AI Product Adoption Checklist
Let's talk about the AI elephant in the room: while companies are sprinting toward technical excellence, they're stumbling on the human side of the equation. Picture your organization as a Formula 1 team. You've got this incredible AI-powered car (the technical stuff), but what about your pit crew (employees), spectators (customers), equipment suppliers (vendors), and race officials (regulators)? Without them fully on board, that high-performance machine isn't going anywhere.
Here's the reality check: organizations are pouring resources into building sophisticated AI systems, but many are doing it in a tech bubble. The real measure of AI success isn't just about having cutting-edge algorithms – it's about how seamlessly humans interact with, trust, and benefit from them.
The companies seeing real ROI? They're the ones who realized AI transformation is as much about people as it is about technology. They're not just coding in isolation; they're orchestrating a symphony of human engagement, understanding, and adoption. The question isn't whether your AI is technically sound – it's whether your humans are ready to take the wheel.
"Companies face numerous challenges when implementing AI initiatives, with around 70% stemming from people- and process-related issues, 20% attributed to technology problems, and only 10% involving AI algorithms—despite the latter often consuming a disproportionate amount of organizational time and resources. Too many lagging companies make the mistake of prioritizing the technical issues over the human ones." Boston Consulting Group, AI Adoption in 2024
The AI revolution isn't just about smart algorithms – it's rewiring how we work at every level. Take insurance, for example: when AI handles basic property evaluations and risk assessments, it's not just automating tasks – it's fundamentally shifting what your people do all day.
Suddenly, your underwriters aren't buried in paperwork and basic research. Instead, they're becoming strategic analysts, diving deeper into complex risk factors and market insights that AI can't fully grasp. It's like upgrading from a calculator to a supercomputer – sure, the basic math is faster, but the real value comes from what your team can do with all that extra bandwidth.
But here's the catch: this shift ripples through your entire organization. You need new training programs because Excel wizardry isn't enough anymore. Your recruiting needs to target different skills. Performance metrics that worked yesterday might be irrelevant today. Even how teams collaborate changes when AI is part of the conversation.
The secret sauce? Moving everything in sync. Your roles, processes, and tech need to evolve together – like a well-choreographed dance. Fall out of step, and you'll stumble on your way to market. Nail the timing, and you'll transform your operation while bringing your team along for the ride.
AI Adoption Checklist
To support your dance and win that race, here's your essential checklist for blending tech smarts with human brilliance.
1. Ensure a strong value use case and customer emotional alignment
Before diving into complex algorithms and cool features, do your research. Steve Jobs nailed it when he said, "You've got to start with the customer experience and work backwards to the technology." He wasn't just talking about sleek gadgets – he was describing a mindset that puts human needs first.
The most sophisticated AI in the world is worthless if your people don't want to use it. Start with the human experience, and let that guide your tech decisions.
Clearly articulate your business problem or opportunity and how that aligns with your organizational strategy
Determine and quantify value metrics such as cost savings, efficiency, customer loyalty, increased revenue and estimate ROI to justify investment
Build your use case to ensure the value and emotional connection to your products exists
Define with employees and customers how best to position
2. Start rallying and aligning leaders early around AI solutions
Too many strategic initiatives are derailed because the right leaders or groups weren’t involved from the start which can result in costly redesign, limited available resources or resistance.
The best use cases emerge when cross-functional collaboration drives the solution.
Secure early executive alignment, sponsorship commitment and resources. Bring together leaders from key areas— for instance for a new customer service solution, you may need marketing, legal, HR, alongside product, data, and cybersecurity teams—to drive results.
Engage leaders along the way in key decision-making. Communicate often and make them part of your team for success.
Clarify leader roles and responsibilities early to increase buy-in and avoid later frustrations.
3. Understand impacts of your AI solution on your environment
Once you have a clear picture of your current and future state design, you should be able to understand the impacts of your AI design. These impacts can include organizational design, role, process, technology, regulatory and other impacts.
Determine impacted groups such as customers, departments, vendors, regulators and others.
Define the gap between current and future state for each group and the desired activity, behavioral or process change needed
Understand what additional leaders need to be engaged to support this change, ie HR, Legal
Build your engagement and learning plans to move impacted groups from unaware to mastery
4. Engage employees as part of your AI solution
Many employees fear AI job displacement, which can result in resistance to change, low morale and loss of talent. Employees are unsure what their careers will look like in the next five years. By engaging employees early in your Ai solutions, you transform them into active partners in the process, rather than potential roadblocks.
