In the rush to leverage generative AI, enterprise tech leaders find themselves in a balancing act. They need to push for more adoption and experimentation while keeping a firm grip on data security and avoiding mishaps from careless usage. It’s an “open the doors, but keep a leash” kind of approach, where employees are encouraged to explore tools like DALL-E and Claude but warned to think twice before diving in. So how can leaders balance these competing demands without stifling innovation?
ROI Comes from Mass Adoption
Let’s face it: the true ROI of AI emerges when everyone’s on board. High-performing organizations don’t dabble—they adopt these tools widely, aiming to equip the whole team. A National Bureau of Economic Research published a paper finding generative AI boosted customer support productivity by 14% on average, with an even bigger impact on newer employees who saw productivity gains of 34%. Not only does AI make the average employee better, it can even turn lower-performing team members into stars.
The straightforward case for AI adoption goes beyond just broad use; it also involves job-specific AI tools. Project management platforms like Smartsheet, Asana, and Monday.com now use task data to power schedules and projections. Meanwhile, CRM systems like Salesforce and HubSpot offer sales reps insights into hidden prospects. In other words, companies that hold back on rolling out AI could soon be at a clear disadvantage, lagging behind competitors who are taking full advantage of the technology.
This potential has some leaders leaning toward a permissive approach. Harvard’s Paola Cecchi-Dimeglio advocates for a culture of open experimentation, arguing that leaders should “encourage employees to experiment with AI and share their experiences. This can demystify AI and highlight its practical benefits across different roles and functions.”
But while leaders may want to empower employees to explore, there’s a big gap in employee confidence.
In a recent survey by Skillsoft, 74% of employees said they don’t trust their company’s training for AI, and only 40% of IT leaders say they even have formal programs to support AI adoption. The “innovation fog” that clouds employee confidence is just as frustrating for managers, who face questions like, “Which training programs make the most sense? Which vendors are reliable? And how will we measure success?”
The good news is that tried-and-true strategies can help cut through the fog. Here are a few ways leaders can accelerate meaningful change and help AI adoption get off the ground:
- Offer Domain-Specific Training: Those PM tracking tools are fundamentally different than those sales platforms. We should train people accordingly. As Cecchi-Dimeglio suggests, tailor programs and guidelines to specific roles.
- Collect & Celebrate Wins: Once you’ve got training programs going, market them. Highlight early adopters’ success stories in a regular newsletter to spark broader interest.
- Nurture a Community of Champions: One forward-thinking leader holds a weekly “GenAI & Citizen Developer” webinar, where employees share tips on tools they’ve tried. For most, just listening in is enough to get them inspired to experiment.
Safety Without Slamming the Brakes
With all this momentum, there’s a reality check: more users and more tools bring more risks. These risks range from data privacy breaches to empowering bias and even criminals. Leaders are responding in various ways to ensure they capture AI’s benefits without compromising safety.
For example, President Biden’s “Executive Order on AI” led to the NIST AI Risk Management Framework, a hefty playbook that defines 72 (!!) controls to “govern, map, measure, and manage” AI. Meanwhile trade associations are also weighing in, such as the ISO which offers three global standards totaling over 100 pages (!!) on AI governance. Then, there are the tech company alliances and individual vendors offering their own frameworks.
With the onslaught of so much governance guidance, it can be hard to know where to begin. Fortunately, we can start with a few common-sense fundamentals:
- Partner with AI Vendors: Whether you go with OpenAI or Google Gemini, vendors offer solid guidance on data security and governance. Most will throw in enterprise governance features—and even bulk discounts for larger companies.
- Publish Simple Usage Guidelines: People want to do their jobs well. Give them straightforward guidelines so they can experiment safely. Many public resources are available, but often they’re overly technical. A clear, company-specific guide can go a long way.
- Inform, Don’t Enforce: Often, teams don’t realize they’re taking risks. Security teams can run scans, then notify managers with a risk assessment and safer alternatives. For bigger projects, offer governance specialists as advisors. With “Governance as a Service” (GAAS), you enable safety without clamping down on innovation.
- Offer Budget Incentives: If teams are using unauthorized versions of tools on their own budgets, offer discounts on safer, vetted alternatives through centralized procurement. Imagine the response when governance teams make safer options both cheaper and easier to use.
You Can Have It Both Ways
AI brings unprecedented opportunities—and equally unprecedented risks. The typical reaction is to either charge full steam ahead or hold back in caution. But transformational leaders know the real power comes from a creative balance: fostering innovation with safety firmly in the mix. When leaders can pull this off, they unlock AI’s potential without compromising their enterprise’s integrity.
