The challenge for most corporate leaders today isn’t whether or not to engage with generative AI, but how best to prepare for, manage, and communicate this enterprise transformation.
How might you develop smart enterprise policy around this technology in a way that doesn’t hobble experimentation and innovation — while still providing guardrails?
If that’s where you are at this moment, then this article is for you. I’ve chosen five domains of GenAI readiness you should consider on your planning and policy-setting journey. They are:
- HR and People
- Legal/IP
- Governance
- Strategy
- Product/Design.
These five zones of readiness, I believe, are the crux issues that all orgs on the GenAI adoption path need to engage in. (Data and technology policy are outside the scope of this article, as it has been well-covered by other outlets. )
• • •
1. HR: People & Skill Readiness
Let’s keep it real. The #1 concern of knowledge workers everywhere is the unanswerable question: will I be augmented, or replaced? Any tech worker old enough to have lived through prior automation waves either saw roles eliminated, or unbundled and transformed. For now, it appears augmentation is where it’s at, which makes the #2 question for employees and managers, “What skills do I need to stay current and advance my career?” Clearly, tech workers (coders and data analysts) must keep pace with an endless treadmill of certifications. For the rest of us, comfort and awareness of GenAI is table stakes. Prompt engineering, important now, will diminish as the prompt-less trend mentioned above unfolds.
Scott Belsky, Chief Strategy Officer at Adobe, said something interesting in one of his recent Implications blogs:
“Perhaps taste and ingenuity are far more important than skills in the age of AI? Taste seems more scarce these days, and is an increasingly differentiating trait as skills-based productivity is offloaded to compute. This realization makes me contemplate new structures and incentives that companies should use as they hire the next generation of talent or devise ways to engage tastemakers.”
I highly commend the full article and Scott’s blog as a useful resource for forward thinking in the AI age. But my favorite piece to help us all reimagine workplaces and enterprise cultures as we move boldly forward into our GenAI powered future is this piece from California Management Review entitled, “Designing the Intelligent Organization: Six Principles for Human-AI Collaboration.” It’s a great point of departure for HR and other organizational leaders to begin adapting to a world of human and agent workers. A helpful video summary of the research by one of the authors is excellent. (See below.)
2. Legal: IP & Data Readiness
Here’s a dark ideation prompt… “How might we… get sued?” In the current environment, this is a very valid question worth some discussion. With cases like the New York Times lawsuit against Microsoft and OpenAI for allegedly training their LLMs on Times content, plus the rise of organizations like FairlyTrained advancing the ability to verify LLMs as copyright infringement free, every executive considering AI adoption must be considering how to protect their corporate backside. Even naive employee use of GenAI might inadvertently exfiltrate company company IP or trade secrets as “prompt leakage,” and that data may become used for training some LLM’s next version. Beyond these scary concerns, what about simply protecting the legitimate AI inventions of your organization yet to come?
The best resources on IP and data readiness come from the World Intellectual Property Organization, or WIPO. The first one is called “Generative AI: Navigating Intellectual Property,” and it addresses very specific mitigations your organization can take for the kind of risks I enumerated above. The second report, “Getting the Innovation Ecosystem Ready For AI: An IP Toolkit,” is an excellent grounding resource for strategically preparing for the AI-driven innovation future. It advocates for taking proactive measures in order to adapt your own IP ecosystem for this new age of AI. This includes things like updating your legal frameworks, providing clear guidance on IP protection for all your AI inventions, and considering the unique challenges posed by AI as an inventor itself.
3. Governance: Policy Readiness
With benefits come risks. And risk mitigation requires oversight and accountability. For big enterprises, the trend may be to distribute GenAI oversight to regional officers, with coordination linkages between them. For example, last month the United States Federal Government set policy requiring every major department to have a Chief AI Officer. For most other mid-sized and large enterprises, the trend has been to create a singular exec-level position. For example, as reported in American Banker, U.S. Bank recently transformed its Chief Digital Officer into a Chief AI Officer. (He’d been the Chief Innovation Officer before that.) More broadly, we’re seeing and will continue to see cooperation between enterprises and governments. EU countries are setting standards. And just signed April 1st between the US and UK is an agreement to form a partnership on AI safety.
My recommendation, if you’re starting the journey towards AI governance, is to pay attention to the work of the folks at National Institute of Standards and Technology (NIST) and their Risk Management Framework, or RMF. NIST’s Trustworthy and Responsible AI Resource Center created the RMF and updates it regularly, so I recommend you bookmark their site, or subscribe to their email updates. It’s dense, but not too technical, so it’s consumable by your full management team. It might help frame your conversations as you put AI governance in place. They even provide a “Playbook,” available as PDF or Excel spreadsheet, which is great to distribute at board meetings and speak with a common language.
4. Strategy: Future Readiness
As a young philosophy major, I remember the day it occurred to me that humans can observe velocity (change over time) but they cannot see acceleration (change in velocity). To quote the futurist and inventor Ray Kurzweil, “We won’t experience 100 years of progress in the 21st century. It will be more like 20,000 years of progress (at today’s rate).” As exponential technologies converge and co-accelerate, the directions of these vectors become highly unpredictable. So, what is one to do?
The innovation world might have an answer: open innovation. Perhaps some humility and transparency around our collective ignorance in the face of this massive change is best. Dave Snowden, creator of the strategy framework called Cynefin famously said the solution to complex social problems is thousands of simultaneous experiments. In other words, we need our best minds thinking and working together on this. In a recent podcast, Todd Olson, CEO of the product analytics firm Pendo, talked about how his company’s AI approach was forged through a series of company hackathons and events. This led to breakthroughs now coming to their product line and established a baseline for staying future forward. Keep experimenting — but consider doing it with more collaborators.
My recommended read on the strategic front is “16 Changes to the Way Enterprises Are Building and Buying Generative AI,” by Sarah Wang and Shangda Xu, two analysts at the venture capital firm Andreesen Horowitz. As much as anything can be “current” on a topic like GenAI, their work is fresh and provides ample support for strategic conversations and future positioning.
5. Product Design: Experience Readiness
Bleeding edge technology like GenAI is amazing, but if the user experience remains where it is today with dull text interfaces, clunky speech recognition, and impersonal customer service interactions, the promise of this technology can quickly vanish as consumer and employee sentiment turns against it. Research firm Valoire found in their 2024 study on AI user perceptions that trust in AI hinges on specific words. For instance, “assistant” is preferred, as opposed to “co-pilot.” Meanwhile, product teams are already seizing the “prompt-less” AI trend as their new north star, which is where chat interactions go away and systems simply infer what we need next, without becoming Microsoft Clippy 2.0.
One of the best recent pieces I’ve come across is Accenture’s very forward-looking “Tech Vision 2024.” I appreciate their broad look at not just GenAI, but all the intersecting technologies which they argue make it imperative to keep humans deeply in the loop. They advocate developing a holistic approach to integrating AI into your enterprise fabric, with a heavy emphasis on enhancing both your stakeholder capabilities (employees, customers, partners, vendors), and of course ensuring ethical uses of technology, while simultaneously preparing your workforce for a future where AI plays a central role in innovation and problem-solving.
