Looking at the Generative Artificial Intelligence (GenAI) market today, there is so much noise, confusion, and focus on GenAI technology and tools… but where in the organization can GenAI be effective? What are the top three uses for GenAI? How can GenAI be used to get the desired results? What purpose is GenAI going to provide and why do you need GenAI? These are the questions that must be asked to shape the intent for leveraging GenAI. Be intentional, be purposeful in your quest for learning and using GenAI.
Once you have a starting intent and purpose defined, what is the smallest role, project, or product having the people that are open and wanting to be the first to experiment—to be an early adopter? As a leader in the organization, do you have a predetermined group of early adopters you go to for starting these types of initiatives? Have there been people in the organization that have mentioned they are interested in experimenting with GenAI? One idea is to ask around to see if anyone is interested in being in the first cohort or group of people to experiment for the organization.
With a starting intent, purpose, and group of people identified, it is time to establish the GenAI Guardrails by defining the values and beliefs which will create the culture and mindset for what is and how GenAI will be utilized. To summarize: today’s GenAI initiatives follow a common critical path:
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- Identify, gather, centralize, and cleanse the data which will be used to train GenAI
- Choose a Large Language Model (LLM) which will best serve your intent and purpose
- Choose an alogrythm(s) which will best serve your intent and purpose
- Train the people on how to train the GenAI
- Start experimenting and discover who will benefit most from GenAI and why
From the basic critical path steps listed above, what do you think is the most important or critical?
Intelligence Catalyst’s perspective is “Train the people on how to train the GenAI” is the most important and critical step which is often missed, not understood, or ignored.
Just like we have learned from previous adoptions and transformations of disruptive technology, attention to an organization’s culture (defining the value and belief set for the mindset of the people to have a psychologically safe environment, experiment, learn by failing, tapping into innovation to evolve, and do something new and different) is required for success. Too often, most individuals and their organizations focus on the tools and technology features & functions, which is why transformational efforts have been (and continue to) fail.
HEADS UP… for GenAI, the stakes are even higher!
Why? With the evolution of GenAI’s reasoning abilities, the disruptive effect of GenAI is exponentially different from previous disruptive technologies. Reflecting back on history, virtualization led to cloud adoption. Once cloud was in place we had software gobbling up the world as Software as a Service (SaaS), which led to a plethora of X as a Service (XaaS) offerings. What differentiates GenAI’s disruption from the previous disruptors is that it is data-based. The ability to put together a data set is a capability which most people in most roles can do by themselves. In order for Software to disrupt, you needed programmers to develop the software. With GenAI, all you need is a person’s data set and access to a GenAI tool. The threshold to create, experiment, learn, and evolve has just been lowered to the level of a person who wants to try. Organizations now have a reality where the culture has to be supportive and sustaining of peoples’ mindset to harness innovation and mitigate disruption across the entire enterprise at all levels of the organization.
Another reason why the stakes are higher is that today’s GenAI decisions are setting the foundation and shaping your future workforce and company’s operational efficiency and effectiveness. Looking at various surveys, company leaders believe GenAI is no longer an option, but a must have tool in order to remain competitive in their industry.
The result of these two reasons alone are believed to elevate the adoption rates of GenAI as the most impactful disruptive technology adoption and transformation for organizations and industries today. It is also being forecasted by experts that the accelerated use of data with GenAI will break silos, push through barriers, and disrupt entire ecosystems in an organization’s business universe.
Forecasting around GenAI’s evolution and disruption, one thing emerges as certain… we recommend to our clients be prepared as best as you can.
Intelligence Catalyst believes one of the best ways individuals and organizations can succeed at their GenAI adoption and transformation, is to start by putting in place values and beliefs that promote a mindset not fearful of GenAI, but to embrace an experimental approach and understand it. Start as small as you can with the smallest dataset you can to create the Smallest Language Model (SLM) you can. Realize you as the human are training GenAI—realize that your ability to learn about how to enable GenAI to learn is the key to how successful, how powerful, and how beneficial your GenAI will be. Also realize that we as humans have a responsibility when we use GenAI. We must be aware of our biases, our ideology, our politics,… etc. and we are responsible for keeping the data sets for our learning models clean and free of errors. Another responsibility we have as we use GenAI is to be transparent of our use of it. One analogy that comes to mind is plagiarism. When you create something, it is yours, but when GenAI provides a processed result, it is GenAIs (specifically, use of a GenAI tool may require relinquishing intellectual property rights). Therefore, remember to declare and disclose when GenAI has been used.
If you are interested in continuing the conversation, would like some help or assistance, please reach out to me or one of my team mates at Intelligence Catalyst. To schedule some time, please visit our Contact Us page and fill out the form, or visit my bio page under Meet The Team and “book a call with John”. Thank you for your time, hope this was helpful, and look forward to hearing from you 😊
From John – This article was written from a TYIIR Part 1 – Individual & Part 2 – Team perspective