Srikrishnan Ganesan, CEO of Rocketlane, is a leading voice on modern delivery operations. Learn more about the future of services and delivery in The 2026 PSA Outlook: Predictions and Trends Every Services Leader Must Know.
Q: To start, could you introduce yourself, your background, and what Rocketlane does?
I’m Sri, founder and CEO of Rocketlane. It’s my second startup with the same co-founders. Our first company, a messaging product we built from 2012 to 2015, was acquired by Freshworks, where we stayed until 2019. The pain we felt scaling upmarket—opaque implementations, misaligned expectations, avoidable escalations—inspired Rocketlane: a purpose-built platform unifying customer collaboration, delivery execution, and services ops (timesheets, staffing, planning, revenue recognition). Rocketlane is an all-in-one platform for services teams across front office and back office needs to create a transparent workspace and more profitable business.
Q: AI is everywhere, yet many companies aren’t seeing real returns. What’s misunderstood about where AI’s value comes from?
AI ROI comes from workflow redesign, not novelty tools and micro-optimizations. When you give folks simple agents for micro tasks, it actually creates more overhead via instruction tax and orchestration tax, if not more. The standout gains come when you re-architect processes – rethink even how the work needs to be done, where the hand-offs are, which systems interact with the agents, etc vs replacing current human tasks. McKinsey and MIT studies show double-digit productivity and quality lifts when AI is integrated into end-to-end workflows, not just used as a drafting aid. The delta is adoption rigor: training, governance, and clear outcome targets.
Q: You’ve said the next wave of AI growth will be driven by services, not software. Why?
AI value is context-dependent. Moving from demo to durable ROI needs discovery, re-engineering, data plumbing, readiness work, change management, and ongoing tailoring/configuration/tuning — classic services work. That’s why OpenAI, Anthropic, and cloud hyperscalers are hiring field and consulting teams. The economic center shifts to outcomes: playbooks, reference architectures, and co-delivery that take customers across the “last mile” to production. Hence the rise of agent PMs, agent engineers, and forward-deployed teams at major AI companies who are also discovering these playbooks and reference architectures at this time.
Q: What’s really blocking AI ROI today?
Three blockers show up again and again: change debt (processes never get redesigned), data debt (fragmented, low-quality data), and adoption debt (teams aren’t trained or incentivized to use the new workflows). The organizations that do see ROI, often treat AI like a product: they set measurable business goals, assign clear ownership, build the right data pipelines, and run enablement with the same rigor as a major rollout. Without that, pilots stall and never compound.
A fourth blocker is leadership distance. When executives are not hands-on enough to understand what to drive or where to look for early signals of impact, the program loses momentum before it ever proves value.
Q: Why is post‑sale delivery now a major competitive differentiator?
In the AI era, sales pitches promise big ROI, but the product alone rarely delivers it. Customers need customization, context, and workflow redesign to reach the outcomes they were sold. That is where delivery becomes a true differentiator: services teams bridge the gap between the product and the promised vision.
Speed is another competitive lever. When you move an implementation timeline from nine months to six–eight weeks, you transform customer appetite, effort, and perceived value. AI forward post-sale delivery also aims to expand your impact. Instead of touching only 15 percent of customers, an ambitious and radically efficient PS motion can target engaging 60–70 percent annually—continually driving outcomes, expansion, and long-term loyalty.
Q: How is AI reshaping professional services and implementation? What opportunities open up?
AI is reshaping services by driving radical efficiency: shorter timelines, lower effort, and much higher throughput. Tasks like documentation, configuration, testing, and data transformation can now be automated, letting experts spend their time on solutioning, decision-making, and higher-order problem solving, with AI augmenting those activities as well.
The impact is significant. Cycle times shrink by weeks. Utilization improves without burning out teams. More customers adopt the product fully, activate more capabilities, and realize value faster. All of that compounds into better business outcomes: lower churn and higher NRR.
Q: What do you mean by “intelligent delivery” and “agentic teams”? How will this change onboarding and projects?
This is the move from “human professionals led work” to “professionals + AI agents” for configuration, documentation, testing, data work, and other PS efforts. With guardrails and human review, teams run more workstreams in parallel, shrinking projects from quarters to weeks, executing with high quality, and building organizational knowledge for customer-facing teams to access.
Q: Looking ahead to 2026, how will companies adopt and scale AI—and where does Rocketlane fit?
By 2026, companies will be scaling AI through repeatability and embedded intelligence. Delivery teams will rely on golden paths for their highest-value use cases, reusable accelerators to avoid reinventing the wheel, and account-level knowledge graphs that capture configuration, context, and history. Support will be LLM-powered and tuned to each customer’s unique environment. Automation rates will jump, and many change requests will shift from ticket queues to near real-time execution.
Rocketlane’s role is to be the operating system for this new era of post-sale delivery. We provide the structured knowledge, workflows, and agent orchestration needed across onboarding, professional services, and ongoing account intelligence—so teams can scale AI, deliver outcomes faster, and drive compounding value.
Q: Personally, what excites you most about this phase of AI? Where’s the biggest opportunity for teams that get delivery right?
Better knowledge access and tools enables more creativity and higher ambition. Getting delivery right builds very compelling outcome stories and a reusable knowledge base that compounds value for the business. Doing that with efficiency gives you the ability to touch far more customers with this value. This turns services into a scalable strategic moat: faster value, tailored experiences, and credible proof of outcomes.
