What do we mean by “AI Transformation Readiness”?
When we launched Knownwell in 2023, one of our first efforts as a team was to create a proprietary diagnostic tool called the Knownwell AI Transformation Readiness Assessment. Our objective in doing so was to help leaders take a good hard look at their businesses to understand where they are more or less ready to handle the firmwide impact of AI.
Why make this piece of IP our first foray into value creation as a new business? Put simply: Our customers. Professional Services businesses, by logic and estimation, are expected to be disrupted by AI more than all other business models. As just one point of supporting reference, a recent Bain study shared NBER data predicting 41% of labor time in Professional Services can be automated using generative AI.
41% is a measure of work, not jobs, of course, and so the reality is that automation’s impact will cut across a larger percentage of total roles. By extension, and just watching things unfold in the market, it is not unreasonable to assume that–at SOME level–AI will “touch” (term of art these days, however ill-defined) 100% of employees, one way or another. It is also not unreasonable to assume that we are only seeing the beginning of AI’s impact. At present the majority of AI use cases are individual- and front-line productivity-oriented; we will soon see a wave of intelligent solutions that focus on higher-order, cross-functional, multi-handed workflows, in customer management, employee management, strategy and operations.
At the risk of adding to the over-use of a familiar refrain, Alphabet CEO Sundar Pinchai repeatedly reminds us that AI is “the most profound technology humanity is working on, more profound than fire or electricity or anything that we’ve done in the past.” One might go further to say AI is less a next technology than a force–for creation, disruption, and reinvention. Even if we discount broad statements such as these, it’s fair to say companies would be wise to recognize AI as the massive rogue wave that it is and ready the organization for its crashing on the beach.
Are companies ready?
Goodness no.
We’ve seen plenty of evidence in the secondary research suggesting organizations are not ready. For starters, data tells us C-suite executives are uncertain about their organization’s expertise to implement AI; have nagging concerns about data security and technology bias and accuracy; and, are not yet comfortable using data from advanced analytics systems. Beyond their personal expressions of blind spot, executives have entire organizations of increasingly curious and expectant employees to consider. The broader employee population has a mixed perception of the opportunities and threats of AI–with internal messaging about the technology “enhancing us all in our roles” conflicting with external headlines increasingly reporting job losses directly attributable to the implementation of AI.
Uncertainty and a perception of not-readiness should come as no surprise, as we are still very much at the front end of a migration we don’t entirely understand. Even so, time is our enemy, and we should do everything we can to prepare the organization for AI–for all its threat and promise.
What does “readiness” look like?
Through a combination of deep primary and secondary research, and the incorporation of broad expertise on the part of the team, Knownwell created it’s AI Transformation Readiness diagnostic to examine the state of enterprise capability in five major categories:
- Strategy and Knowledge
- Culture and Leadership
- Talent and Operations
- Data and Technology
- Governance and Responsibility
Readiness can be achieved when companies have systematically examined and upgraded practices in these five areas.
Having run the assessment for some time now, we are beginning to see noteworthy patterns in company readiness. To be clear, we don’t present the following as unassailable statistical analysis; instead, it is a window into what we are seeing and hearing from leadership teams we interact with, intended to prompt reflection and thought on the part of others.
What are we learning from the readiness assessment?
Companies don’t score exceptionally high in the assessment overall. Participants tend to see themselves hovering below or at a score of 2 out of 5, or “emerging” on our scale.
The feeling is that, at the highest level, most leaders see their organizational readiness state as “a little better than immature” and “not all the way to competent.” This seems to us a fair and honest appraisal, again given we are at the front end of a broadly impacting yet still emerging wave.
There do seem to be noteworthy patterns within the five broad categories, where participants score themselves mildly above or below that overall average:
1. Data and Technology: (Relative) Strength
Companies tend to be (or perceive themselves to be) more ready on average (though still between “emerging” and “competent”) in Data and Technology than the other categories. Assuming the self-appraisals to be a fair representation of reality, this is good news as having a strategy around right datasets to use for creating new value and the right steps to modernize the tech stack, data science capabilities, and data management approaches are surely all long-lead-time (and, often, high-expense) items. It is further heartening to observe in this category that companies are feeling relatively more positive about their data and cyber security protocols and processes, as these will be central to practice and regularly tested in the coming months and years.
Where our experience with customers leads us to cautionary conclusions in this category is around alignment. Are the expensive and time-consuming data and technology projects all connected to–or, preferably directly driven by–an updated (AI-influenced) view of firmwide strategic and operational goals for the next 18-36 months? And, particularly in light of the spectacular flow of AI-powered innovations newly available with each passing day, are all the investments and work streams in the data and technology project plan in fact necessary? In some cases AI is offering clever ways to work around a full-blown end-to-end integration standard.
