First, a hopefully unfamiliar feeling of attempting to bring a product to market but lets see how close to home this strikes.
Imagine a construction technology company on the verge of launching an innovative drone equipped with advanced AI to inspect roofs for potential repairs. The product promises to revolutionize roof inspections, offering faster, safer, and more accurate assessments. However, as the company gears up for the launch, it becomes painfully clear that the different departments are not on the same page and surprise surprise there is more and different types of competition that they have ever seen in the past.
The marketing team is busy crafting high-level messaging about how this drone will "transform the future of construction," focusing on broad benefits and visionary statements. They're thinking about social media buzz, eye-catching visuals, and appealing to a wide audience. Meanwhile, the sales team, eager to hit targets and close deals, is willing to promise almost anything to potential customers. They assure buyers that the drone will work flawlessly under all conditions, that it's the easiest on the market to use, and that it integrates seamlessly with existing systems. The goal is to generate interest and lock in commitments, even if it means stretching the truth a “smidge.”
Then there’s the product and engineering team, who have worked tirelessly to develop the drone. They know its strengths, like the cutting-edge AI that accurately identifies roof damage, but they're also aware of its limitations. The drone works best in certain weather conditions, requires specific training to operate, and has compatibility requirements that can't be overlooked. They feel overwhelmed trying to compete with the high-level promises of marketing and the ambitious guarantees of sales. They're frustrated that the true differentiators of the product—its precision, reliability, and the engineering behind it—are getting lost in the noise.
As launch day approaches, the disconnection among these teams becomes more apparent. Marketing’s high-level messages don't address the technical details and practical concerns that customers actually care about. Sales is over-promising features that engineering can't realistically deliver, setting up the company for disappointment. The engineering team feels sidelined and underappreciated, worried that customers will have unrealistic expectations. What was supposed to be a seamless launch of a groundbreaking product turns into a potential disaster, with misalignment leading to confusion, dissatisfaction, and the risk of the product failing to meet its market potential.
This scenario highlights a pattern I have seen countless times: the messiness and likely circle of blame between sales, marketing, and product development. It's a problem that can lead to mixed messages, unmet expectations, and ultimately, a damaged brand reputation.
This is where training AI can play a transformative role, not just in product launches but in aligning teams and ensuring a consistent, accurate message across the board.
Cognitive Benefits of Teaching Others (including AI):
Training others has long been recognized as one of the most effective ways to reinforce learning. When individuals teach, they are forced to process information at a deeper level, clarifying their understanding and filling in knowledge gaps. This concept, known as the "protégé effect," suggests that explaining concepts to others strengthens the trainer’s comprehension and retention of the material. For example, in the case of the drone product, if a sales representative has to teach the engineering team about how they plan to sell the drone, they will need to align their understanding with the product’s capabilities, which enhances their grasp of both the product and the selling strategy.
Benefit of Group Training for Alignment (especially with AI):
Training as a group fosters a shared understanding and consistent messaging. When a team learns together, they can engage in discussions, ask questions, and clarify doubts, which leads to a unified perspective on strategies and objectives. This collective learning process promotes alignment, ensuring that everyone is on the same page. For instance, conducting a joint training session for sales, marketing, and engineering about the drone’s features and limitations ensures that everyone understands and agrees on how to position the product, leading to more cohesive and aligned efforts.
What Do These Principles Look Like In Practice When Training AI ?
Marketing + Sales Edition:
Consider a scenario where marketing and sales teams come together to train the AI embedded in the drone’s inspection system. The AI needs to be trained to recognize specific types of roof damage accurately and provide actionable insights. Both teams participate in feeding the AI real-world data and discussing the exact capabilities of the drone. Through this joint training, they engage in detailed conversations about what the AI can realistically deliver, such as identifying common issues like shingle deterioration or water damage while acknowledging its limitations, like struggling in certain weather conditions.
By training the AI together, both teams develop a unified understanding of the product’s capabilities and limitations. Marketing learns to craft messages that accurately reflect the AI’s functionality, setting realistic expectations. Sales gains clarity on what to promise to customers, avoiding overstatements that could lead to dissatisfaction. This collaborative training session serves as a boundary object, bringing diverse perspectives together and focusing on a common goal. It not only enhances the teams’ understanding of the product but also fosters a sense of shared responsibility and alignment, minimizing functional competitiveness and avoiding ego issues. The AI becomes a central figure in this process, creating a cohesive narrative and ensuring that both teams are aligned in delivering consistent, truthful, and effective messaging to the market.
Now, lets do Sales + Engineering
Consider a similar scenario where the sales and engineering teams come together to train the AI system used in the drone’s inspection technology. The AI needs to be trained to identify specific roof damage accurately, so both teams contribute real-world data and insights (where did we hear that pattern before?). Through this joint effort, they discuss what the AI can realistically detect, such as missing shingles or cracks, and acknowledge its limitations, like difficulty operating in low-light conditions.
By training the AI together, sales gains a realistic understanding of the product’s capabilities, which helps them make accurate promises to customers. Meanwhile, engineering understands the common customer needs and concerns, enabling them to prioritize features and improvements. This shared training experience acts as a boundary object, bridging the gap between the technical details and customer-facing promises, ensuring both teams align on delivering a consistent, reliable message about what the drone can truly offer.
Interdisciplinary and multidisciplinary teams benefit significantly when training AI because the process taps into the learning science principles of teaching others and collective learning. By training AI as a “boundary object”—a concept that serves as a shared reference point across different fields—teams can collaboratively focus on a common goal without worrying about the blame of who did or didn’t do their part to make a product launch successful. This shared effort helps bridge the gap between diverse areas of expertise, fostering alignment without the friction of departmental competition or ego clashes. Since AI serves as a neutral entity that everyone can contribute to and learn from, it naturally facilitates communication and mutual understanding, creating a mission mindset that everyone is rallying together to accomplish.
So, how do we do this at SkillBuilder.io (and for our clients)? How can you make sure AI doesn’t trigger anyone?
Highlight AI’s Role as a Knowledge Amplifier not Taker: Emphasize that AI is there to enhance and scale human expertise, not replace it. By training AI, employees make their specialized knowledge more valuable, as AI can take over routine tasks, allowing people to focus on strategic, creative, and problem-solving aspects of their jobs.
Showcase Real-World Success Stories: Share case studies and examples where AI has been successfully used as a boundary object to improve alignment and efficiency. Demonstrate how companies that embraced AI saw improvements in team collaboration, customer satisfaction, and overall business performance, reducing fears about job displacement.
Engage Teams in Collaborative AI Training Sessions: Organize hands-on workshops where employees from different departments work together to train AI systems. This not only demystifies AI but also helps employees see firsthand how their input and expertise are crucial for AI’s effectiveness, reinforcing their value within the organization.
Offer Up Skilling Opportunities: Provide training sessions that teach employees how to use AI tools to their advantage. By learning how to work in harmony (not balance) with AI, employees can develop new skills that are valuable in the evolving job market, making them more confident and less fearful about AI’s role in their work.
Encourage Open Dialogue About AI: Create forums or feedback sessions where employees can express their concerns about AI. Address these concerns directly and transparently, showing how AI is intended to support their work rather than replace them. Use these sessions to highlight how AI can take over mundane tasks, allowing employees to engage in more meaningful and impactful work.
I’ll admit, this can be a lot to process despite feeling intuitive on the surface but if you are excited to learn about how to start up skilling your entire team when you hire your first AI employee please reach out. It’s a lot of fun the moment your entire organization realizes that training AI brings clarity and alignment around a mission and doesn’t leave anyone behind.
