AI Clarity Engine
4 mindset shifts to transform how you approach AI and turn it into your ultimate thinking partner
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We are thrilled to feature a guest post by Manmohan Sharma, Principal Technical Product Manager at Amazon, who leads Cross Border Delivery Experience Products.
In a world where Generative AI promises limitless productivity, most leaders still feel buried in noise, not clarity. In this article, Manmohan outlines how he broke free from constant reactivity by pairing human‑driven strategic frameworks with AI’s processing power. He unpacks the 4 mindset shifts (and his exact prompts) you can use to turn AI into your most valuable thinking partner.
Apply these frameworks to cut through the AI noise, focus on what matters, and lead with confidence.
AI Clarity Engine
When Generative AI first exploded onto the scene, I didn’t see it as just a breakthrough in technology; I saw it as a shift in how we think, work, and make decisions.
As a Principal Technical Product Manager at Amazon leading Cross Border Delivery Experience Products, I began experimenting relentlessly. I began applying it to the complex problems I was tackling every day. What started as personal productivity hacks quickly evolved, and I started sharing my findings with my immediate team, which led to organizing training sessions for my broader 600-person organization.
The response was overwhelming. The most common feedback wasn't about the tech itself; it was that I made AI feel accessible, like a tool anyone could use to solve real problems. To scale these conversations, I created an internal "AI Productivity" Slack channel, which is growing fast (1100+ subscribers in 30 days). However, through these hundreds of interactions, a single, frustrating pattern emerged.
We now have access to the most powerful productivity tools in history, yet many of us feel more overwhelmed than ever. My journey over the past few years has been about understanding this paradox and using AI to find signal in the noise. What I have discovered is this: It all starts with asking the right question.
This brings me to a question for you.
The Productivity Trap
I want you to think about your calendar from last week. Was it a strategic roadmap for achieving your most important goals, or was it a series of defensive blocks against a flood of incoming requests?
If you’re like most leaders I know, both at Amazon and across the tech industry, our days are defined by a relentless series of context-switches. We jump from a 30-minute supply chain deep dive, to a 1:1 with a struggling team member, to a 60-minute budget review. We spend our careers reacting, processing, and responding.
We are drowning. The common refrain is that we are "drowning in information," but I disagree.
In reality, we are “drowning in noise”.
Now, a new wave of technology has arrived, promising a lifeline: GenAI.
We're told to use AI to become more "productive" to clear our inboxes faster, summarize meetings, and write performance reviews more efficiently. But this approach is a trap. It’s a high-tech version of the same old hamster wheel. Using AI to answer emails 30% faster doesn’t result in a clearer mind; it just makes you more efficient at being reactive.
This feeling of being professionally overwhelmed isn't just an observation; it’s a core part of my own story.
The Reframe - My Journey to the Clarity Engine
My journey to this way of thinking was not a straight line. I started my career as a software engineer, a world where productivity was tangible. You wrote code, you fixed a bug, you shipped a feature. The results were immediate and logical.
When I transitioned into program and product management at Amazon after my MBA, that certainty vanished. My days were no longer filled with the clarity of code, but with the profound ambiguity of strategy. My core task was to write 6-page documents answering questions like, "How should we operate an Inbound supply chain for just three cents per unit?"
I remember the feeling of staring at a blank document, tasked with defining a multi-year strategy for a system I was still trying to understand. It was paralyzing.
For years, I compensated with brute force and manual systems. My solution was hours of solitary brainstorming, whiteboards filled with frameworks, and a constant, manual effort to structure my thoughts before any important meeting or document. I was building my own mental frameworks out of necessity, just to survive the ambiguity.
Then, about three years ago, GenAI arrived.
It was the missing component I had been waiting for, for nearly a decade.
The "aha!" moment was realizing that the personal, manual 'systems' I had built to survive were, in essence, a series of complex prompts. The very frameworks I used to structure my thinking, such as how to deconstruct a problem, how to analyze different viewpoints, and how to build a narrative, could now be given to an AI to execute at incredible speed.
This is what led me to develop my "Clarity Engine" concept.
It’s not just about AI. It’s the fusion of two powerful elements:
The deep, human-led strategic frameworks I developed over a decade of wrestling with ambiguity.
The immense power of AI to act as a tireless thinking partner to execute those frameworks.
This combination is what finally helped me move from a state of constant reaction to one of consistent clarity.
The Playbook: 4 Mindset Shifts to Build Your Own Clarity Engine
The key to building a clarity engine is making fundamental shifts in how you approach your work and how you leverage AI as a true thinking partner.
