When I explain generative AI to non-technical stakeholders, I use the example of the abacus. My mother was an accountant in China in the 1980s and used an abacus every day to calculate her books. The abacus didn't replace her knowledge of accounting, it simply sped up the process and freed her to focus on higher-order thinking. I frame generative AI the same way: it is a tool that can accelerate and expand human work, but it still depends on human judgment and oversight to be accurate and meaningful.
I tell our clients to think of generative AI as the world's most advanced autocomplete. It isn't 'thinking' in the human sense. It is making an incredibly sophisticated prediction about what word or line of code should come next, based on analyzing trillions of examples from the internet. This simple analogy immediately helps non-technical leaders grasp both its immense power and its inherent unreliability. Your phone's autocomplete can suggest a word that fits grammatically but is completely wrong for the context. Generative AI does the same thing on a massive scale, which is where 'hallucinations' come from. This is why we insist on human oversight. Our developers use AI to accelerate tasks like drafting boilerplate code, but a senior engineer must always validate the output. The tool has no real-world judgment or accountability, so the expert in the loop is essential.
In 2014, standing on a Manhattan street corner frantically waving at occupied taxis while running late to a crucial meeting was an accepted part of doing business. The inefficiency was so normalized that we built entire buffer periods into our schedules just to accommodate the unpredictability of urban transportation. Then Uber arrived, and within 18 months, that same street corner experience felt as antiquated as using a rotary phone. Today, B2B sales is experiencing its identical inflection point. And if you're in sales leadership, your response to this moment will likely define the trajectory of your career for the next decade. The early adoption data reveals a performance chasm that's expanding rapidly. According to Salesforce research, 83% of sales teams with AI saw revenue growth in the past year versus 66% of teams without AI. This 17-percentage-point gap represents the difference between market leadership and competitive irrelevance. Organizations implementing AI Sales Intelligence are seeing transformational results: 80% of sales reps say it's easy to get the customer insights they need to close deals versus 54% without AI, with deal velocity 38% faster and a 45% increase in seller efficiency through AI smart prioritization. The comparison to Uber's transportation revolution isn't hyperbolic—it's structural. Both industries faced the same fundamental challenge: massive inefficiency caused by information asymmetry and coordination failures. Pre-Uber transportation suffered from three critical gaps: — Visibility gap: No real-time awareness of available resources — Coordination gap: Manual, inefficient matching of supply and demand — Intelligence gap: No predictive insights about demand patterns These are precisely the same gaps plaguing sales today. Static territories lead to unequal opportunities, prioritization problems, and coverage black holes, creating misallocated sales quota capacity causing gaps in market coverage and missed revenue. Just as Uber didn't just improve taxis but reimagined transportation infrastructure, AI Sales Intelligence isn't improving traditional sales, it's reimagining revenue generation infrastructure entirely.
When I explain generative AI to non-technical stakeholders, I frame it as a collaborator, not a replacement. The analogy that resonates most is comparing AI to a musical instrument. The instrument can play notes, but it takes a musician to create music with meaning. AI can generate text, images, or insights, but it takes human context, cultural fluency, and judgment to make it valuable. At Ranked, we used this analogy when rolling out AI-powered analytics for brand campaigns. Instead of presenting AI as a magic box, we showed it as a tool that organizes millions of cultural signals down to the zip code, while the human strategist decides which signals matter for the brand's story. The clarity clicked, stakeholders understood that AI amplifies intelligence, it does not dictate direction. The result was more trust in the technology and more willingness to experiment. People stopped fearing "black box" AI and started seeing it as an amplifier of human creativity and community insight.
In my approach to explaining generative AI capabilities and limitations to non-technical stakeholders, I often use the analogy of a highly skilled chef. Generative AI is like a chef who has tasted thousands of recipes and ingredients and can creatively combine them to cook up entirely new dishes based on a request. However, just as a chef can only work with the ingredients they've been exposed to, generative AI can only generate content based on patterns it has learned from existing data—it doesn't truly "understand" or possess original thought. This analogy helps stakeholders grasp that while generative AI can produce impressive, human-like outputs, it also has boundaries and can sometimes create errors or biased results. Using this simple, relatable example has made it easier to communicate the balance of excitement and caution needed when adopting generative AI in business.
I explained generative AI to non-technical stakeholders by comparing it to a highly capable intern: efficient and helpful, but not always accurate. I said, "Imagine asking an intern to write a report. They can do it in minutes, but they're pulling from what they've seen before—not necessarily verified facts. You still need to review their work." The analogy was effective because it positioned AI as a tool to support, not replace, human judgment. It helped leadership recognize both the benefits and the risks: AI can accelerate drafting, summarizing, and ideation, but should not be relied on without oversight, particularly for legal, financial, or customer-facing matters. This approach set realistic expectations while highlighting the value of AI.
