Summary. As an AI expert, I understand the challenges companies face when starting with generative AI without proper guidance. The insightful case studies from a growing global community of over 3,000 GenAI practitioners introduce a new category of work, more nuanced than traditional ‘knowledge work.’ Dubbed ‘WINS Work,’ this concept focuses on tasks and functions that involve the manipulation and interpretation of Words, Images, Numbers, and Sounds (WINS). This innovative framework assists leaders in identifying their business’s vulnerability to advancements in AI technology and aids in strategising an effective response. It’s a critical tool for businesses aiming to adapt and thrive in an era increasingly shaped by AI-driven transformations.
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To best understand generative AI (GenAI), look at it through bifocal lenses. Through the top lens, one sees the long view of big, looming issues, such as accuracy, privacy, and bias, as well as GenAI’s potential impact on “knowledge workers” and even economy-wide job losses and societal risks. While this view is important, we have heard over and over that it’s not helpful to executives and board members because it’s difficult to translate from the generic insight that millions of jobs will be impacted to what it means for their businesses today.
That is why we also recommend looking at GenAI through the bottom lens, and taking in what’s directly ahead. Here, leaders can find the immediate opportunities and threats from GenAI that firms face today. In taking this view, however, leaders may also need to adjust what they’re looking for.
Our case studies, based on our growing global community of over 3,000 GenAI practitioners, point to a new category of work, more precise and actionable than “knowledge work.” We call it WINS Work: the places where tasks, functions, possibly your entire company or industry are dependent on the manipulation and interpretation of Words, Images, Numbers, and Sounds (WINS). Heart surgeons and chefs are knowledge workers but not WINS workers. Software programmers, accountants, and marketing professionals are WINS workers.
GenAI has the potential to be power tools for WINS work. It can generate new prose, computer code, images, narration, music, and videos as well as ingest and summarize, critique, improve, and reformat almost any manner of document or analysis. Every WINS task, subprocess, and end-to-end process within your enterprise (and in many cases the entire enterprise) should be evaluated for potential leverage with GenAI.
How Urgent is it for My Firm to Pay Attention to GenAI?
We believe the easiest way for companies to proceed is to ask themselves two simple questions:
- How much of our cost base is made up of WINS work?
- How digitized are the WINS inputs today?
Plotting your company on a 2×2 matrix, ask yourself where it falls. The top-level categorization can be accomplished by looking at the cost base of the firm overall and then by function, and estimating the percentage of work that is WINS work. For example, software development, customer service, marketing, and R&D are just four areas with high levels of WINS work. Here’s a closer look at each competitive position.
In the Crucible
Industries with a high percentage of cost in WINS work and that are highly digitized are “In the Crucible” and must understand and embrace GenAI immediately. Software, entertainment, professional services, financial services, education, and others are In the Crucible, because competitors who adopt GenAI rapidly will be better, faster, and cheaper.
The tools of GenAI provide new, creative answers and expressions for everything from resumes to marketing. Think of it like the power of photography in portraiture. While portrait painting was previously available only to the wealthy – an artifact created slowly by highly trained craftspeople – photography expanded the market radically, transforming the entire idea of a portrait (think about a selfie) and its economics. In the hands of great talent, photography became a new form of art. Likewise, software development, script writing, film production, tax filing, accounting, etc., are likely to be under significant cost pressure – and in time will undergo wholesale transformation. These activities may or may not become totally automated, but just as you’d never hire an accountant today who doesn’t use Excel, having GenAI capability may become table stakes for many tasks.
Holding a Lever
Companies that are “Holding a Lever” are able to gain advantage in cost, time, and quality, even if their cost base is not heavily weighted toward WINS work and their customer end product or deliverable is also not WINS nor digitized. For example, Moderna has recently required all employees be trained in GenAI tools. They believe it is a fundamental skill to drive WINS worker productivity even though their product is a molecule or treatment intervention. In our GenAI learning community, we have found GenAI is excellent for supporting bid preparation when responding to an RFP. Speed of sale is a critical performance variable for all, even those firms with few WINS workers. Many SG&A functions, key aspects of R&D, and even the entire end-to-end product development and supply functions can leverage GenAI.