Each shift is designed to replace a low-value, reactive task with a high-value, strategic one.
Mindset Shift #1: Stop Summarizing, Start Synthesizing
(Using AI as Your "Chief of Staff" to Prepare for Deep Work)
The most common and least imaginative use of AI today is to summarize a meeting after it has happened. This is a marginal gain because it helps you document the past. A leader's job, however, is to shape the future, and that requires being the most prepared person in the room. The 10x productivity gain comes from using AI to synthesize complex information before a critical event.
Case in Point: A core responsibility for senior leaders at Amazon is writing Technical Promotion Assessments (TPAs). This is a high-stakes, time-consuming process that requires analyzing extensive peer feedback from multiple documents to build a clear, data-backed case for a candidate . The old way involved hours of manually reading, copying, and pasting quotes to find patterns.
My new process is different. Before I write a single word of the assessment, I feed all the raw, scattered feedback into an AI. But my prompt isn't "summarize this." It is a strategic request:
"Act as an executive talent reviewer. Analyze all the provided peer feedback for this promotion candidate. Synthesize the input and structure it into the key themes of 'Technical Depth', 'Leadership Impact', 'Cross-Functional Influence', and ‘Demonstration of Amazon LPs’. Extract the most powerful direct quotes that serve as evidence for each theme."
The AI becomes my "Chief of Staff." It does the hours of low-judgment synthesis in minutes. My time is then spent entirely on high-judgment work: evaluating the strength of the case, crafting the core narrative, and making a thoughtful, well-supported recommendation.
This has consistently saved me around 50% of my documentation time on these crucial assessments, but more importantly, it has improved the quality and clarity of my thinking.
This mindset can be applied to any high-stakes meeting. For example, before a quarterly business review, feed the AI all the pre-reading documents and ask it to synthesize the key takeaways, identify contradictions, and formulate the three most important questions you should be prepared to answer. This helps you stop being a participant who is trying to recall information and become a leader who is prepared to shape the outcome.
Mindset Shift #2: Stop Brainstorming, Start Sparring
(Using AI as Your "Devil's Advocate")
The second common mistake is using AI for generic brainstorming. Asking a model to "brainstorm ideas for a new marketing campaign" will often yield a list of predictable, uninspired thoughts. It’s a creativity-by-the-numbers approach. A far more powerful use of this technology is to have it act as an intelligent, critical "sparring partner" to stress-test your own ideas.
Great ideas are not created in a vacuum; they are forged in the fire of critical debate.
Case in Point: Last year, I was preparing to present a new vision to one of our Senior Vice Presidents (SVP). My plan was to fundamentally improve international delivery speeds —a challenging and ambitious goal.
But I knew the biggest challenge wouldn't be the technical plan. The SVP was laser-focused on keeping costs down, so my task was to sell a vision of speed while proving it wouldn't create a new cost problem. I had to make sure we had answers to all the tough questions about the speed-versus-cost trade-off before I walked into that room.
Before that high-stakes meeting, I used AI as my sparring partner. My goal was to find every potential weakness in my own strategy. Instead of asking the AI for ideas, I gave it a specific, adversarial role based on what I knew the SVP would care about. One of my sample prompts was:
"Act as a skeptical SVP of Cross Border Business. You believe maintaining cost is more important than improving speed. Here is my proposed Plan. Find three biggest risks that I am not seeing. Challenge every assumption I've made."
This process was invaluable. The AI, unburdened by corporate politeness, highlighted potential cost overruns, questioned my timeline, and forced me to build stronger contingency plans. When I eventually presented the vision, I was prepared for the toughest questions because my AI sparring partner had already asked them.
Today, less than a year later, that "overly ambitious" project is delivering faster speeds in some of our toughest cities, with positive feedback from country leaders. Using AI to pressure-test my strategy didn't just help me get buy-in; it made the plan more resilient.
Mindset Shift #3: Stop Drafting, Start Structuring
(Using AI as Your "Narrative Builder")
Many leaders now use AI to write the first draft of an email or a document. This can be a mistake. A first draft written by an AI often lacks a soul; it has the right words but the wrong feeling because it doesn't understand the nuanced context. The real challenge for a leader isn't just writing words; it's structuring a compelling argument.
Case in Point: As a former software engineer, my brain thought in terms of code and logic, not narratives. When I transitioned to strategic roles at Amazon, I discovered that having the right data was useless if I couldn't structure it into a compelling story. Writing Amazon's famous 6-page documents was a massive struggle.
