A.I. Literacy That Sticks

The Human Side of Change

Change is something everyone must manage regardless of where they are from or what language they speak. It is universal. Teams reorganize, leaders rotate positions, markets shift, and people are asked to deliver while the ground moves under them. For many of us, that story can be very personal. I think of my own personal change, from the discipline of the military to the ambiguity of business school, then into consulting in a new city which required a new way of thinking and rhythm. Each transition demands the same core muscles; adapting quickly, learning what matters, and staying focused.

Inside organizations, it looks ordinary but is actually heavy: new org charts, new systems, new ways of working that weren’t on last quarter’s plan. What makes those transitions easier to make stick is three simple ideas worth calling out:

• Clarity: why this change, why now, what does it mean (for me & for the business)‍
• Confidence: knowing what good looks like, how to talk about it, and how to bring it to life ‍
• Commitment: seeing the work through and delivering what we said we would

These ideas have been tested in high-stakes work, shaped in the classroom, and polished through years of hands-on consulting. We apply these “three ‘C’s” to the loudest wave of change that we are all navigating today: Artificial Intelligence.

Momentum doesn’t stall on models or data, it stalls on human understanding: what AI is, how it changes the job, and how to use it safely. This adoption hurdle is the primary reason for failure; one MIT analysis noted that 95% of enterprise AI pilots report zero measurable business return, citing employee resistance and skills gaps as key factors^[i]. Until executives, managers, and teams share a common language, organizations can’t move from curiosity to confident application. BizLove’s human-first approach bridges that gap. We get under the hood of complex problems and make them mutually understandable, wherever you sit in the organization.

We’ve seen this play out in recent work we’ve been delivering at BizLove. The Global HR team of a top-tier life-sciences company asked us to design and facilitate an offsite summit on AI literacy. The ask from the client was simple:

Help our leaders grasp what AI means for their people today and how to build internal capabilities to integrate AI across the office, labs, and manufacturing sites.

What we walked into

Classic signals showed up immediately: pockets of excitement, low confidence about where to start, and no clear “where do I go for help?” path. Leaders wanted value without taking on any additional risk; managers wanted playbooks, and employees wanted to know what would change in their day-to-day.

How we approached the ask

1) Assessing baseline & readiness (fast, human-focused lenses unique to different roles)‍

• A short pulse (survey + interviews) to capture current usage, various levels of confidence, and friction by audience (executives, managers, employees)
• A “job-family impact scan” to identify a handful of workflows where AI was both safe to deploy and valuable (policy Q&A, SOP drafting assist, shift-handover summaries, recruiting JD drafts). We see this lens used in the marketplace too, with companies like Walmart and its “MyAssistant” tool^[ii] and ServiceNow’s HR platform^[iii] using scaled AI agents to gather inputs, understand policies, and refine requisitions
• A simple readiness heatmap and “first ten” micro use cases with owners

2) Facilitating learning (a Summit with clear and accessible goals)

• Leadership framing: helped leaders define how AI drives unique business value, the risk exposure to be aware of and the story to rally our people around.‍
• Live demos: short, role-specific case studies provided through demos (HR partner, plant supervisor, lab lead).‍
• Keynote speaker: scouted a top AI voice in business leader to give a tailored AI speech to industry and specific challenges‍
• Breakouts: “from idea to workflow”—each group mapped one use case using a template: purpose, guardrails, inputs/outputs, quality checks, RACI, and success signals. Making AI tangible in this way it what often breaks down barriers to its adoption and its effectiveness.‍
• Operating model clarity: where questions go, who decides, and how issues escalate.‍
• Measurement:  defined metrics that matter to adoption and business value, delivered a scorecard to be maintained over time.

3) Making it stick (the right tools to empower people across the org)

• Manager playbooks (when/why/how to use, quality gates, how to coach).
• Micro-learning modules for employees (prompting basics, safe use, “when to escalate”). This focus on foundational skills is a proven adoption driver ‍
• Mastercard, a global credit card company, for example, made mandatory data policy training a prerequisite for AI tool access, finding that adoption grew once employees understood how to "ask prompts correctly".^[iv]‍
• Portal v1 on SharePoint as the home for toolkits, FAQs, office hours, and success stories.
• 0–30–60–90 plan to carry momentum beyond the event.

What Changed

Clarity → Confidence.

Leaders left with a shared narrative and guardrails; managers had practical playbooks; employees knew the first, safest places to try AI in their role.

Pilots → Practice.

Those “first ten” micro–use cases went from slideware to owned workflows with simple quality checks. Early wins were socialized through the portal and the monthly readout.

One-offs → Operating rhythm.

Leveraging the measurement scorecard and structured touchpoints, adoption stopped being “someone’s project” and became part of how the team runs.

Results

Within the first quarter, the organization saw a visible lift in confidence, sustained use of internal AI champions and measurable time saved in a handful of targeted workflows, a result that mirrors broad industry findings.

A 2025 SAP survey, for example, found that employees save, on average, nearly five hours per week using AI tools (and 77% say they use that reclaimed time to do more job-related work).^[v]  

Why This Matters for You

If you’re standing up AI integration & literacy in a regulated, technical environment, the path is the same:

• Start with people
• Make it role-based
• Prove it in real work
• Give teams a simple way to keep improving
• Measure what matters

That combination of learning by doing the work, having clear roles, and measuring what matters, motivated people from simply being curious to taking confident ownership in the integration of AI.  

‍

^[i] "6 Hard Truths Behind MIT's Finding That 95% of AI Pilots Fail." CloudFactory Blog, CloudFactory, https://www.cloudfactory.com/blog/6-hard-truths-behind-mits-ai-finding

^[ii]Williams, Lauren C. "Walmart rolls out generative AI-powered assistant to 50K employees." Retail Dive, 19 Oct. 2023, https://www.retaildive.com/news/Walmart-generative-AI-tool-My-Assistant/692402/

^[iii] B-Johnson, Keith. "Workflows, Generative AI, and Agentic AI: Choosing the Right Tool for Business Transformation." ServiceNow Community, 21 Oct. 2024, https://www.servicenow.com/community/hrsd-blog/workflows-generative-ai-and-agentic-ai-choosing-the-right-tool/ba-p/3376545

^[iv] Krazit, Tom. "How Mastercard encouraged AI adoption with training and data." Runtime, 23 Sept. 2024, https://www.runtime.news/how-mastercard-encouraged-ai-adoption-with-training-and-data/

^[v] Krauss, Ann. "AI Saves Employees 5 Hours A Week — But Who Really Benefits?" Forbes, 28 July 2025, https://www.forbes.com/sites/sap/2025/07/28/ai-saves-employees-5-hours-a-week---but-who-really-benefits/