Unleashing the Power of Generative A.I.
A New Era of Hyper-Productivity and Personalization
In my previous post, I stressed the need to be cautious regarding the speed and implementation of A.I. models, particularly new Generative Large Language Models (LLMs). This time, I want us to jump into the potential short and medium opportunities Generative A.I. models provide.
Although the jury is still out there for whether it will change the world for the better, one thing is certain: the world will change.
One way in which we can look at the potential changes is by focusing on how it will affect us as users and consumers.
As users, A.I. will scale human creation as it makes us faster, more efficient, and more capable. I.E., it will unleash an array of hyper-productivity like never seen before. You are seeing some of it today. When you read that companies such as JP Morgan or my old company, Accenture, are investing quite heavily and on a hiring spree for A.I. and prompt engineering experts, they are saying that they seek to unlock the additional potential of their employees. In a matter of months, you will begin to see an unprecedented level of change at work.
The beauty of these models is that they can be implemented in any industry, for any function, at the individual level, and globally. So, it is safe to assume that most non-manual labor jobs will change (and it is fathomable that even those could change).
But what happens when you, and everyone around you, can produce 1,000x more of what it did before?
Well, it unlocks the story's next chapter: robots talking to robots. To illustrate, imagine your company implements a localized Generative LLM to respond to client proposals. Suddenly, you can use all the proposals your organization has ever created (as long as they are in their data lake) to automatically generate, in seconds, a new proposal perfectly tailored to your customer's request, including graphs, images, realistic photos, or whatever you may need. A proposal that you can review, tweak, and submit within the hour…
This is mind-blowing (if your organization is anything like the ones I worked in), and in which responding to RFIs, RFAs, and RFPs, usually involves multiple people, across various departments, at different levels, and with various expertises. Bringing them together to contribute, review, edit, and sign off is a task worthy of PMP Master. The fact that we can simplify that process and reduce it to hours (if not minutes) while allowing all those people to continue their jobs unlocks an incredible amount of value. And, although we are control freaks who want to have the final say before we are convinced that a computer can do it better, sooner than later, people will realize that even that can be done by the A.I. (training the model to think and contribute as we would do). Those companies that see this vast potential will.
But what happens when you are on the other side? Let's say you are either an FMCG company asking for a campaign proposal to marketing firms or a bank asking an architectural firm for a new building idea. Based on previous experience, you would expect 5-10 proposals, from which only 2-3 are good. Suddenly, you receive 100 excellent proposals within the first two hours.
Well, you can also create a model that not only uses your own organization's data to help you filter, review, and compare proposals to find the best fit but can also communicate with the providers for clarifications or additional information before moving to the next stage. In other words, Bots Talking to Bots. The exponential growth of data quality and quantity will demand organizations to implement their own A.I. solutions to help sift through and manage it.
And this is applicable across all industries and functions:
Sift through studies and research to improve products
Create procurement or supply chain forms
Provide or review accounting or financial reports
Review resumes
Create and implement large population evacuation plans
Manage distribution fleets
Plan and manage a cruise
Create lesson plans
Talk caringly to patients
Etc.
Etc.
Etc.
If that is how your work life will change, how will your personal life change?
Enter the age of hyper-personalization. Two recent examples show the magic and potential in this space. The first was Sal Khan's Ted talk, in which he introduced Khamigo. Khamigo is Khan Academy's personal tutor, career coach, and now most valuable learner companion for students. Technically, it is an LLM that sits on top of ChatGPT. The LLM is a layer within the Khan infrastructure to serve as the guardrail, ensuring accuracy and effectiveness. But the magic is a tutor solely focused on you becoming the best student you can be and providing you with all the guidance, support, and time to get you there. It never tires, gets hungry, frustrated, or bored, and it is (will be) free (beta asks for a $20 monthly donation).
I came across the second example on TikTok, where a marketing firm created a hyper-personalized marketing campaign for Carvana. The campaign's goal is to make customers nostalgic for when they bought their current car (I assume that it also seeks to inspire them to buy a new one, but I digress). Using both Carvana's consumer data and augmented external data, the campaign gives their cars a voice, takes the buyer back to the day, time, and place where it was bought, plays the music that was at the top of the charts and recounts many of the events that happened during that time. Wow! Now imagine the brands you love can provide you with a user experience.
For some, that may be a dystopian future reminiscent of the Minority Report scene in which Tom Cruise walks through a mall and myriad personalized ads jump into being. To others, many of the services we consume, from wealth management to accounting, to travel, to entertainment, will be tailor-made to our current needs (if not our desires).
So what can companies do to take advantage of these opportunities?
I recently had two separate conversations that helped me connect some dots. First, I spoke with a friend and former colleague who served as an H.R. executive in various global firms. We talked about how A.I. is changing how companies hire, how it is easier for tech companies to hire that talent, and how the combination has many executives in non-tech industries paralyzed as their boards ask them to futureproof their organizations and are not yet equipped to do so.
In parallel, I recently began advising and working with Robert Maguire and Jin Paik, founders and Managing Partners of Altruistic, an A.I. Business Services and Investment Firm. Rob is a successful serial entrepreneur, and Jin, until very recently, was the Head of Labs at the Data, Digital, and Design Institute at Harvard University (D^3) and the founding G.M. for the Laboratory of Innovation Science at Harvard (LISH).
They see many of the same issues and are working to solve them. The first issue they see is talent, particularly for non-tech companies. All organizations are struggling to understand their data fluency at all levels (e.g., from the executive issue I mentioned before to data entry I.C.s); what are these organizations actually trying to achieve with their A.I. implementations (Generative and otherwise), and how to combine the talent with the right algorithms to achieve their goals.
Our talent conversations are pretty interesting, as most executives are unaware of how transferable data fluency and data skills are (they are), how to leverage them in their organizations, how to appropriately assess them, and how to provide a roadmap skills development for their people to align with the goals of the organization. To that end, they recently launched Talente (still in beta). It is a Data Talent Platform developed in partnership with Harvard. It assesses people's data fluency across seven different data roles (Business Analyst, Data Translator, Data Analyst, Prompt Engineer, Data Engineer, Data Scientist, and Machine Learning Engineer).
The beauty of their approach is that the A.I. algorithm running in the background provides individuals executable paths for skills development and the organization to see their data knowledge composition and gaps at various levels. I find it great because it gives organizations a baseline of where they are and the tools to screen for their needs.
I also learned from them that although many companies claim to be A.I. or provide A.I. services, only a few have proprietary models that can be deployed right now to help their clients achieve their goals.
That combination of proprietary ai models, expertise, and mental prowess makes me think many companies will spend a lot of money implementing A.I. solutions. Still, only a few will succeed: Those who can baseline their organization's data fluency, adjust as needed, and partner with the right A.I. partners will leapfrog their competition. And, by the time their competitors react, it will be too late.
Generative A.I. models promise a future characterized by hyper-productivity and hyper-personalization. As we prepare to ride this tidal wave of change, organizations must understand their current data fluency, make necessary adjustments, and choose the right A.I. partners. Every company, function, and person will use it differently, but only you understand what's holding you back.
The future may seem daunting, but I am now old enough to have seen this play before. The Mozilla browser showed the world the power of the internet. It changed how we communicate, work, socialize, and entertain. So did the advent of the smartphone. It changed how we communicate, work, socialize, and entertain. Social media also changed how we communicate, work, socialize, and entertain. And so will (Generative) A.I. change the way… well, you get the gist.
Feel free to comment, share, or reach out for a deeper discussion on how A.I. can revolutionize your business.