Now, my process is different. After I've done the deep thinking and have a messy collection of bullet points, data, and core insights, I use AI as a "Narrative Builder" to help me find the story.
One of my sample prompts is:
"Here are my core findings and data points for a new product initiative. My audience is a senior leadership team that is short on time and skeptical of new costs. Structure these points into a compelling narrative using the 'Problem-Agitate-Solve' framework. Start with a strong hook that highlights the customer pain point."
The AI isn't inventing the core ideas. It's taking my ideas and expertly structuring them into a proven communication framework. It turns my raw analysis into an influential story, fast. It’s the tool I wish I had had a decade ago when I was staring at that first blank page.
Mindset Shift #4: Stop Solving Puzzles, Start Solving Problems
(Using AI to Build with Empathy)
The final and most important shift is realizing that in an AI world, technology is becoming a commodity. Because of this, a deep understanding of a real-world problem is the ultimate competitive advantage. As technologists and leaders, we are often drawn to interesting technical puzzles. We see a new tool and ask, "What cool thing can I build with this?"
This is a path that often leads to elegant solutions for problems nobody has.
A far more powerful approach is to start with a real human struggle. True innovation isn't about the sophistication of the tool; it's about the depth of your empathy for the person you are trying to help.
Case in Point: I have access to world-class AI models that can do incredible things. I could have tasked myself with building a complex "Concept Graph" tool to map meeting intelligence over time, a fascinating technical puzzle. But I didn't.
My most impactful personal AI project started much simpler. I was watching my wife (Dipti Sharma), who runs an SEO agency, struggle late into the evening. She wasn't facing a single problem; she was dealing with a messy, fragmented workflow. She had meeting recordings on her phone from in-person client sessions, Zoom recordings from strategy calls, and video testimonials from clients. She had a collection of tools that each solved 20% of her problem, resulting in frustration and lost time that impacted her client follow-ups.
The problem wasn't "transcription." The problem was workflow chaos. The pain was personal and palpable.
That empathy became the product brief. The AI tool I built for her wasn't about showcasing a fancy feature. It was about creating a single, simple "front door" for any audio file that could bring order to her chaos. Before writing any code, I used AI to help me think through her specific pain points. My prompt was not technical; it was empathetic:
"Act as a product strategist. A small business owner is overwhelmed with managing audio/video files from multiple sources (phone, Zoom, client files). This is causing delays in her client work. What is her core, underlying problem? What would a 'magical' solution that solves her frustration look like, prioritizing simplicity and control above all else?"
This process led to the creation of a privacy-first transcription tool. It succeeded not because the technology was revolutionary, but because its design was fundamentally empathetic. It was built to solve her problem, not a puzzle that interested me. This is the essence of building with AI today. The models are a commodity; your deep understanding of a human problem is the real differentiator.
Conclusion: The Future is Clarity
These four shifts (using AI to Synthesize, Spar, Structure, and Solve user problems) are the core components of the Clarity Engine. They work in concert to transform a leader's workflow. Instead of reacting to the day's chaos, you begin each critical task from a position of deep preparation and strategic focus. You’re no longer just managing the noise; you are conducting the signal.
The future of work for senior leaders will not be defined by who can answer the most emails or attend the most meetings. It will be defined by who can consistently produce the most clarity for themselves, for their teams, and for their customers. The quality of our decisions is directly proportional to the clarity of our thinking.
The Clarity Engine isn't about having the loudest voice in the room; it's about having the most resilient, well-thought-out perspective, so that when you do speak, your voice has weight and influence.
Ultimately, AI is not a replacement for human strategic thought, empathy, or judgment. It is a tool. And the most powerful way to use that tool is not as a shortcut to bypass work, but as a lever to create the conditions for the deep, focused work that only we, as human leaders, can do. It's about augmenting our intelligence, not outsourcing our thinking.
If this idea of building your own Clarity Engine resonates with you, I invite you to follow my journey on LinkedIn, where I continue to explore these frameworks and build new tools in public.
If you enjoyed this article, give it a like so we know to write similar types in the future.
Thank you, Manmohan, for sharing your clear and actionable framework to cut through the noise and harness AI as a true 10x thinking partner (not just a 1.5x productivity tool).
Connect with Manmohan on LinkedIn to keep up with his latest AI insights and practical frameworks.
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Before & After AI Prompt Examples — We updated the Ethan Evans AI (EthanGPT) “Getting Started Guide” to showcase the different between an ineffective vs effective prompt.
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