Engage employees to share valuable functional expertise to enhance your AI solution.
Have leaders provide names of potential change agents to support from the effort design through implementation.
Encourage change agents to share updates with their team.
Make your change agents part of your testing team.
Host open hours on new AI tools before they launch when possible.
Reinforce and reward desired AI behavioral change with small offerings such as gift cards and recognition opportunities.
5. Redesign processes to optimize the Ai experience
Good AI technology should impact processes, roles/responsibilities and legacy technologies with a focus on automation and data consolidation. This could mean a streamlined approval process; different ways of using current tools such as ServiceNow; elimination of old forms and tools; and redefined IT support to name a few possible process changes. If you don't get this right, chaos and workarounds will occur.
Know your process impacts for each group from Step 3 above.
Determine Level 1 "what" to Level 3 "how" impacts as needed and understand what is change for whom and how
List the impacts and improvements required to support the AI implementation. Review and validate changes with appropriate stakeholders.
Build process changes into your upskilling and communication needs
6. Determine how to address skill gaps and customer/workforce readiness approach
The talent game is changing fast. While AI handles the routine stuff – data crunching, basic analysis, repetitive tasks – tomorrow's workforce needs a different toolset. Think less spreadsheet guru, more creative problem-solver.
Over the next three years, we'll see a major shift in what makes someone valuable in the workplace. The hot skills? Critical thinking that AI can't replicate, creativity that breaks new ground, and the ability to work with both humans and AI tools. It's like upgrading from a manual to an automatic car – you need different skills to excel.
And it's not just employees who need to adapt. Customers are facing a flood of AI-powered marketing and sales tools. From chatbots to personalized recommendations, they're learning to navigate a new landscape of AI interactions. Companies that help their customers master these tools will win the loyalty game.
The bottom line? Whether you're an employer or service provider, investing in AI skills isn't optional – it's essential for staying competitive in this rapidly evolving marketplace.
Know your skill gaps from your impact assessment Step 3 above. Also determine if there are any organizational design impacts such as moving a team to a different department or outsourcing/insourcing key roles.
Create or obtain training to support activity, behavior and process change training needed. This shouldn’t just be technical training it has to work with the culture and share behavior needed changes also. When there are high-impacts to a team, work with their leadership and HR to identify new credentials needed and the opportunity for certifications to support the change.
Consider additional supports needed from leadership to drive substantial change, such as coaching, team redesign and behavior reinforcements.
Develop a campaign for your customer and the effective change tools (ie. FAQs, videos and feedback) to explain changes and garner support.
Since, AI systems rely on vast amounts of data, provide clarity on how you will manage data privacy, security and regulatory concerns. Customers and employees may not trust AI systems, especially when they involve critical decision-making processes.
7. AI Integration with Legacy Systems
AI adoption often requires significant integration with legacy systems such as ERPs, CRMs, and financial tools. Have a clear system picture of this integration, the cost and the usage changes with your AI product.
As you redesign your processes ensure you understand how critical these tools are to AI design and how this impacts employee daily work.
Test workarounds with functional employees to ensure there are no technical or workflow concerns.
Build these changes into your training to provide clear directions on how to support the new AI product or service.
8. And finally, if you haven’t already, create and implement an AI enterprise strategy, governance and communications approach
An enterprise approach to AI strategy paints a picture for your organization of how to engage with AI, your product prioritization, organizational ways of working and overall vision and alignment with business objectives. Done well this builds a foundation of trust and understanding in AI, your leadership and the decisions being made.
Share your enterprise AI strategy and guidelines so employees, customers and vendors know what to expect and how they will be impacted.
Determine your AI enterprise governance approach and determine how that governs AI at the product level. For more ideas, check out our article on Strategic Governance to Fully Harness the Power of AI.
Communicate AI progress through regular enterprise communication channels such town halls, digital tools and leadership meetings.
Build an enterprise approach for employees to learn and support AI through AI basics course such as prompt engineering and common AI tool usage education either online or interactive workshops.
Find functional change network champions, utilize AI focus groups, and monitor employee feedback on AI. Clarify how AI will shift employee career development and opportunities for growth.
Welcome employees to engage in AI development through suggestions and provide a safe space to ask questions and share concerns.
If you need support on any of the above, reach out to the A-HUMAN-I team. We can create a customized solution to put the Human in your AI solutions.