Next in Line
The category of “Next in Line” in our framework may provide an opportunity to take tasks that are not digitized today and digitize them to create opportunity. For example, many leading home décor companies are investing in what they are calling their “digital front door,” enabling customer engagement in the identification and purchase process. GenAI will enable new levels of customization to help customers take actions to envision home furnishings in much more realistic and imaginative ways, leading to a better experience, greater customer uptake, and far fewer returns for those companies that are in the forefront.
In the Balcony
For companies that are “In the Balcony,” we see low digitization and limited WINS work as characteristic of the value creation process today. These are often industries with high amounts of low-skilled labor or, when high skill is involved, the nature of the skill is more in the creation of a physical product or service. Figure 2 gives a sample of industries in each of the four quadrants.
Unlike much CapEx and technology spend that takes several years to see a return, GenAI, even at this early stage, can often be accretive to EBITDA within the year it is adopted, because the near-term productivity boost is so compelling. Over time, these initiatives may birth strategic investment opportunities to create defensible assets or competitive moats.
Legal and Risk Issues
When using these tools, it’s vital to have human review of important and high-risk decisions. Today’s GenAI models can sometimes “hallucinate” and give wrong answers. Therefore, for now, staff should use it to augment – not fully automate – high-risk tasks. In time, innovators will figure out ways to improve and augment the core models to improve accuracy. In addition, if you are using one of the open access models like ChatGPT, have clear policies as to when you will allow data and queries to be part of their knowledge base and when will you keep it private.
Where to Start
To get your GenAI initiative moving, we suggest the following approach:
1. Get fully educated on the entire suite of GenAI tools that can drive productivity, change, and innovation in your company and industry. This is not a classroom exercise. This is more akin to swimming. You cannot learn to swim by listening to a lecture or watching a video. Roll up your sleeves, dive into the pool, and swim laps. Learn how the tools work and what they can do.
2. The board/CEO should appoint a cross-functional team to start at the task, subprocess, and process levels for practical experiments and report on progress.
3. Have a cross-functional business/technology/finance team examine whether and where you could generalize the lessons of your early experimentation. Discover where you “hold the lever.”
4. Perform a strategic review of the cost drivers in the WINS category and current digitization to assess how urgent it is to invest broadly in GenAI. Find out if your company is “In the Crucible.”
5. If you discover you are “In the Crucible” or “Holding a Lever,” create a test-and-learn strategy linked to a six-to-24-month program of improvement. We believe that’s how much time you have before competitive intensity increases in your industry.
6. If you are “Next in Line,” get smart on GenAI and begin to move toward digitization of all WINS work so you are ahead of the curve. You are next to be “In the Crucible,” and if you don’t transform your industry, someone else will.
7. For those “In the Balcony,” continue to learn. While things are less imminent for you, GenAI tools will come along to make your business easier and faster, much like Excel and Word replaced calculators and typewriters.
Looking at GenAI Through Bifocal Lenses
As we said in the beginning, we advocate that all business leaders should put on their GenAI bifocals and look not only into the distance through that top lens, but perhaps more urgently and importantly, look through the bottom lens to see what’s near. Historically it has taken about five to seven years for a disruptive firm to change industry models. Five years after Uber’s 2009 founding, taxi medallion prices in NYC peaked at about $1,000,000 in 2014. By 2017 their value was $250,000 or less and continued down.
Firms with heavy reliance on WINS work need to act today to fend off stiffer competition and to overcome disruptive competitors within 36 to 60 months. Don’t be caught with high costs, old processes, a data disadvantage, fleeing talent, and expensive capital.
Paul Baier is CEO and Co-Founder of GAI Insights, an analyst firm dedicated to exploring practical applications of generative AI.
Jimmy Hexter is the Co-Founder of GAI Insights and is the Philip Van Horn Gerdine Professor in Global Business Professor of the Practice at Boston University’s Questrom School of Business. Previously, he was a Senior Partner at McKinsey & Co and was a Managing Partner at L Catterton.
John J. Sviokla, Co-Founder of GAI Insights has been a senior consultant with PwC, Diamond, and was on the faculty of HBS for over decade. His passion is understanding how computability of reality changes organizations and competition and that’s what he researches, writes, and speaks about.
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