them don’t think they have the skills to deal with these types of changes.” 

We are at the early but not too early stage of AI, all experiencing various degrees of “AI anxiety”, “AI hype”, or just FOMO (fear of missing out), or “AI greed”. 

On a positive note, AI, like the water swelling beneath the boat, will elevate your industry, your company (the “boat”), and everyone inside. It will increase productivity, creativity, and employee satisfaction (ESAT), customer satisfaction (CSAT), and customer delight (specifics to be discussed in Part 2 of this article).

On a cautionary note, AI, just like electricity, just like nuclear power, can improve human life, bring humanity closer to peace and co-existence. But if it is used wrongfully, it can destroy, divide, and isolate us from each other, making our world a contentious, hostile, and dangerous place. AI cannot be used as a zero-sum game, meaning one party’s gain is based upon another party’s loss. It must be all-way-winning for all parties involved.  It is entirely up to us to let AI vanquish humanity, or advance it. We really don’t have a choice. 

Leaders need to be mentally and practically prepared NOW, for the AI high tide, and surf the AI wave instead of being overwhelmed or worse, drowned.

Here is a step-by-step approach to AI for leaders. (If I miss anything, please comment and add.)

Does my company need AI? — Take these steps to assess:

– Take a deep breath. Pause.

– Not everyone needs AI. Do not engage in herd mentality.

– If you know for sure that the end users or customers of your particular products or services can NOT benefit from AI tools – congrats! Have a good day. You don’t need to read the rest. 

– But if you are not sure, take the next steps:

(1)  Figure out what specific pain points of your customers, what concrete issues can and should be solved by AI. If you want to either adopt or develop narrowly focused AI solutions, are the pain points significant enough to be worth the investment?  Please note that the pain points are NOT merely about your company’s cost-cutting and revenue goals, but about the satisfaction and delight of your end-users, clients, and customers.  

  •  For example, when a customer calls a contact center, gets frustrated with an automated, pre-recorded, and mechanical voice (the “conversational AI” without a conversation.) She gets stuck pressing phone buttons with no answers to her questions, or with typing questions on a website and the chatbots cannot automate solutions for her specific issues. AI can play a role here. AI should immediately detect “customer sentiment”, either from her tone of voice, and/or choice of words, and signal it to a human agent, who can chime in right away: “I see that you are frustrated, can I help?”  This is a very specific and common customer pain point that AI can solve in real time, to shorten customer wait time and eliminate customer friction points. That can create value for both customers and the company’s sales. But why is no one doing it? Because these companies are using AI to cut costs. They are not customer centric. But with little investment in training AI to alert human agents to intervene at this particular point, customer facing companies can increase sales with improved customer experience and customer satisfaction.


(2) This is one of the most important steps: Before you decide to develop any AI solutions, ask yourself again, and again, and again: Is this something humans can do faster, better, and more efficiently than AI? This is one of the most important lessons Elon Musk has learned: After making the mistake of blindly automating Tesla’s production line, he had to de-automate afterwards, throwing away the robots that cannot do as well as humans, as recounted in the book “Elon Musk”, by Walter Isaacson. To ride the AI wave, to get advantage, is to adopt AI in a focused way, NOT generic application but for very specific, well-targeted use.

(3) Before you rush into adopting or developing any AI tool, do yourself a favor: conduct a careful research and thorough analysis on the pros and cons of developing AI vs. developing humans. (If you need independent, objective, and experienced consultants who are outside your own forest, AND don’t have the internal agenda of your CTO, CIO, or anyone else, contact us.) 

(4) Upon conducting objective research, if the answer is a resounding NO – there is absolutely NO WAY humans can do a specific task faster, better, and more efficiently than AI, then and only then proceed to the next ROI analysis. Please note again that your ROI is not merely for myopic cost-cutting and revenue goals, but for the long term bottom line, by delivering satisfaction and delight to end-users, clients, and customers.

(5) Carefully study the ROI for developing a specific AI solution for the targeted problem or pain point of your customers.

After going through all of the above, there are two more fundamental questions to ask.

Will AI benefit my customers, my company, and humanity? — Two Questions for AI leadership: 

Since the AI genie is long out of the bottle, ignoring or disengaging from it are not wise choices for those who stand to benefit. We need to “grab the bull by the horns” and force AI to go where WE want it to go. Generative AI will add lots of value to businesses and humanity IF DESIGNED AND USED IN THE RIGHT WAY.  We humans must set the course for generative AI to assist and augment humanity, not to destroy it. 

Leaders must ask themselves these two questions, before getting into the nitty gritty of AI adoption or development:

(1) Will AI be used to serve or to harm humanity? It is a moral choice, based on practical consequences, not just a general, philosophical question.  if the answer is the former not the latter,

(2) What kind of AI data will be fed to the AI tools, specifically serving my customers, organization, and humanity’s well being (since AI is as good as the data we feed it)?

Leaders need to evaluate on a case by case, usage by usage basis, for every AI related decision.  NEXT, in Part. 2, I will discuss “What leaders should do NOW, to develop AI tools”, and how to feed AI tools with experience design, brand messaging, and brand voice.   

Stay tuned.

© Joanne Z. Tan    All rights reserved.

In Pt 1, I discussed AI related decision making steps for business leaders. In Pt. 2, I talk about how to create quality content or data to feed AI tools. In Pt. 3, I discuss developing leadership and people for the AI Age. In Pt 4, I explain experience design (XD), after listing what AI can NOT do.

About the author: Joanne Z. Tan is an executive brand coach, brand strategist, content creator, AI experience expert, leading 10 Plus Brand, Inc, a multiple award-winning brand building, brand marketing, experience design for AI, customers, users, consumers, global digital marketing agency. Please feel free to contact us for any content, branding, and experience design advice by visiting, thank you.


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