Editor’s Note: Today’s post is by Stephanie Lovegrove Hansen. Stephanie is the Vice President of Marketing at Silverchair.

For the last two years, AI has dominated our industry: conference agendas, whitepapers, webinars, and even these pages. It feels like AI is everywhere. And yet, organizational adoption of AI is still far from universal, with some companies still outright banning the use of AI tools.  

Recent research from McKinsey also shows a deepening divide in AI adoption and mindsets between leaders and employees, with a 31% gap in the belief that their company has a high level of AI literacy. Whether we use AI individually or organizationally, however, it’s not going anywhere. That’s why it’s becoming increasingly important to facilitate a deeper understanding of AI broadly, as Roohi Ghosh recently pointed out: 

The way to overcome this resistance is not technical proficiency, but a commitment to fostering AI literacy and creating spaces to openly discuss its challenges and implications. As we stand on the cusp of an AI-driven future, it is essential to move beyond resistance and engage thoughtfully with these technologies.

So how can organizations facilitate safe and comprehensive engagement with AI? And how can individuals within those organizations engage and advocate for their own AI literacy? 

I’ve been fortunate to experience early adoption and enthusiasm for AI at my organization and have hosted a number of AI-centered speakers and discussions in the last few years. Many common themes have arisen through those experiences, with key actions for both organizations and individuals to foster AI adoption. 

For Organizations

Whether you’re an early adopter or a late follower, AI isn’t just about competitive advantage — it’s becoming a necessity for the workforce of tomorrow (and arguably, today). Research from the World Economic Forum showed that more than half of hiring managers already say they wouldn’t hire someone without AI literacy skills. Organizations that fail to leverage AI capabilities risk not only missing out on efficiencies and product differentiation, but also jeopardize their ability to recruit and hire the best talent. Slow adoption may adversely affect retention for current employees, too: McKinsey research showed that 3x more employees are using gen AI for a third or more of their work than their leaders imagine. 

Successful implementation requires more than just purchasing new tools, however. It means laying out strategies for adoption and delivering a clear vision of how AI supports your organization’s goals. Once you have your “why,” then you can support adoption through specific organizational efforts, like the ones detailed below. 

Provide training & resources 

If you want employees to prioritize AI adoption, you need to first provide them with the tools to do so in a safe environment. To help first-time adopters, it’s also useful to show easy first steps to AI application through training programs and prompt examples catered to different roles and technical backgrounds. This might include: 

• A high-level AI literacy course for all employees
• Advanced technical training for IT teams (with licenses for tools like Copilot or Cursor)
• Specialized training/resources for departments with unique AI applications (for example, Silverchair’s internal AI tools contain pre-built prompts for different role-types, such as Developer or Business Analyst)

Providing a centralized knowledge base with tutorials, case studies, and best practices and offering clear venues for employees to ask questions and share their experiences will help build the foundation for AI literacy. 

Foster a culture of experimentation 

Current AI solutions and use cases are best learned through hands-on experimentation. Encourage teams to test AI tools in low-risk environments — create sandboxes and gated instances where employees can experiment without fear of failure, allowing them to build confidence and discover practical applications relevant to their work. 

This also means making employees feel protected, which AI policies can offer. In a May 2024 webinar, Barbara Kline Pope, Executive Director of Johns Hopkins University Press, shared that she was surprised by the hesitancy of staff to actively experiment with AI tools despite encouragement from leadership, only to learn that they felt reluctant in the absence of firm guidelines, worried that they’d inadvertently leak organizational data or breach some unwritten rule. After putting a formal AI policy in place, the team more confidently waded into learning about these tools and trying them out in various scenarios.  

AI policies are quickly becoming a staple of employee handbooks, and recent guidance advises that “an effective AI policy can cultivate responsible innovation, build trust, and assist in a smooth transition into an AI-driven work environment.” A clear policy can also help your team stay aware of ethical and privacy considerations, with guidance for new users on how their data is used by these systems and what your organization’s specific requirements are on entering proprietary information into LLMs.  

At the same time, the culture of experimentation extends to the policies themselves. Be willing to adapt the policy as you learn more, as models change, and as your team becomes more AI literate. In a recent Scholarly Kitchen interview, Aaron Wood of the American Psychological Association noted that they “started with a quite restrictive policy, but then opened up by finding the right tools for staff to use.” (This whole interview is a great case study of developing an AI approach.) Given the pace of change in AI offerings, flexibility ensures you continue to take advantage of the latest capabilities while protecting your organization. 

