AI-native CRO Breaks Down His Highest-leverage AI Workflow (And Where AI Isn’t Worthwhile)
Discover how an AI-native CRO uses intelligence-led workflows to improve pipeline quality, reduce research time, and help revenue teams focus on context over activity.
Revenue Insight: Dhiraj Khare emphasizes the need for human judgment in revenue decisions, even with AI's support.
AI Engagement: Transitioning to intelligence-led engagement enhances pipeline quality and understanding of account context.
Operational Efficiency: Matters.AI focuses on reducing operational drag and increasing signal density to drive effective engagement.
Cultural Shift: AI promotes analytical thinking in revenue teams, shifting conversations from opinion-driven to evidence-based.
Skill Evolution: Modern revenue professionals must develop analytical, storytelling, and AI fluency skills to succeed.
Dhiraj Khare is a CRO with a technical background and mindset. He’s currently redesigning revenue processes at the AI-native cybersecurity company, Matters.AI.
We sat down with Dhiraj to learn which AI workflows are pulling their weight and which ones aren’t. Here’s what he told us.
Revenue from an engineer’s perspective
I am Dhiraj Khare, CRO at Matters.AI, an AI-native cybersecurity company focused on enterprise data. I started in software development before I moved into enterprise sales, and that shaped the way I think about revenue. Technical people are trained to stay with the problem until they actually understand it; sales teams are often trained to move before they understand it. I never fully fit into the latter model.
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My career moved through companies like MongoDB, SentinelOne, and PingSafe. Different companies, different stages, yet the same pattern underneath. Cybersecurity teams were already operating at machine speed, revenue teams weren’t.
Cybersecurity had real-time intelligence, behavioral analysis, and automated response systems. GTM teams were still buried under spreadsheets, disconnected tooling, manual research, and pipeline reviews built on optimism disguised as forecasting.
This disconnect stayed with me. A lot of people frame AI in revenue as a productivity story. Faster emails. More outbound. More sequences. I think that misses the point entirely.
The problem in modern GTM is not a lack of activity, it’s too much noise. Most outbound today is operationally expensive spam pretending to be personalization. Buyers know it immediately. Everybody has the same automation now, so volume stopped being a real advantage a while ago.
What matters is context. Why this account? Why now? What changed inside the business that makes this conversation relevant today instead of six months ago?
That’s what pushed me toward AI-driven revenue leadership. At Matters.AI, we’re building an intelligence-first GTM organization where AI helps compress research, connect fragmented signals, surface timing indicators, and reduce operational drag so teams can spend more time thinking clearly and engaging intelligently.
Dhiraj's Thoughts
Everybody has the same automation now, so volume stopped being a real advantage a while ago…What matters is context. Why this account? Why now? What changed inside the business that makes this conversation relevant today instead of six months ago?
Matters.AI’s GTM organization is intentionally lean. We operate across enterprise sales, strategic outbound, partnerships, solution engineering, and AI-assisted workflows, but the philosophy underneath everything is simple: scale intelligence before you scale headcount.
A lot of companies still try to brute-force growth through activity volume. More SDRs, more sequences, and more touches. We’ve taken almost the opposite approach.
We care more about signal density than activity density. The focus is on understanding where urgency actually exists, where timing is real, and where customer context is strong enough to justify engagement.
Most of our work today sits around AI governance, data security, compliance readiness, and enterprise risk management across sectors like BFSI, SaaS, technology, and regulated industries.
Why AI cannot replace revenue judgment
I use AI to inform account prioritization, stakeholder mapping, deal scoring, engagement analysis, forecasting support, churn detection, and pipeline risk analysis.
Machines are far better than humans at processing fragmented operational signals at scale. That part is obvious now.
But decisions that involve executive alignment, negotiation and trust stay human. Strategic judgment definitely stays human.
Enterprise cybersecurity buying decisions are emotional underneath all the spreadsheets and procurement workflows people like to hide behind. A deal can look healthy operationally, while trust is quietly collapsing inside the account.
AI still struggles with that layer. So, I don’t see AI replacing revenue judgment, it's more about reducing operational blindness.
How AI enables intelligence-led engagement
The biggest change I’ve made was moving away from activity-led outbound toward intelligence-led engagement.
Earlier, outbound looked like what most organizations still do today, a mix of large account lists, static sequencing, manual research, broad personalization templates, and a lot of wasted effort pretending to be precision.
