Ankur Dhingra, CEO of ProHance – Interview Series

Ankur Dhingra, CEO of ProHance, is an experienced technology and operations executive with more than two decades of leadership across enterprise productivity, financial services, and global business operations. He has led ProHance since 2020, focusing on building a unified platform for observing, governing, and improving productivity across human workers, vendors, and AI systems. Before becoming CEO, Dhingra was a Partner at FutureIP, where ProHance was developed as an operations management and analytics platform. Earlier, he spent more than eight years at American Express, serving as Vice President and General Manager of Global Back Office Operations and previously leading customer engagement operations encompassing large-scale phone, chat, regulatory, and business assurance functions. His career also includes senior leadership roles at Genpact, where he managed a 1,100-person banking practice and a $35 million P&L, and GE Capital, where he led financial services operations in India.

ProHance is an enterprise workforce intelligence and productivity analytics company that provides organizations with real-time visibility into how work is performed across employees, external vendors, AI copilots, and autonomous agents. Its platform, positioned as a “Productivity Control Room,” combines workforce analytics, capacity planning, intelligent work allocation, productivity measurement, and governance tools to help enterprises identify operational bottlenecks, optimize resource utilization, and understand the impact of AI on organizational performance. ProHance says its technology is used by more than 450,000 users across 220 enterprises in 55 countries, with applications spanning banking and financial services, business process outsourcing, global capability centers, healthcare, and information technology.

You spent more than two decades leading large-scale operations at GE Capital, Genpact, and American Express before becoming CEO of ProHance. What did those experiences teach you about the limitations of traditional workforce and productivity management, and how have those lessons shaped the way you are building ProHance for an AI-first enterprise?

My experience across GE Capital, Genpact, and American Express taught me that one of the biggest gaps in traditional workforce management was not necessarily on the front end, but in the back office.

At American Express, I had previously worked on the customer-service side, where the contact center had access to fairly sophisticated technology to measure productivity – where people were spending their time, utilization, interaction volumes, and how processes could be optimized. But when I moved into leading back-office operations, I saw a very different reality.

A huge amount of work in large enterprises happens behind the scenes – transaction processing, case management, data entry and other process-driven activities – yet there was very little meaningful measurement of how that work was actually getting done. The technology and visibility that existed for the front office simply hadn’t translated to the back office.

I became almost irrationally obsessed with the problem. I started looking at the available technologies and speaking to established players, but what I found was either too clunky, too expensive, or simply not designed for the realities of back-office operations. I kept thinking: this is a $150 billion-plus industry, and this is a problem that has existed for years. Surely someone has to be solving it better.

That search eventually led me to ProHance, where Rajesh Sharma and Kishore Reddy (co-founders of ProHance); had already built technology that addressed many of the challenges I had identified as an advanced user of these systems. I knew exactly what the technology needed to do – capture activity automatically, work across different operating systems, measure time both on and away from the system, and provide a reliable view of how work was actually happening.

That experience shaped how I think about ProHance today. The lesson is that you cannot build an AI-first enterprise simply by adding AI to existing workforce-management tools. You first need a reliable, granular understanding of the work itself. AI can then transform how that work is analyzed, optimized and ultimately redesigned.

For me, ProHance is about bringing that same level of visibility and intelligence that enterprises have historically had in the front office to the entire organization – and using AI to get a better measure on productivity which will fundamentally improve how work gets done.

As generative AI and AI agents take over a growing share of knowledge work, traditional productivity signals such as hours worked or application usage may become less meaningful. How should enterprises redefine productivity when a human can accomplish in 30 minutes what previously took several hours?

Productivity has to move from measuring activity to measuring outcomes. If AI enables someone to complete in 30 minutes what earlier took three hours, the three-hour benchmark is no longer relevant. The focus should be on what was achieved, the quality of the outcome and how effectively time and skills were used.

This also means enterprises need to understand where AI is genuinely removing repetitive work and where human judgement remains critical. Productivity should increasingly reflect outcomes, capacity, quality and business impact, rather than hours spent at a desk or application usage.

