The GenAI Duality: Why Enterprise Value Lies in Accuracy, Not Adulation
Accuracy Over Adulation: A Leader's Guide to AI Modulation
Oct 26, 2025
15 minutes
Tech | AI | Prodcut | Leadership
Tania Makroo | Transformation Strategist
People | Jobs | Digital Work
A Trailblazer's Guide to Modulating the GenAI Engine
Recently, I saw some beautiful content on the screen. I work with some of the best designers in tech, and the visuals are always stunning, engaging, and following a clean layout and design system. But as I watched, something wasn't right.
There was a clear miss in one area and, more jarringly, the misspelled name of a valued team member on a slide that meant the world to them in the form of being recognized for their tremendous effort for a key customer engagement. These small moments of recognition are sparks of light and encouragement. We should care about these as leaders of talented individuals.
When I shared this feedback, I was met with a new and unsettling response: "It was AI's fault."
I had to pause. I thought to myself, "AI is a tool. It's here to support us, to augment our capabilities not do all the work for us. Where was the human in the loop? Where was the accountability?". Ironically, the final portion of the session was on a discussion of AI use cases and sentiment of the team on the worries of it.
I learned about a high school kid being hand-cuffed due to the AI system mis-reading what he had in his hand. Another of a consulting firm whose data was based on hallucination and is paying the dues for that error. A company had set incorrect instructions and permissions of administration for their AI system, therefore lost irrecoverable data.
These minor and major anecdotes, less uncommon than we think, are a critical warning sign across all industries leveraging Assistive and Agentic AI.
We are deep in the Adulation Trap; so dazzled by the "beautiful" 95% output that we abdicate our responsibility for the critical 5% that reduces credibility, demonstrates disrespect, and exposes the business. We're celebrating the demo and ignoring the risk. More and more of these stories begin to surface in mass-media and have been on the internet for sometime now.
A Trailblazer's Duality: Championing Tech, Demanding Balance
Let me be clear: I have and will always be a trailblazer when it comes to tech, I look for patterns and advancements in everything related to technology including AI, Robotics, Systems and more. In my work, when I teach, and speaking at panel interviews, etc… I have and will always speak about how great it is. We all should be learning about it and incorporating it.
But that's not without the warnings many before me have already shared.
I try to keep a pulse of the end-user sentiments, stakeholder news, and technology research. This isn't just because it keeps me ahead in my job; it's because I am on a personal journey for a balanced life. And balance, by its very nature, comes with the duality of good and bad.
Generative AI is the perfect embodiment of this duality.
The "Good" i.e. The Goal:
This is the promise we're all striving for. It's the ability to move quicker and more accurately. This is the vision of a modulated AI, a reliable partner that augments our teams, surfaces correct information, and frees us for higher-level strategic work. This is AI in balance.The "Bad" i.e. The Problem:
This is the dangerous default state. It is the combination of the un-modulation of the raw engine and the adulation we give it. The "un-modulation" is the source of the risk such as the hallucinations, the errors, the misspelled names. "Adulation" is the human trap that makes us overlook these critical flaws, accept "AI's fault" as an excuse, and abdicate our accountability.
As leaders, our job is to fight for the "Good" by actively architecting systems that defeat the "Bad." We must replace adulation with accuracy and build the modulation.
The Executive Mandate: Building the "Modulation Layer"
To defeat the "Bad" (the un-modulation and adulation) and achieve the "Good" of true accuracy and speed, we must build a system. The "human in the loop" isn't just a person; it's an architecture, the business mandate and key driver.
We are not here to be the "Chief Wrap-it-up-in-a-pretty-bow Officer". Half baked isn't fully cooked. Let's cook. Let's hold ourselves and our teams to a higher standard. "It's AI copy so not my problem" should never be an answer. To turn a high-risk engine into an enterprise-grade tool is to have definitive answers through due diligence.
The 3 Essential Pillars to Modulation and Accuracy
Pillar 1: Grounding (The "Accuracy" Anchor)
Action by directly attacking un-modulation. An un-modulated AI guesses from a sea of public data. We stop this by grounding it in our own truth. This is the essence of Retrieval-Augmented Generation (RAG). We must stop asking the AI, "What do you know?" and start demanding, "Tell me what this specific, verified document says."
Your Strategic Takeaway: Your most critical AI investment isn't the model; it's the data pipelines and vector databases that feed it your ground truth. This is how the AI knows the correct spelling of your team member's name, your product's secret sauce, and your non-public brand guidelines.
Pillar 2: Guardrails (The "Safety" Brakes)
Systemic answers to the adulation that encourages abdication. It is the "human-in-the-loop" built at scale. It's the set of rules that defines what the AI cannot do (e.g., "Give me a list of underperforming employees"), what it must flag for human review (e.g., legal or financial advice), and where it must simply stop and defer to a person.