We built an AI-assisted account prioritization workflow that compressed hours of research into minutes.
Now, AI continuously aggregates signals across hiring activity, security posture shifts, cloud adoption, compliance pressure, executive movement, funding patterns, organizational changes, and technology adoption indicators.
Then it synthesizes those signals into contextual account narratives. That part matters because raw signals alone are useless. Sellers don’t need more dashboards; they need clearer understanding.
Once the context is surfaced, the seller validates it manually, pressure-tests relevance, personalizes the engagement, and decides whether the timing actually justifies outreach. After meetings, AI assists with summarization, action-item extraction, stakeholder analysis, engagement tracking, and risk monitoring across the deal cycle.
The point is not to automate outreach. It's to understand whether the account actually deserves attention in the first place. That changed pipeline quality immediately.
Conversations became sharper. Discovery cycles tightened. Executive engagement improved because outreach was grounded in context instead of manufactured personalization.
How AI changes culture
The upside of AI was obvious pretty quickly, and I already mentioned a lot of it.
However,the bigger change was cultural. People started thinking more analytically about revenue execution. Conversations became less opinion-driven and more evidence-driven. This shift matters more than most tooling conversations happening in the market right now.
The downside is that AI can create false confidence very easily. That’s the part people underestimate.
Often, AI outputs often sound polished enough to feel trustworthy even when the underlying assumptions are weak or incomplete. Leaders need to remember that AI is probabilistic, not omniscient. Judgment still matters. Operator experience still matters.
In many ways, critical thinking becomes even more important in an AI-native environment.
AI outputs often sound polished enough to feel trustworthy even when the underlying assumptions are weak or incomplete. Leaders need to remember that AI is probabilistic, not omniscient. Judgment still matters. Operator experience still matters.
AI-generated personalization underperformed badly in the beginning. Not because the technology was weak, but because buyers have become incredibly good at detecting fake relevance. Most AI-generated outbound today sounds polished but emotionally empty. It mirrors the structure of personalization without actually understanding the customer deeply enough to say something useful.
There’s a difference between inserting context and demonstrating understanding. And most AI systems still struggle with that distinction. We learned pretty quickly that synthetic personalization is not the same thing as insight.
The irony is that as AI-generated outreach scales, authenticity becomes more valuable, not less.
Why AI can’t take the ambiguity out of forecasting
With AI, our forecasting became more disciplined. But I want to be careful not to oversell that part because enterprise forecasting is still messy by nature.
Enterprise deals move for reasons that are rarely captured cleanly inside structured systems. Budget timing changes. Internal champions lose influence. Executive priorities shift quietly. Procurement friction appears late. Political alignment breaks underneath the surface.
AI struggles with those invisible layers. You can have strong engagement metrics, active meetings, healthy response rates, and positive stakeholder participation while the deal is already drifting internally.
AI improves visibility. It does not magically remove uncertainty.
Dhiraj's Thoughts
The irony is that as AI-generated outreach scales, authenticity becomes more valuable, not less…AI improves visibility. It does not magically remove uncertainty.
How revenue skillsets are changing
AI is increasingly automating repetitive operational work, so a modern revenue professional needs a very different skill profile. Now, we prioritize analytical thinking, contextual storytelling, systems thinking, executive communication, adaptability, and AI fluency much more heavily than before.
The best sellers today are becoming intelligence orchestrators. They know how to validate signals, synthesize context, ask better questions, and build strategic trust faster.
Pure activity-based selling is becoming commoditized. Human differentiation is shifting toward judgment, creativity, and narrative clarity.
Why AI must be treated as an operating model shift
My advice is to treat AI as an operating model shift, not a tooling upgrade.
That’s the mistake most organizations are making right now. They are layering AI on top of broken workflows and calling it transformation. Usually, that just creates faster confusion.
If your positioning is weak, your CRM discipline is inconsistent, your workflows are fragmented, or your customer understanding is shallow, AI amplifies the mess. It does not rescue you from it.
The companies that benefit most from AI are usually the companies that already operate with strong fundamentals.
Where revenue teams should start with AI
Start with operational friction and bottlenecks: Research overload. Pipeline inspection. Knowledge retrieval. Forecast visibility. Account prioritization. CRM fragmentation.
Those are real operational problems worth solving. I’d also spend far more time on AI literacy than most companies currently do. The technical implementation is usually the easier part. The harder part is helping teams rethink how they work without making them feel replaced.
