Pratima Arora, Chief Product and Technology Officer at Smartsheet – Interview Series

Pratima Arora, Chief Product and Technology Officer at Smartsheet, is a seasoned product and technology executive with a track record of leading high-growth platforms and scaling global teams. In her current role, she oversees product management, marketing, user experience, pricing, and strategic partnerships, helping drive the evolution of Smartsheet’s AI-powered work management platform. Prior to this, she served as Chief Product and Technology Officer at Chainalysis, where she led engineering, data science, and product strategy while significantly expanding the organization and accelerating revenue growth. Her earlier leadership roles include heading the Confluence business at Atlassian and driving AI-powered product innovation at Salesforce, building a reputation for delivering scalable, customer-centric solutions across enterprise software.

Smartsheet is an AI-powered, enterprise-grade work management platform designed to help organizations plan, track, automate, and report on work at scale. The platform enables teams to streamline workflows, collaborate in real time, and gain actionable insights through automation and data-driven tools, supporting a wide range of use cases from project management to enterprise operations. Headquartered in Bellevue, Washington, Smartsheet serves millions of users worldwide, including a large share of Fortune 500 companies, positioning itself as a key player in the evolving collaborative work management space.

You stepped into Smartsheet in 2025 after leading product and technology at Chainalysis and holding senior leadership roles at Atlassian and Salesforce. Now that your role has expanded to Chief Product and Technology Officer, how are you bringing that cross-industry experience into Smartsheet’s next chapter?

I’ve been a B2B SaaS leader for more than 20 years and have seen major waves of innovation – from the internet, to cloud, mobile, and social. AI is a much bigger transformation, both in scale and speed, and my focus is on helping Smartsheet navigate that shift and turn it into a real advantage for our customers.

Externally, that means accelerating how we embed AI into the product experience–helping teams work faster, make better decisions, and drive outcomes at a scale that wasn’t possible before.

But AI is also changing how we build. Product and technology are converging, and the lines between functions are blurring. Designers are getting closer to code, engineers are contributing to product definition, and teams are becoming more hands-on builders. A big part of my focus internally is bringing that builder mentality into how we operate, with an AI-first approach to development, and doing so with pace. That allows us to move faster as a unified team and translate innovation into meaningful outcomes for our customers.

Smartsheet has been positioning itself around the idea of work management. How do you define that concept today, and what separates it from the broader wave of AI features being added across enterprise software?

Work management is where people, processes, and data converge–with AI as the execution layer that turns plans into outcomes.

Analysis of 1.4 million active enterprise projects across the Smartsheet platform reveals a critical imbalance: automation intensity per enterprise account is up 55% year over year, and overall activity is up 46%. Work is being initiated at a pace that would have been unimaginable three years ago. But finishing work — coordinating across teams, maintaining alignment as priorities shift, making the judgment calls that keep execution on track — that’s where most organizations are drowning. The workday is getting denser, and the organizations feeling it first are those where priorities, ownership, and decision rights still live in people’s heads instead of in the system.

Where many approaches fall short is that AI is layered on top of workflows rather than embedded within them. It can assist with individual tasks, but it can’t orchestrate outcomes across teams or the full enterprise.

Our approach is different. We ground AI in enterprise data and integrate it directly into workflows so it can operate with real context — the relationships between projects, the intent behind the plan, and the judgment encoded in how the work has been structured. That’s what allows AI to orchestrate execution, not just assist with a task, and ultimately drive meaningful business impact.

In your November 2025 vision for Smartsheet’s future, you described a platform that brings together people, data, and AI in a more unified way. What did you feel was missing from existing work management tools that pushed you toward that direction?

We saw a persistent gap between planning and execution, especially at the enterprise level. Teams were working across multiple disconnected systems, which made it difficult to stay aligned or get a clear, real-time view of progress.

Many tools were solving for parts of the problem — planning, workflows, or collaboration – but they remain disconnected. Each was addressing a problem within their individual stack or system, but not across the full company. The fragmentation becomes a real barrier when you’re operating at scale. That’s where Smartsheet shines.

Our focus has been on bringing those elements together into a single, unified system so teams can stay aligned, adapt quickly, and execute more effectively.

One of the more interesting parts of that vision was the move toward AI systems that can understand context across projects, workflows, and teams. How important is context in making enterprise AI genuinely useful rather than just impressive in demos?

AI that understands context is fundamentally different from AI that generates content. Frontier models generate. Systems of record store. But neither one comprehends how your organization actually works, the dependencies, the intent behind the plan, or the judgment calls embedded in every workflow. That’s the layer Smartsheet occupies.

Smartsheet comprehends the operational shape of your business and puts AI to work inside it. When you ground AI in that kind of understanding, it shifts from being reactive to becoming an intelligent layer in execution. It’s not just responding to prompts. It’s operating with a comprehension of how the business actually runs, and that comprehension compounds over time.

Every plan, every workflow, every decision captured in Smartsheet becomes an intelligence asset that makes AI more useful in that specific organization. The context, intent, and judgment your teams have been building for years–those are the three things AI cannot generate on its own.

Smartsheet’s Model Context Protocol server suggests a shift from AI that simply answers questions to AI that can interact with live work data. From your perspective, what makes that a meaningful turning point for enterprise software?

This is a shift from AI that informs work to AI that can act on it. With the Smartsheet MCP Server, companies are no longer locked into a single AI tool; the protocol works with the AI models already embedded in their workflows, whether that’s Claude, Gemini, ChatGPT, or others. Teams can now connect directly to live work data and operate within the systems where work actually happens, enabling them to move beyond chat into execution. As the MCP ecosystem expands, we’ll extend support to additional leading models, ensuring Smartsheet remains compatible with whatever AI solution teams choose. When AI has access to real-time data across projects and workflows, it can surface risks earlier, support better decision-making, and take action, like creating tasks or updating work.

