AI Transformation Starts with Customer Value Related Problem-Solving (Pt 1 of 2)

By Stephen WullschlegerStephen Wullschleger | Jun 20th, 2024 | AI Consulting & Implementation, data analysis & BI, digital transformation, management consulting, strategic operations, | 0 Comments

Human centered AI transformation needs to focus on customer value as the most important business outcome; start with the problem, not technology.

AI adoption continues to accelerate in 2024, fueling growth for enterprises and organizations around the world. As per a June 2024 McKenzie report, the percentage of organizations that have adopted AI in at least one business function has grown from 55% to 75% in the past year.

Across industries, AI is playing an important role in increasing productivity, predicting potential issues with real time analytics, improving decision-making, enhancing cost efficiency and scalability, and improving customer experiences. Led by marketing and sales, product and service development, and IT functions, all corporate departments are beginning to regularly use AI:

A McKinsey illustration of AI transformation in marketing and sales, to products and services and IT, used by Steve Wullschleger, AI consultant at

How do businesses strategize and implement AI technology? Following the discussion in our last article about common questions organizational leaders should ask, this article provides a guideline for AI transformation.

What is AI transformation? How is it related to digital transformation?

AI transformation refers to the process of integrating artificial intelligence into various aspects of a business or organization to improve efficiency, decision-making, and overall performance. This transformation can impact multiple areas, including operations, customer service, product development, marketing, finance, and more. The goal of AI transformation is to leverage AI’s capabilities to gain a competitive edge, innovate, and adapt to changing market demands.

AI transformation is a subset of digital transformation, focusing specifically on integrating artificial intelligence into various aspects of a business or organization. Before jumping into AI transformation with both feet, it is important to ensure that digital processes are effective and reliable. Digital transformation relies heavily on data analytics to drive insights and decisions. AI transformation takes this a step further by using advanced AI models to analyze large datasets, predict trends, and automate complex tasks, thus making data utilization more powerful and efficient. In a future article, we will discuss the importance of timely, reliable, secure, private and auditable data and data strategy.

Speed is one reason why AI is proliferating. Companies are using AI to:

  • learn more quickly from the data at their fingertips, as well as data available outside their organization
  • adapt and implement solutions more quickly
  • embrace knowledge, increasing the overall “iq” of the organization
  • speed up innovation, adapting to changing trends and market dynamics
  • enable decision makers to stay current and leap ahead of competition in a rapidly changing world. Conversely, if you don’t you will be left behind.

Today, all digital transformations include AI transformations too. “A digital transformation’s work is never done. Successful companies treat it like a muscle, one they are continually building and strengthening” (see McKinsey). To win this long game, “The 4 Cs” may help organizations sustain their digital and AI transformations: Capability driven, Continuous, Competitive, CEO led.

As your industry evolves, how will you keep ahead? How will you continually provide greater customer value? 

The AI transformation journey starts with the problem, not the technology

“For digital and AI transformations to succeed, companies need to understand the problems they want to solve and rewire their organizations for continuous innovation”, said Eric Lamarre in a podcast, November 22, 2023, in a McKinsey analysis.

Technology trends can be alluring, but not all new technologies are suitable for every organization. Starting with the problem helps avoid the trap of adopting technology for its novelty rather than its utility, ensuring that decisions are based on real needs rather than hype.

By identifying and understanding the specific problem, your AI initiative will be directly aligned with the organization’s needs. It will be easier to obtain stakeholder buy-in and to allocate resources effectively.

As Rodney Zemmel, Senior partner, McKinsey & Company says, “It’s best to start with a concentration in a particular area rather than sprinkle a little bit of digital or a handful of analytics use cases broadly across the organization. Pick one area of the business and really focus on building some momentum there first and then on growing from there on out.” 

“When you reach a rewired state, those few teams multiply a 100-fold and work in various parts of the organization—sales, supply chain, manufacturing, R&D. They serve the leaders of those different areas, developing technology to solve their problems. At that point, no one calls IT to develop a solution because each area has that capability. IT evolves into a distributed function with distributed technology capabilities.” In other words, businesses think of technology less as a separate department such as HR or finance and more as a fundamental aspect of the organization.

Business outcomes are both the starting point and the destination for AI transformation

An AWS illustration of AI transformation process starting and ending to business outcomes, used by Steve Wullschleger, AI consultant at

The above illustration in an AWS whitepaper highlights that both the starting point AND the destination of an AI transformation journey are the business outcomes that you seek to achieve, and from which you work backwards.

What is missing from the above illustration, as one of the most important “business outcomes”? – End-user or customer value! 

“Transformation must begin from the inside with an acute understanding of pressing stakeholder needs. Maintain continuous awareness of user sentiment through human-centered approaches to pinpoint areas ripe for intervention. Without human-centered methodologies, downstream solutions will likely lack the context needed for adoption and targeted impact”, said this Forbes article, about how to identify areas for change.

As a rule of thumb, approaching AI transformation is more effective and efficient by going through these steps:

  • Adopt AI where it helps achieve the end goals for the highest customer value by making  your teams more productive, creative, and collaborative;
  • Identify high priority projects that deliver immediate value for customers; 
  • Incrementally improve and iterate, one project after another, not all at once;
  • View AI Transformation holistically, since all interconnected parts of digital processes and infrastructure need to be readjusted and re-constituted after one part is changed.

Whenever you develop a technology, there will be a secondary effect somewhere in the system that will prevent you from fully capturing the value. For instance, adopting AI at an airport to shorten luggage processing time was met with subsequent pallet loading delay. The answer was to train operators at the airport on how to maximize pallet caseloads. That’s not a technology problem; that’s just a good old operational problem. Technology usually unveils a bottleneck in the process that needs to be solved.

Any large technological adoption agenda is a long journey, especially when adopting a technology that is rapidly evolving, such as AI”, according to the same AWS whitepaper. “Begin with the end in mind”, said Stephen Covey a long time ago in the book “The 7 Habits of Highly Effective People”. And the ultimate end for AI transformation is customer value.

Key steps for AI transformation

Before an organization begins to take the steps outlined below, it is paramount that you first go through an initial AI feasibility assessment, as outlined in our previous article on AI consulting.  After aligning AI initiatives with your brand‘s mission, vision, values, and business objectives, while keeping a continuous pulse on the needs of the end users and customer experience, develop a clear AI strategy for AI investment and deployment in line with a company’s business goals to achieve specific results.

Learn by doing. Find a place to start and get started. Embrace a bias toward action. Be adaptive with leadership. Consider the following 6 steps for AI transformation. (To be continued in Pt. 2 of 2).  To schedule an initial consultation about your AI journey, please contact us. Thank you.

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Steve Wullschleger, AI consultant at addresses a comprehensive approach to AI transformation for companies: people, data, digital transformation, etc.