Identify and support AI champions 

Once employees are comfortable experimenting, offer venues to share their learnings and use cases with others within the organization, to help spur their own experimentation. Those most willing to dive in and share with others will be your early adopters and change champions.  

As in all change management scenarios, identifying your early adopters and influencers is key to wider adoption. Seek out your enthusiastic early adopters as AI champions within each department and involve them in communication plans and best practices development. Give these individuals the tools (and time) to provide peer support, share success stories, and help troubleshoot problems as they arise. These people also serve as good feedback loops to surface employee concerns, hesitance, or additional support needs. This can look like focus groups, early access to new tools, lunch-and-learns, or added opportunities for professional development around AI solutions. 

Internal champions can be identified through volunteer opportunities like pilots and internal demos, or by working with managers to engage their teams on department-specific applications and sharing uses cases they’ve discovered.  

Demonstrate value  

Leveraging your early adopters, kick off your AI adoption efforts with high-impact, achievable pilot projects that clearly demonstrate AI’s value. As they progress, celebrate and communicate these successes to build momentum and excitement across the organization. 

Establish metrics to track AI adoption and literacy improvements – you may even consider writing AI skills into employee and team goals to ensure accountability.  

As with any new skill or toolset, it will take time to develop a strong foundation of AI skills throughout your organization, but at this moment in AI’s meteoric rise, the most important thing is to foster a workplace where AI tools enhance human capabilities rather than intimidate them, giving your team enough hands-on experience to make informed strategic decisions. 

For Individuals 

AI skills are quickly moving beyond being a “nice to have” in the workforce and becoming a base-level requirement. Amidst the current turmoil in the economy and disruption in our industry, it’s important to upskill and reskill your own offerings to futureproof your career.  

Know thyself 

Are you AI curious? Phobic? Blissfully ignorant? Wherever you are on your AI journey, it’s good to get a sense of your current position. What DO you know about AI – how it works, what it can and can’t do, possible use cases? And where are your gaps? Are you anti-AI? Why? How can you mitigate your worries / distaste? Once you have a good idea of your current position, you can better identify the best starting points and growth areas for your own AI literacy. 

Take matters into your own hands 

Does your organization offer any of the resources detailed above? If so, take advantage of them! If not, there is no shortage of free resources for training and development around AI. Sign up for newsletters, attend webinars, watch explainer videos. And importantly, use the tools themselves. See what they can do firsthand. If your organization prohibits the use of AI at work, explore offerings outside the office to continue building your skills and overall literacy.  

Have fun with it 

Things I have used AI for in my personal life: planning a trip to Puerto Rico with my 11yo son, writing a sonnet to accompany my cocktail competition entry, and requesting a series of questions to help me uncover my personal growth areas. AI doesn’t have to be all productivity and copywriting — it can also just be fun. Experiencing AI in low-stakes, trivial environments can be a great way to learn about its strengths and limitations as well as to stimulate ideas about how else it could be applied. For example, the above examples taught me how iterative interactions with GenAI tools (asking a series of questions to refine results) can vastly improve the outputs. 

Lean into your human skills 

As AI adoption rises, certain skills become table stakes, making those uniquely human skills all the more valuable. I moderated a session at SSP 2024 on the place for human skills in the age of AI, in which the panel identified everything from the art of curation to the power of community building and beyond (further reading here). On an individual level, this can look like building strong interpersonal connections with your network, deepening your management and communication skills, and leveraging empathy as a way to connect not only with coworkers but also with customers and audiences.

All this said, each organization is different, with different limitations and needs based on size, focus area, and growth plans. Same with individuals. Often, the best place to start is finding a peer (organizational or individual) who seems to be a couple steps ahead of you on their path and ask them how they got there and what they recommend.  

In that spirit, what approaches have you found valuable in encouraging AI literacy, either within your organization or for yourself? What is your favorite tool (and why is it Claude)? What would you recommend to someone just starting to wade in to AI understanding? 

The AI revolution isn’t going anywhere, so it’s up to us as organizations and individuals to engage in ways that build our overall literacy so we can make informed decisions and continue to be part of the conversation.