Reframing the acronym for AI itself often opens the doors for understanding. Complexity is not a requirement, and I ask the group to think of AI not only as 'Artificial Intelligence' but as 'Advanced Idation' or 'Additional Ideas', therefore picturing it as a practical tool generating new possibilities rather than something mysterious or threatening. This simple shift helps people see the opportunities in adopting these new tools. I ask teams if they're 'all in' on AI - a phrase that both creates buy-in and keeps that 'AI' acronym top of mind in their daily work. The real breakthrough occurs when stakeholders recognize that AI presents an opportunity to explore creative design and product development with numerous options, enabling operations to move forward more quickly. I emphasize that AI should be viewed as a partner in day-to-day activities, making team members more impactful and independent than before. To win across organizations, especially in complex teams within established enterprises, we cannot ignore the legal and regulatory aspects of our work. The human role alongside AI is integral to success, enabling non-technical stakeholders to view AI as an enhancement to their expertise, not a replacement.
In an earlier generative AI presentation, I recalled my efforts to bridge the knowledge gap for non-technical stakeholders. They often had/possessed only an abstract notion of how such technology operates and the utility it brings to the table. I put it in simpler terms of an intern who can generate multiple drafts, brainstorm fresh ideas and open up new avenues, but still requires direction. With this, I was able to connect two implicit features of AI: speed and productivity enhancement, and the lack of sharpness in judgment and precision. AI can be equated to an intern in the sense that it can produce something noteworthy but lacks a genuine grasp of comprehension and intent, thus needing human intervention. The metaphor worked best because it leveraged a shared knowledge base, the idea of improved output and efficiency, as well as the risks tied to blindly trusting the output of an intern's labour, which is known to decision-makers.
I like to explain generative AI by comparing it to a GPS system: it can quickly chart out a route based on data patterns, but it doesn't know if a road is closed or flooded unless someone tells it. In real estate terms, AI can draft a sharp-looking property listing in seconds, but it won't know that the house has a quirky layout or a breathtaking porch view--things only a human with boots on the ground can catch. That analogy helps stakeholders understand that AI is a great guide, but it still needs human oversight to avoid wrong turns.
For non-technical folks, I explain generative AI like having a powerful new tool in your construction belt. It can frame a house incredibly fast and accurately, but a human still needs to pour the foundation and ensure the electrical wiring is safe. Using an example where AI drafted a really compelling neighborhood description, but completely missed the fact that the area was about to undergo a major road construction project, really brought home the point that the human element is still crucial for understanding context and unforeseen issues.
When explaining generative AI, I compare it to having a perfect blueprint of a house without knowing the family that will live in it. The AI can draft up floor plans, list square footage, and suggest materials, but it can't understand a family's unique story or what will truly make it their home--like needing a backyard for a new puppy or a kitchen big enough for holiday gatherings. It provides the structure, but we, as people, have to bring in the heart and personal understanding to make it the right fit.
When I explain generative AI to non-technical stakeholders, I like to compare it to using a recipe when buying and renovating homes: the AI follows a set of instructions (the data and prompts), but just like following a recipe doesn't always mean the final dish suits everyone's taste, AI's results can sometimes lack the local flavor or insight a real human brings. Sharing a story about an AI-generated home description that forgot to mention something obvious--like proximity to the beach--makes it clear: AI can be fast and helpful, but it still needs a local's touch to get it right.
I explain generative AI like having a top-notch property scout who can instantly analyze thousands of market listings and draft compelling descriptions, but who's never actually sat across from a homeowner facing foreclosure or felt the weight of their story. When I showed investors an AI-generated investment opportunity report that flagged all the right financial metrics but completely missed that the seller was a veteran struggling with medical bills--a detail that completely changed our negotiation approach and timeline--they immediately understood that AI gives you the data, but only human connection reveals the real opportunity to help someone while making a fair deal.
I explain generative AI by comparing it to a paint-by-numbers kit--it can fill in a canvas quickly with the right shades, but it doesn't know which details really matter to the family moving into that home. For example, I once showed an AI-written property description that nailed the square footage and finishes, but it completely skipped that the house was in walking distance to a beloved elementary school. That gap made it clear--AI can handle the framework, but the human touch adds the story buyers and sellers actually connect with.
I explain generative AI by comparing it to an automated lead finder; it's great at flagging houses with public records of distress, like tax liens or code violations, but it can't listen to a homeowner's story and creatively solve their actual problem. For instance, AI would never have suggested we help a client clean out a hoarder house as part of the purchase agreement, but that was the only way to unburden the family and close the deal.
I explain generative AI as a massive digital sifter for potential property deals. It can instantly comb through thousands of data points to flag undervalued properties, but it fundamentally lacks 'street smarts'--it can't tell you if a seller is just testing the market or is truly desperate to sell due to a personal situation. AI provides the lead, but only a human can read the room and close the right deal.
I use my experience in the shoe business to explain generative AI: it can quickly scan data to identify top sellers and suggest prices, almost like an automated online marketplace. But what it can't do is feel the quality of the leather or understand why a collector would pay more for a rare pair--like when I once sold sneakers to a client specifically because they were his favorite player's model. That real example helps stakeholders see AI's data power alongside its human limitations.
I usually explain generative AI like a photo filter app--it can instantly enhance an image and make it look polished, but it doesn't know whether the picture actually captures the most important moment. In real estate terms, AI can write a smooth property description, but it might highlight the granite countertops and skip the fact that the back deck overlooks a lake. That example makes it clear--it's powerful for speed and structure, but you still need a human to decide what really matters.