Making GenAI Adoption "Sticky:" Practice #1

Building "enough" AI Literacy.

This post was originally posted by Julie Foss on Substack on January 23, 2025.

I recently joined a cohort of coaches in a program created by Shirzad Chamine, called “Positive Intelligence.” The program is designed to help participants build new neural networks that increase the stickiness of new habits and change practices.

The program frames our psyche through the lens of “Saboteurs” and “Sages.” According to Coach Shirzad, saboteurs are the voices in our heads that may have first appeared to protect us but are now stymieing our growth and progress. Sages are the positive forces that counter negative thoughts and enable us to navigate work, life, and relationships with objectivity and calm. You can read more about the program here.

As I apply these concepts to my personal practice, they also have me thinking about how they translate to the work I do with teams. Specifically, I have been wondering

What practices might be inhibiting or supercharging GenAI adoption in learning organizations?

Over the next few weeks, I hope to share what experience working alongside communities and following my own curiosity about this topic are teaching me. Also hoping to share some concrete strategies and resources for how teams can translate these practices into how they work and serve others. Looking forward to hearing what resonates and what you would push back on from your own experience. Let’s jump in to Practice #1: Building enough AI literacy.

Practice #1: Building enough AI literacy.

In Accenture’s Report on “Reinventing Enterprise Operations with Gen AI,” 78% of executives reported that training efforts are struggling to keep up with advancements in AI and GenAI (Chakraborty et al., 2025). Though the pace of new developments is for sure an inhibitor to building AI literacy, so is building enough of it.

By “enough” I mean two things:

First, if teams have had a single learning experience about GenAI, they may have overview understanding of what GenAI is and is not, how it works, and have some quick reference “dos” and “don’ts” around safety and ethics.

However, they likely do not have enough AI literacy to…

• talk about GenAI,

• connect when and how to use GenAI within their specific domain expertise.

• discern how GenAI could impact elements of their workflow, or

• have the ability to measure whether GenAI is having an impact on their investment of time, energy, and output.

This image was generated by Napkin.ai.

This does not mean a team is not capable of doing so. It simply means they do not have enough literacy to transfer GenAI literacy to practice.

Secondly, if GenAI literacy is concentrated among a few people in an organization, no matter how deep their expertise, that organization will not have “enough” AI literacy to impact the organization as a whole.

The power of GenAI lies in the partnership between subject matter and domain expertise.

If we put the entirety of the responsibility for GenAI subject matter expertise on one or two people without scaling that literacy across the organization, we will:

• one, burn out our SMEs,

• two, limit solutions to those within the domain expertise of the SME,

• three, miss opportunities to connect people across departments and job functions, and

• four, miss an opportunity to evolve as an organization, with shared vulnerability, urgency, and responsibility.

Again, this is not a question of capability. It is a question of bandwidth, scale, and shared commitment to change.

Tips for Translating This to Practice:

Consider approaching AI literacy as a continuum of literacy to fluency.

• Codify different levels of literacy with specific learning outcomes and key skills for each level.

• Map a progression of learning experiences that help teammates move from literacy to fluency.

• Consider AI literacy through the lens of general knowledge, job specific application, and organizational capability. Which roles need which learning and when?

• Develop multiple pathways for learning at each level and match people and experiences based upon their previous learning and next level fluency.

• Keep track of what learning translates to what impact and adjust learning progressions accordingly.

Consider the needs of different groups within your organization.

• Resources: Build repositories of resources that team members can access on demand. Seek their input in the creation of resources that don’t yet exist. Keep track of which resources are being used and which aren’t and dig deeper to understand why.

• Differentiated Support: Provide different pathways to learning to honor the preferences and needs of teammates. Consider a mix of low stakes mini-challenge type activities, shared experiences, and individual learning and practice. Set expectations, hold one another accountable to goals, and celebrate milestones around learning that is taking place across the organization.

• Balancing Personal Use with Organizational Impact: Noodles on questions like: “How will your AI literacy efforts build GenAI subject matter expertise in a way that complements and uplifts the domain expertise of individuals?” “What will be different in your organization if GenAI use is having an impact?” “What are the individual use cases that move the needle on desired organizational impact?”

Build AI literacy in community.

• Cohorts: Consider developing AI learning cohorts by role or cross functionally that provide opportunities for colleagues to learn in community. Set clear outcomes about what will be different for participants because they attended the cohort sessions. Whenever possible, create artifacts that create ripples of learning for those beyond the cohort. Communities have the power to communicate a sense of shared urgency, be a force multiplier of ideas for how to use GenAI in practice and empower team members with agency and the ability to anticipate shifts to their work before they happen.

• Peer-to-Peer Sharing: Creating informal opportunities for colleagues to share how they are using GenAI and the tools they are using to do so has a lot of potential benefits. Peer sharing is low stakes. It reinforces a culture of learning by democratizing ideas and demonstrating that they can come from any member of the team. Peer sharing also provides real time insight into the current state of GenAI use in your organization. Time spent in practicing in community will create transparency around use, inform next level learning, and build commitment to change in collaboration with others.

Building AI literacy is important. So is building “enough” of it. The greater your investment in individual capacity, the stickier AI adoption becomes for your organization.

Looking forward to the next post on Practice #2: Aligning AI initiatives and Organizational Mission, Vision, and Values. Between now and then, I would love to hear from you.

❓How are you building “enough” AI literacy in your organization?

References

Chakraborty, A., Tayob, Y., & Rao, B. (2025, January 15). Generative AI in operations for AI-Powered reinvention. Accenture. https://www.accenture.com/us-en/insights/strategic-managed-services/reinvent-operations-with-genai?c=acn_glb_aipoweredoperatmediarelations_14200178&n=mrl_0924

A roadmap for upskilling your workforce on AI. (2024, July 29). Guild. https://guild.com/compass/ai-upskilling-roadmap-for-hr-leadersPositive Intelligence. (2025, January 14). PQ® Program | Positive Intelligence. https://www.positiveintelligence.com/program/The graphic included within this article was generated by Napkin.ai.