The Rise of Agentic Execution: Rocketlane CEO Srikrishnan Ganesan on AI’s Next Phase in Professional Services

Artificial intelligence has already begun reshaping enterprise software, but much of its early impact in professional services has been concentrated in analytics dashboards, recommendation engines, and productivity copilots. While these tools improve visibility and individual efficiency, the responsibility for coordinating projects, executing tasks, and managing delivery outcomes has largely remained with human teams.

A new phase of AI adoption is now emerging—one where intelligent systems move beyond assistance to actively executing parts of service delivery workflows. This shift toward “agentic execution” introduces the possibility of AI handling repeatable project work, enforcing delivery governance, and identifying risks before they disrupt customer outcomes.

Rocketlane is positioning itself at the forefront of this transition with the launch of Nitro, which the company describes as the first agentic execution platform for professional services organizations. By embedding AI agents directly into delivery plans, the platform aims to automate repeatable implementation tasks while allowing consultants and delivery leaders to focus on strategy, customer alignment, and outcome design.

In this Q&A with ERP News, Srikrishnan Ganesan, CEO, Rocketlane discusses how agentic AI could reshape the economics of professional services, why execution has remained the missing layer in enterprise AI adoption, and how delivery teams may evolve as AI agents begin handling portions of implementation work. He also shares his perspective on the emerging “Outcome Era” and what it means for how services organizations measure success in the coming years.

From AI Assistance to Agentic Execution in Professional Services

Q: Rocketlane describes Nitro as the first “agentic execution platform” for professional services. What fundamentally changes when AI moves from assisting delivery teams to actually executing parts of the work?

A: With agentic execution, the system doesn’t just tell your team what’s going wrong or what to do next; it actually does the work that fits clear patterns and guardrails. Nitro’s agents sit natively inside your delivery workflows, execute repeatable tasks like configurations, data transformation, migrations, documentation, testing, and validations directly inside those plans. They also generate and adjust project plans, rebalance resources, and your team moves from chasing tasks and updates to supervising outcomes, stepping in to share context, creativity, or complex trade-offs that require human judgment.

This impacts the economics and timelines of projects in a radical way, getting customers to outcomes much faster, and unlocking margins for the business.

This shift also changes how you manage risk and scale. Instead of waiting for red flags in a dashboard, Nitro continuously monitors delivery signals and customer interactions, enforces policies automatically, and escalates when intervention is needed, so projects stay on track by default rather than by heroics.

In practice, you can handle more projects with the same headcount and deliver a more predictable customer experience.

Q: Many AI tools in professional services focus on dashboards, copilots, or recommendations. Why has execution remained the missing layer — and why is now the right moment to introduce it?

A: Historically, most AI in services investments have gone into three buckets: analytics and dashboards, copilots for individuals, and recommendation engines. They improve visibility or speed up a single person’s work, but they don’t change the fact that humans still have to coordinate across tools, teams, and timelines to get anything done. The result is many AI pilots, but very few that translate into sustained, measurable business outcomes.

Execution has been the missing layer because it’s harder, and because it just became possible. To do it safely and reliably, you need a platform that understands delivery context end to end, from scope, resources, constraints, and customer intent, and can operate within strict governance, financial, and compliance boundaries. We’re at a moment where the underlying AI, the data foundation inside modern PSAs, and the pressure on services teams have all converged. That makes it possible for AI to take on owned execution, instead of only providing guidance.

Q: Rocketlane says Nitro can reduce delivery effort by up to 50% on repeatable workstreams. Which types of services work are best suited for agentic execution today?

A: The sweet spot today is repeatable, well-defined workstreams where “how” you do the work is known, but “how fast” and “how consistently” are still challenges. For example, Nitro can execute large parts of implementation projects, such as reading SOWs and past projects to auto-generate tailored project plans, configuring systems, transforming data, running validations, and preparing documentation.

These are all areas where teams currently spend a huge amount of manual effort that doesn’t really differentiate their services unless you have automation to ensure speed and quality. By letting agents handle the grunt work within clear guardrails, you not only cut delivery effort by up to 50% on these tracks, but you also de-risk execution because the standard path is automated and consistent.

Rethinking Delivery Economics and the Shift to Outcome-Based Services

Q: You’ve introduced the concept of the “Outcome Era.” How should services leaders rethink success metrics when AI is responsible for completing work, not just surfacing insights?

A: In the Outcome Era, traditional metrics like utilization, hours billed, and even project margin are useful but no longer sufficient. When AI is executing parts of the work, leaders need to ask: Are we delivering the outcomes customers were promised, on time and in full, with less effort and less risk? That means tracking metrics such as time-to-value, attainment of outcomes vs. sold value, risk incidents avoided, and delivery reliability across segments and regions.

You also gain new levers at the unit level. You can measure effort per outcome, not just effort per project, and see how much of that effort is handled by AI versus humans. Over time, the benchmark shifts from “How many hours did we bill?” to “How much value did we create per unit of human attention?”