Your Strategic Takeaway: Define your "red lines" before you deploy. Accountability cannot be an afterthought. This framework is what separates a mature enterprise tool from a high-risk liability.
Pillar 3: Metrics (The "Value" Scorecard)
Our direct antidote to adulation. You cannot manage what you don't measure, and for too long, we've been measuring "wow." It's time to measure accuracy. The "adulation" metric is, "How many employees are using our internal chatbot?" The "accuracy" metric is, "What is the hallucination rate of our chatbot against our verified knowledge base?"
Your Strategic Takeaway:
Shift your KPIs. Reward accuracy, not just activity.
✦ From Adulation → To Accuracy: The importance of tracking the right metrics.
Adulation based Metrics | Accuracy based Metrics |
|---|---|
🚫 Employee engagement scores with the tool. | ✅ Human-in-the-loop (HITL) intervention rate |
🚫 Time-to-first-draft. | ✅ Time-to-task-completion with 100% verification. |
https://www.techspot.com/news/108920-ai-interview-bots-spark-backlash-frustrated-job-applicants.html
An Action Plan: From Adulation to Accuracy
This is not a theoretical exercise; it's an immediate leadership priority. Here is how you can begin building your "Modulation Layer" to fight the "Bad" (un-modulation and adulation) and achieve the "Good" (accuracy and speed).
✔️ This Week: Start the Diagnosis
Your immediate goal is to identify where "adulation" is creating blind spots. (Essentially User Observational and Interview Testing)
Hunt for an "AI's Fault" Error: Go to your team and ask for one recent example of a "minor" AI-driven error, like the misspelled name. Ask why it happened and who was accountable for the final review. Use this as a teachable moment about accountability.
Audit 1 - 2 AI Assisted Workflow(s): Sit with an employee and watch them use a GenAI tool for a real task. Where do they check its work? Where do they not check it? Get a real-world baseline for how much un-modulated content is getting through.
Ask "Where Do You Distrust It?": In your next team meeting, ask a simple question: "For those using GenAI, where do you distrust it the most?" Their answers will give you a pre-built roadmap of where to focus your accuracy efforts first.
✔️ This Month: Architect the "Modulation Layer"
Your goal is to build the initial framework for governance and accountability.
Define Your "Red Lines" (Guardrails): Assemble your legal, security, and brand leaders. Define and publish a simple, one-page list of what your GenAI tools cannot be used for. What data is off-limits? What questions must never be answered? This is your first set of guardrails.
Draft Your First "Accuracy" KPIs (Metrics): Take one high-value process (e.g., writing marketing copy, summarizing research) and define one accuracy metric. This could be "Hallucination Rate per Article" or "Human-Intervention Rate." Start tracking it now.
Appoint an Owner (Accountability): Assign a single leader as the "Chief Modulation Architect." This person is now accountable for the Grounding, Guardrails, and Metrics of your AI systems. If accountability is everyone's job, it's no one's job.
✔️ This Quarter: Build the Foundation
Your goal is to move from ad-hoc use to a reliable, scalable system.
Launch One "Grounding" Pilot (RAG): Don't try to boil the ocean. Pick one critical domain (like HR policies, product specs, or customer support documentation) and launch a pilot project to "ground" a GenAI tool only in that data.
Identify Your "Golden Datasets": Task your data-governance team with identifying the "single source of truth" datasets for your top 5 business units. This is the "ground truth" you will eventually use to modulate all enterprise AI.
Train Your Managers: Host a workshop to train your leadership on this new framework. Teach them how to coach their teams on "Accuracy over Adulation" and how to manage to the new "Accuracy" KPIs. Accountability must be cascaded.
https://www.techspot.com/news/109703-ai-actress-tilly-norwood-slammed-sag-aftra-soulless.html
The Balanced Path Forward
Resilience in the age of AI isn't just about infrastructure (the theme of our last conversation); it's about balanced implementation.
The adulation for GenAI is fleeting. It's a sugar high. The trust lost from a single, high-profile failure; whether it's a massive hallucination that goes viral or a "minor" error that disrespects a colleague…can set your program back for years.
Key Takeaways
Grounding (RAG): The 'Accuracy' Anchor that connects AI to your truth.
Guardrails: The 'Safety' Brakes that define your red lines.
Metrics: The 'Value' Scorecard that shifts KPIs from "wow" to "verified accuracy."
The real "magic", the real "Good" isn't what the AI can generate. It's the disciplined, human-led framework we build around it. The "AI's fault" excuse is a failure of leadership, not technology.
Be a trailblazer. Champion this technology. But be a balanced one. Prioritize the hard, unglamorous work that it takes to doing things the right away when things like people's lives, identities and much more are on the line. That is the only path to achieving genuine accuracy, and sustainable success.
