Why should you care about my PoV: Front row seat on AI adoption
I’ve spent the last 18 months helping large enterprises understand, experiment with, and adopt AI — first-hand, through demos, POCs, and deployment cycles at Salesforce India. Before that, I was a data scientist, a founder trying to scale, and a product manager building dynamic pricing for B2C travel from grounds up. That mix — part builder, part analyst, part storyteller — has given me a close-up view of what it actually takes for AI to move from buzzword to business impact inside the enterprise.
We’re living through what economist Noah Smith calls the “third magic” of humanity:
- History gave us memory (knowledge accumulation)
- Science gave us method (generalization)
- AI now gives us learning and search — the ability to operationalize knowledge at scale
But there’s a catch: AI enables operational control at scale without complete understanding. This tension explains why purely technical approaches to enterprise AI fail. The challenge isn’t deploying models; it’s orchestrating human systems around AI capabilities while maintaining organizational coherence.
The Enterprise AI Dilemma
Every CXO knows they need to “do AI.” The fear of falling behind is real and lack of AI tech implementation know-how. But between top-down mandates and bottom-up reality sits a messy middle:
- Siloed data and legacy systems
- Competing power centers between business and IT
- Ambiguous success metrics
- Incomplete digital transformation
This leads to a familiar cycle:
“We need to do AI."
"We have no idea what to do in AI."
"We don’t know how to do AI."
"Is AI even doing anything?”
Meanwhile, POCs gather dust, dashboards don’t get used, and AI projects quietly fade away. The conversation and work ends up becoming what I call “innovation theater” — AI projects launched to satisfy board mandates rather than solve real problems.
Enter the Translator
The key to breaking this pattern is a new kind of role that is getting popular — part product manager, part consultant, part engineer. Let’s call them the translator: someone who can connect AI’s technical sophistication to the messy realities of business adoption.
A great tech translator does the following:
- Understand the terrain beyond the org chart — Map stakeholders, power centers, tech gatekeepers & change champions. Know who owns the problem and who controls the budget.
- Evangelize with empathy — Teach, run workshops, create “AI days.” Demystify without dumbing down.
- Design for reality — Understand current workflows and silos before trying to automate. Sometimes the right move isn’t optimization but reinvention of the workflow itself.
- Risk Calibration, Not Risk Elimination — Different orgs have different risk tolerances for human-in-the-loop versus autonomous operation. Success means designing systems that match organizational appetite, then gradually expanding autonomy.
- Scope wisely — Be honest about feasibility: what’s agentic, what’s rule-based, what’s still aspirational. Translate hype into actionable architecture.
- Establish trust — Build guardrails around privacy, observability, and governance. The contract is no longer won at signature — it’s won during deployment.
Generally the skills that are useful
The people who thrive in this translator role usually have a few things in common:
- Pseudo-technical literacy: Can go deep enough to hold a meaningful conversation with engineers, but abstract enough for executives.
- Executive presence: Can tell stories that earn trust in the boardroom.
- Builder’s mindset: Comfortable prototyping, failing fast, and iterating.
- Systems empathy: Can sniff inefficiencies and connect dots across org silos.
- Ownership: Don’t stop at demos — they stay through the adoption journey.
These are often people who’ve been founders, PMs, consultants, or data scientists — people who’ve lived on both sides of the business–tech divide.
So how to run the show of Enterprise AI adoption
1. Discovery: Ground the Ambition in Reality
Every journey starts with ambition — “We want to be an AI-first company.” But before jumping to solutions, the first task is to unpack that ambition into concrete use-cases that map to business outcomes.
At this stage, as a good consultant:
- Surface the real problems that AI could solve, often hidden behind process inefficiencies or legacy bottlenecks.
- Map stakeholders, incentives, and constraints — understanding who owns the problem and who controls the budget.
- Define early success criteria: What does “impact” look like — efficiency, customer experience, revenue lift, or innovation?
This phase is about aligning language before aligning systems.
2. Evangelize and Educate: Build Organizational Literacy
Before you build models, you have to build belief. AI adoption fails when leadership drives it as a technology mandate instead of a capability transformation.
That’s why top-performing companies start with internal evangelism:
- Running AI days, workshops, and internal demos that build shared understanding.
- Teaching teams what’s possible (and what’s not) — so expectations are grounded.
- Creating cross-functional “AI champions” within business and tech units who carry the momentum.
The goal is to make AI a team sport, not a vendor-driven project.
3. Design and Prototype: From Concept to Proof
This is where vision meets feasibility, or rubber meets the road. The goal isn’t to build the biggest model or deploy the flashiest tool — it’s to scope tightly, validate assumptions, and create a working proof that integrates with real workflows.
At this stage:
- Identify data readiness — what’s usable, what’s missing, what’s fragmented.
- Run small, high-impact POCs or pilots with clear KPIs.
- Test adoption hypotheses: Will users trust this? Is the ROI visible enough to justify scale?
Success here comes not from technical perfection but from credible proof of value.
4. Deployment and Change Management: Where the Real Work Begins
Most people think the finish line is deployment — but that’s actually where the hard part starts. The deployment phase is about operationalizing change, not just deploying code.
This means:
- Integrating AI outputs into existing systems of record or decision workflows.
- Designing feedback loops between business users and technical teams.
- Building trust through transparency — explainability, observability, and auditability.
- Managing the human side of adoption — retraining, reskilling, and redefining roles.
The golden rule: AI success = technical adoption × human adoption.
5. Scale and Institutionalize: From Pilot to Platform
Once an AI capability proves its worth, the next challenge is scale — but scaling isn’t just about volume; it’s about repeatability and governance.
At this phase:
- Standardize your AI development lifecycle: data pipelines, testing frameworks, deployment guardrails.
- Create reusable components — prompts, agents, evaluation frameworks — that make new use-cases faster to build.
- Establish clear policies around privacy, security, and compliance.
Enterprises that reach this stage turn AI from isolated projects into platform capabilities embedded in their operating model.
6. Evolve and Reinvent: The Flywheel Effect
Once AI systems are in production, the best organizations don’t stop — they learn, iterate, and reinvest.
- User feedback feeds model improvement.
- New business insights inspire next-generation use cases.
- Governance frameworks mature alongside product evolution.
- New AI capabilities get added to the solution framework.
This continuous loop — from experimentation to institutional learning — creates a flywheel of AI adoption. It’s what turns a single POC into an organization-wide capability.
The Mindset Shift & opportunity ahead
Ultimately, successful AI adoption is not about building smarter systems — it’s about building organizations that can learn. The most effective AI consultants don’t just deploy solutions; they help enterprises rewire how they think, decide, and operate.
For enterprises, the promise of AI isn’t just automation — it’s the chance to reimagine work itself. For professionals who can bridge the gap between vision and implementation, this is one of the most exciting career paths of the decade. Because in AI, the real win doesn’t happen at the contract signing. It happens at deployment — when people start using what you’ve built, and trust it enough to change how they work.
I’ve seen that journey up close. I’d love to exchange notes with leaders who are serious about turning AI from strategy slides into real systems of intelligence.