Nitro is built to give leaders visibility into that shift while enforcing the underlying operational rules.

Q: Professional services organizations often struggle with flat headcount, margin pressure, and delivery risk. How does Nitro change the traditional economics of scaling services teams?

A: Traditional services economics are linear: more projects require more people. When headcount is flat and margins are under pressure, that math simply stops working. Nitro changes that relationship by introducing scalable execution capacity that isn’t tied to headcount, so you can increase throughput and predictability without proportionally increasing cost.

Because operations automation, delivery governance, and work execution are all handled in one platform, you reduce the hidden costs of coordination, rework, and fire drills that typically erode margins as you grow. Nitro enforces resourcing rules, time policies, and financial controls in real time, so you’re doing more profitable, in-policy work that you can forecast and rely on.

Your expert human resources are now focused on the solutioning and customer alignment activities, while the execution of delivery is increasingly delegated to AI. This allows them to touch more customers and spend more time developing best practices and creating leverage for the company.

Q: Risk detection is a core promise of Nitro. What kinds of delivery risks can AI realistically surface weeks earlier than humans, and how does that impact customer outcomes?

A: In professional services, most big issues start as a series of small signals, like a stalled email thread, a change in stakeholder tone, a pattern of missed internal deadlines, or a subtle mismatch between what’s being worked on and what was sold as the outcomes. Nitro continuously mines activity across project plans, resourcing changes, and customer interactions to surface these patterns early.

That can mean spotting emerging scope creep, deteriorating sponsor sentiment, recurring blockers across similar projects, or early signs of an under-resourced critical path. Catching these weeks sooner gives teams time to replan, reallocate, and realign with the customer before trust is damaged or value erodes – one of the most direct ways agentic AI improves outcomes.

Q: Nitro operates across operations automation, delivery governance, and work execution. Which layer tends to deliver the fastest time-to-value for customers adopting the platform?

A: For most customers, the fastest time-to-value comes from operations automation and delivery governance. When you turn on Nitro’s automation around time policies and financial controls, you immediately remove a lot of manual oversight and copy-paste work across tools. Leaders also see quick wins when Nitro starts identifying risk signals and  auto-magically delivers account intelligence out of the box.

Work execution, agent configuration of systems, running tests, and handling migrations are incredibly powerful and high-impact, but they naturally involve deeper setup and integration into your products and ways of working. Documentation agents for hand-offs, design documents, etc., are easier to set up and deliver value faster at the execution layer.

Our approach to working with customers, though, isn’t to go after just the low-hanging fruit or the fastest time-to-value use cases. We believe solving the hard problems that have a high impact will create the most valuable outcomes for the teams we work with.

The Evolving Role of Services Teams in an AI-Driven Delivery Model

Q: As AI agents take on billable delivery tasks, how do you see the role of consultants and delivery managers evolving rather than being replaced?

A: Agentic execution doesn’t remove the need for consultants and delivery managers; rather, it elevates what they spend their time on. Instead of writing every status email, chasing tasks, or manually executing every test case, they become orchestrators of outcomes by defining the right success criteria, shaping implementation strategies, and coaching customers through change.

Their roles shift toward value engineering, outcome design, stakeholder management, and continuous improvement of the playbooks that Nitro executes. In practice, that means more time in high-value conversations – aligning on business goals, resolving complex tradeoffs, and identifying expansion opportunities – and less time buried in spreadsheets and ticket queues.

Q: From early customer conversations or pilots, what has surprised you most about how teams are using agentic execution in live client engagements?

A: One pleasant surprise has been how quickly teams start trusting agents with meaningful work once they see them operate within clear controls. In early conversations, we’ve seen initial inertia and a trust deficit when starting with agents, and it takes a hackathon or some form of forced exercise for them to experience the change and start trusting and delegating more to AI.

We’ve also seen that, for some use cases, an 80% accuracy outcome still adds immense value as long as the experience is purpose-built and provides easy ways to validate or fix the output. We had one customer use our documentation agent to create a 38-page design document that used to take them 15 hours, and they reduced the effort to just 2 hours.

Q: Looking ahead, how do you see agentic AI reshaping professional services over the next 2–3 years, and what role does Rocketlane aim to play in that transformation?

A: Over the next 2–3 years, I expect professional services to move decisively from intelligent delivery to truly outcome-owned, agentic delivery. AI agents will become standard in handling executional and administrative work, while human teams reorient around designing, measuring, and guaranteeing outcomes across the customer lifecycle.

This should also shrink effort and cost, increasing the appetite for services engagements in a big way.

Rocketlane’s role is to be the platform that makes this transition safe, reliable, and measurable for services organizations of all sizes. With Nitro, we’re combining PSA, collaboration, and agentic execution in one place so that every project, every stakeholder, and every outcome is managed coherently from signed to value delivered. If we do our job right, agentic execution will simply become how modern services teams operate, and customers will judge success by the outcomes they achieve, not the hours it took to get there.