Change dot AI
Creating Competitive Advantage in GenerativeAI
This week’s post is from Santiago Vallejo. Santiago is a digital transformation leader focusing on improving the odds of change sustainability.
Unless you’ve been living under a rock, you’ve heard, read, and seen all sorts of hot takes about Generative AI (GenAI). While the general topic of Artificial Intelligence has been around for decades, GenAI has taken on more headlines due to its scale and speed of adoption. From the impending doom of job replacement to… wait, no, GenAI will not replace jobs, the consensus is clear:
GenAI is bringing technological disruption not seen since the days of… many other things that we also call unprecedented.
Computer scientists and engineers are churning out Large Language Models (LLMs) of different sizes and capabilities at breakneck speed, and marketers are eagerly affixing the GenAI label to a plethora of products and services, reminiscent of the “smart” device trend during the Internet of Things (IoT) boom. Concurrently, Wall Street — professional and retail investors alike — is on a drunk binge, throwing money at everything loosely tied to AI.
Given the velocity and magnitude of this paradigm shift, it is prudent to question our collective preparedness for the imminent transformation of our professional and personal spheres. GenAI’s impact is not linear but exponential, and it is challenging to recall a publicly accessible technology that has sparked such intense debate around ethics, privacy, intellectual property, and the fundamental nature of human cognition and creativity. However, the critical question remains: How can organizations assess their readiness to navigate this impending wave of change?
I posit that change management will be a critical competency for any organization seeking success in its GenAI journey.
Most organizations are likely going to be on the receiving end of GenAI, not on the creator side. This is because Large Language Models (LLMs) will likely be dominated and consolidated to a handful of players who have the scale, capital, and expertise (here’s an excellent piece by Scott Belsky on this subject).
Winning by developing a foundational model will be a long shot for most players who were not first to market (e.g., OpenAI, Meta, Alphabet, Anthropic). Developing a niche, fine-tuned model will be an opportunity, but that, too, lends itself to going the way of apps and web services that hitched their wagons to social media platforms.
However, an organization’s ability to adapt to and effectively utilize change will prove to be a significant competitive advantage in the GenAI landscape.
Through numerous conversations with colleagues across the spectrum of GenAI proficiency, I have identified three recurring deficiency themes across industries and sectors: lack of clarity, lack of clear benefits, and lack of guidelines for GenAI application.
- Lack of organizational clarity: Despite public announcements of GenAI initiatives, there is often a lack of clear communication regarding the practical implications for employees and stakeholders.
- Uncertainty regarding benefits and trade-offs: Ethical considerations and questions of personal relevance are prevalent. Discussions often center on whether utilizing GenAI constitutes “cheating” and if engagement with these systems equates to training our future replacements.
- Lack of guidelines and ambiguity in implementation: Even organizations actively engaged with GenAI struggle to define clear rules of engagement. While some proactive entities have issued guidelines on GenAI usage, many have deployed tools without providing adequate context for their application within the organizational framework.
To address these deficiencies, I propose the use of a structured change management approach.
One example of a simple framework used by Change Management practitioners is Prosci’s ADKAR, which stands for Awareness, Desire, Knowledge, Ability, and Reinforcement. ADKAR or a similar change model will enable leaders to understand the psychological, technical, and political state of the organization and its readiness and ability to incorporate GenAI into their system and its components (refer to The Iceberg model).
Combined, a change model and the basic tools from Design Thinking, like empathy and journey mapping, would provide a more comprehensive view of the context in which person and machine will interact.
For instance:
- Awareness: What’s the degree of familiarity with GenAI? What’s their emotional state regarding GenAI?
- Desirability: What are the net benefits to the system and its components? What would motivate this change?
- Knowledge: What practical and technical knowledge exist? How do people operate and interact with technology today?
- Ability: What are the physical, emotional, ethical, and technical skills that they’ll require to fully utilize the end product or service?
- Reinforcement: What measurements are in place today in the context in which people operate? How are they affected by GenAI?
While the journey to GenAI adoption will be long, tedious, confusing, and likely unnerving, the humans remain in control of their fate. Taking a change management lens in the design phase, rather than at the last stage of project planning, will bring a higher probability of success and sustainability to the adoption of GenAI.
- What do you think about the future of GenerativeAI?
- How are you approaching it for your organization?
Leave your thoughts in the comments or contact Santiago directly.
Santiago Vallejo is a visionary leader who is passionate about applying change management techniques to improve the experience of customers and employees in their daily routines. His experience spans operations, product management, innovation, and customer enablement. He is currently obsessed with all things GenAI as well as behavioral change. If you’d like to connect with Santiago, you can find him on Linkedin.
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