The early signal is clear. Within the first 30 days of launch, thousands of Smartsheet users completed 1.76 million actions through the Smartsheet MCP Connector for Claude. And a significant portion of those interactions weren’t about retrieving information – they were moving work forward. Creating tasks. Updating plans. Acting with context.

That’s what makes this a turning point. AI becomes embedded in existing workflows people already use, enabling organizations to move from individual productivity gains to coordinated execution at scale. The companies whose operational foundation already lives in Smartsheet are compounding that advantage right now. For example, teams are turning meeting notes into tasks automatically, with the model even inferring to whom the task should be assigned based on conversation context, so that decisions made in a room become tracked work in Smartsheet without a single manual entry. That’s coordination at scale – not because people worked harder, but because the system finally kept up.

When AI is connected to operational systems and live business workflows, trust becomes critical. How are you thinking about security, governance, and auditability as AI becomes more action-oriented inside the enterprise?

Trust, security, and governance are essential to any true enterprise adoption. As AI becomes more action-oriented, trust isn’t optional – it’s foundational. For us, that starts with ensuring AI adheres to the same governance model as everything else on the platform. It follows existing permissions, so it can only access and act on the data it’s explicitly allowed to. Your data stays your data.

Equally important is visibility. Organizations need to understand how AI is interacting with their systems, what actions are being taken, by whom, and in what context. That’s why auditability is built in: every action, whether initiated by a person or AI, can be tracked and reviewed. We’re also thoughtful about where autonomy makes sense. For higher-impact actions, we build in human-in-the-loop controls, so teams can review and approve before anything significant happens.

The goal is to give organizations the confidence to let AI move work forward, while still maintaining the control, transparency, and accountability they expect at enterprise scale.

Smartsheet has also emphasized open architecture, including support for external AI ecosystems. Why do you believe openness and interoperability will matter so much in the next phase of enterprise AI adoption?

Enterprise work doesn’t live in a single system. It’s spread across tools, teams, and data sources. If AI can’t connect to that environment, it stays limited. It might generate answers, but it can’t actually help drive execution.

That’s why openness matters. It allows AI to connect to live data across systems and operate with the full context of how work actually happens. With MCP, companies apply their preferred corporate AI standards and governance to work in Smartsheet, rather than adopting new tools or working in silos.

That’s the shift. When AI can work across systems, it moves from isolated interactions to actually supporting how the organization runs. That’s where you start to see real impact at scale.

Your product vision also introduced ideas like Smart Assist, Smart Agents, Smart Flows, and a knowledge graph layer. Which of those capabilities do you think has the most potential to change how teams actually work on a day-to-day basis?

It’s less about any one of those in isolation, as they’re really just different ways people interact with the system.

The real power lies in the intelligence beneath, powered by our data layer and the Smartsheet Knowledge Graph. That’s what gives the system context across projects, workflows, and teams, and allows it to understand how work actually connects. That context is what makes everything else work. The Smartsheet Knowledge Graph already maps relationships across work at a significant scale, with over 100 million nodes. That allows us to layer in context, from industry best practices to organizational, team, and individual data, so the system can deliver much more relevant insights than a standalone model.

From there, it shows up in different ways. Sometimes it’s an assistant helping someone understand project status or surface risks. Sometimes it’s an agent taking action, like creating timelines or updating work. Sometimes it’s coordinating workflows across systems.

But they’re all grounded in the same operational foundation – the accumulated context, intent, and judgment of how work has actually been done. That’s what actually changes day-to-day work, not a single feature, but a system that comprehends your organization.

Many enterprises are still struggling to measure whether AI is delivering real business value. How should leaders think about ROI when the goal is not just faster outputs, but better decisions, stronger execution, and less operational drag?

Many organizations start by measuring AI adoption, such as the number of people who use the UI daily. That is a useful signal, but it is not the full picture. The real value shows up in execution, and that is where many teams are still trying to catch up.

In most enterprises, the challenge is not generating outputs. It is coordinating work, staying aligned across teams, and making decisions with the right context. If those things do not improve, faster outputs do not necessarily translate into better business outcomes.

When AI is connected to the system of work, that is where you start to see a different kind of impact. It can help surface bottlenecks earlier, improve visibility into what is actually happening, and drive more consistent ways of working across teams.

So ROI is not just about speed. It is about how effectively an organization executes at scale, with more clarity, accountability, and predictability. That is what ultimately translates into measurable business value.

Looking ahead, how do you see the role of product leaders changing as AI becomes a core layer in enterprise platforms? Does building for an AI-native future require a fundamentally different mindset than traditional software product leadership?

There are four things I think about here.

Product leaders need to embrace AI with intellectual curiosity and a growth mindset. The field is changing quickly, so the ability to learn and adapt is critical.

Second, first principles and platform thinking become even more important. Getting the foundational elements right, especially around data and governance, that enables teams to experiment quickly and safely.

Third, customer focus is just as important. There is a lot of noise in the market right now, and not everything being labeled as AI or agents is delivering real value. Leaders need to stay grounded in solving real problems rather than chasing something new for its own sake.

And finally, there is a real shift in how teams build. The lines between functions are blurring, and more people are becoming builders. Product leaders who lean into that and genuinely engage with the technology will succeed.

Thank you for the great interview, readers who wish to learn more should visit Smartsheet.