About five years ago, I published a post on LinkedIn called The 10 Commandments of Digital Transformation (you can still read it here). A lot has changed in the tech world since then and now feels like the perfect time for a well deserved sequel about the (not so new) kid in town: AI.
My goal with the 10 Commandments series is to give clear one liners to help shape your thinking and approach. If you’re reading this, chances are you’re not new to AI and if you are, welcome! Grab a chair and feel free to look around.
So, dear reader, here goes:
1: Start with Real Problems, Not Cool Tools
Don’t fall into the trap of using AI just to look innovative. Begin with specific pain points. Tasks that are repetitive, data heavy, or time consuming and ask, what tools are available to improve this particular problem.
If it doesn’t solve a real problem, it’s just a toy.
2: Educate Before You Integrate
You can’t expect buy in if your team doesn’t understand what your AI tool can and can’t do. Provide simple workshops, demos, or even 5 minute videos that break it down.
Knowledge = Confidence = Adoption.
3: Go for Quick Wins First
Start with use cases that deliver visible value fast like customer support summaries, internal reports generation, or content generation. Prove it works. Then scale.
Momentum is your best friend.
4: Involve End Users from Day One
AI adoption is as much a people and business decision as it is a tech decision. Involve the actual users early. Get their input and make them feel part of the process. Don’t just dump something on their laps and force them to use it. Humans are funny. They’ll see to it that it fails!
Build with people, not for them.
5: Keep Humans in the Loop
AI should support, not replace, your people. Especially in sensitive areas like hiring, customer service, or finance. Make collaboration easy.
People + AI > AI alone.
6: Prioritize Transparency
Avoid “black box” tools. Use AI systems that explain their outputs. Show the logic. Explain the confidence score. People trust what they understand.
Transparency builds trust. Always.
7: Meet Users Where They Are
Don’t ask people to switch platforms just to use the new AI. Integrate it into their day to day tools like Slack, CRM, Notion, email etc. Make it seamless.
Convenience is key to consistency.
8: Track & Show Results
Measure the right things: time saved, accuracy improved, customer satisfaction. AI without performance tracking is just a guessing game.
What gets tracked, gets improved.
9: Be Ethical from the Start
AI can amplify biases or misuse data if you’re not careful. Use responsible practices like auditing models, cleaning data, and setting clear guidelines.
Responsible AI is smart AI.
10: Build for the Long Term
AI isn’t a one time implementation. It’s a shift in how you work. Invest in internal skills, revisit your workflows, and keep evolving.
Think capability, not project.
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The commandment in Practice
A few months ago, I spoke with a leader at an eCommerce company that had been struggling with delivery pickups, especially in remote areas. Customers were not showing up to pick up their deliveries despite the numerous texts that were sent to them. The team was overworked, support tickets were piling up from the same customers, and customer satisfaction was dipping fast. They knew AI could help, but weren’t sure where to start.
Instead of trying to “AI-ify” the whole company overnight, they focused on one small thing: automating delivery update messages using an AI enabled omnichannel engagement solution trained on their data. They soon realized that people were not missing deliveries because they were nonchalant, but because the channel the delivery notifications were coming through were not ideal for them at that time. Within weeks of deploying an omnichannel approach that sent notifications on the right channel at the right time, they reduced support requests by 40%, saved hours of manual work, and even saw happier customers just by tackling one real pain point.
Now, they’re rolling out AI to help with route planning and warehouse inventory. But they didn’t start there. They started small, started smart, and brought their team along for the ride.
Remember, people. AI adoption doesn’t have to be big and loud. It just has to be real
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