2. Strategy and Knowledge: (Relative) Strength
Participants also tend to score readiness (relatively) higher in this category. Often initially at the direction of boards or investors, many companies are taking the time to thoughtfully review current and potential AI-driven change in their markets–customers, competitors, partners, regulations, etc.—and to convene the leadership team for socialization and workshops to play out the impact of those changes on strategy, cataloging long-standing customer pain points and opportunities that can at last be addressed, and X-raying business operations to find new sources of efficiency and effectiveness. In addition to updating their appraisal of AI’s impact on strategy, board and executive leaders are also investing in upgrading their own levels of AI acumen and awareness, either individually or through corporate task force and education initiatives.
Where confidence around Knowledge and Strategy tends to dissipate is when the body in question is the larger employee base, rather than just the leadership team. AI acumen across the firm varies wildly, as one would expect, and there’s a real and pressing opportunity for companies to deliver training on the basics, in order to set parameters early on prudent and careful use. Clarity on the strategy and operational implications of AI, if it exists, primarily does so only at the highest levels of the organization. Mostly this is due to the fact that leadership teams either haven’t gotten there yet themselves or have a view but are being deliberate about the cascade.
3. Talent and Operations: (Relative) Weakness
Per the prior conclusion, the work of analyzing AI transformation implications on talent and related operations tends to be a trailing consideration for most. While not simple to resolve, these are all questions that must be addressed sooner rather than later, as employees will be seeking answers as awareness grows around the potential magnitude for AI-driven change in how their work gets done.
- What workflows will be affected, and how much will they change;
- What departments, teams, and roles must be assessed and redesigned;
- How will skill requirements, performance expectations, and productivity standards change; and,
- How will all of these changes roll up to a new approach to workforce planning and talent stewardship?
While it’s true that the disruption of talent standards will vary by industry vertical, strategy, and offering, even at the “light” end of the spectrum, given the far-reaching influence of every-day AI alone, the change management work will be substantial. For those on the “heavy” end (we think again, here, about much of Professional Services), the business transformation effort ahead will be the central focus of People and Operations teams and leaders for years to come.
4. Governance and Responsibility: (Relative) Weakness
Within this category, and true to time-honored tradition, companies self-assess better on Governance than they do on Responsibility.
On the one hand, executive teams and their boards have by now spent meaningful cycles pondering potential AI-driven risks to the business:
- Where could our strategy or business model be compromised?
- Where across all our many contracts and relationships might we experience exposure?
- In what ways might the business experience financial risk or instability?
On the other hand, the more existential questions surrounding AI get less attention. Given the vast rebalancing of work ownership, machine vs. employee, leaders should be deliberately formalizing their stance on human agency. Similarly, questions of societal and environmental obligation remain on the to-do list. While, by their very nature, these questions tend always to sit more on the “to-do” than “done” list, the accelerating sweep of AI presents a critical occasion for re-examining commitments to duty of care.
5. Culture and Leadership: Mixed
Assessment in this category tends to be fairly variable across companies and–interestingly, in some cases–within companies. This strikes our team at Knownwell as something between curious and concerning as it is often the strength of a leadership team and health of a culture that see companies through the most turbulent times.
Even in light of the variability, though, the leadership capabilities tend to score a bit higher than the culture management ones. Reading between the lines and listening to discussions among customers, there is a sense that companies feel they have good people in load-bearing roles who have a strong ability to lead, but perhaps they haven’t yet equipped those people with the guidance and tools to do so, in light of what’s coming.
The culture management question that regularly scores lowest is around culture definition. Essentially, have we stepped back and asked ourselves whether our values are still the right ones for the times, and, assuming so, are we clear on how they influence successful outcomes? And, perhaps even more importantly, have we consistently engaged the organization around our values and culture, such that we feel confident they will propel us for the work ahead.
Ready to gauge your AI Transformation Readiness?
Reflecting on what we’ve learned so far, companies have plenty of readiness work to do as we embark on what will be a years- or decades-long transformation of business to its next form and structure. Rome wasn’t built in a day, and neither shall the modern AI-ready/AI-powered business. But the building must begin; and, the smart builders are the ones who are considering the full array of potential business impacts and then prioritizing strategic, procedural, operational, and structural change.
We invite you to use the AI Transformation Readiness Assessment to help with the work. If we at Knownwell can help you in any way, including a discussion of your assessment results or further engagement around readiness planning, please contact us.