You have described workforce intelligence as becoming increasingly critical in the AI-first enterprise. What does “workforce intelligence” actually mean in practice, and how does it differ from the employee monitoring and workforce analytics tools companies have used historically?

Workforce intelligence is essentially about giving an organisation a clearer understanding of how work is actually happening. It brings together signals around workload, capacity, utilisation, processes and outcomes to help leaders identify where teams are stretched, where capacity exists and where work is getting stuck.

That is different from traditional employee monitoring, which often focused on individual activity. Workforce intelligence should operate at a much broader level. The objective is not to ask, “What is this employee doing every minute?” but rather, “How is work flowing across the organisation, and what can we do to improve it?”

ProHance is increasingly combining deterministic workforce data with machine learning to generate predictive insights, including identifying potential disengagement and retention risks. How do you ensure these AI models distinguish meaningful patterns from normal variations in employee behavior, and how important is explainability when managers are making decisions about people?

At ProHance, we believe the quality and context of the underlying data are critical. Employee behaviour naturally varies depending on projects, workloads, teams, seasons and business cycles. A change in activity by itself doesn’t necessarily indicate disengagement or retention risk.

The approach is to look for patterns across multiple signals and over time, rather than drawing conclusions from a single behaviour. AI can help identify patterns that may warrant attention, but it should not be treated as the final decision-maker.

Explainability is equally important. If a system flags a potential risk, managers should be able to understand the factors behind that signal and apply their own judgement. When technology is being used to inform decisions about people, transparency and human oversight are essential.

There is understandable employee concern when organizations introduce technology capable of analyzing work patterns, application usage, idle time, or performance. Where should enterprises draw the line between gaining useful operational visibility and creating a culture of surveillance?

The line is largely determined by intent, transparency and how the data is used. Employees are understandably uncomfortable when technology is introduced without clarity on what is being measured and why.

The objective should be to understand work at an organisational and operational level, rather than constantly watch individuals. If workforce data helps identify excessive workloads, bottlenecks or inefficient processes, it can actually improve the employee experience.

Enterprises should also be clear about what they are not trying to do. Technology should help managers have better conversations with their teams, not replace those conversations or create a system of constant observation.

AI copilots and autonomous agents are increasingly becoming part of the workforce itself. Do you think workforce intelligence platforms will eventually need to measure the productivity of humans and AI agents together, and what might a meaningful human-to-AI productivity metric look like?

Yes, I think this will become increasingly important. As AI agents start taking on parts of workflows, measuring only human activity will give enterprises an incomplete picture of how work gets done.

But I don’t think we will simply arrive at a single “human versus AI” productivity score. A more useful approach would be to understand the combined output of people, AI tools and agents across a workflow.

Enterprises could look at how much time AI removes from repetitive tasks, how quickly processes move, the quality of outcomes and where human intervention is still required. The real question becomes: how effectively are humans and AI working together?

ProHance talks about moving beyond raw productivity measurement toward operational intelligence, including workload analysis, forecasting, process optimization, and resource allocation. How much of the future of this category will be about AI proactively recommending what managers should change rather than simply presenting them with dashboards?

I think that is where ProHance sees the category heading. Dashboards are useful, but they still leave managers with the responsibility of interpreting information and deciding what to do next.

The next stage is for platforms to identify a pattern and help answer “what should we do about it?” That could mean highlighting an overloaded team, identifying an underutilised skill set, forecasting a capacity gap or suggesting where a process could be improved.

For ProHance, the value will increasingly come from moving from visibility to action. AI should help managers make better decisions while keeping the decision itself with the person who understands the business and the context.

You have compared productivity intelligence to an annual health report for an organization. What are the most important signals executives should be looking at to determine whether their workforce is genuinely healthy, productive, and adapting successfully to AI?

I would look at a combination of signals rather than one productivity number. Capacity and workload are obvious starting points, but executives should also understand whether work is distributed effectively, where bottlenecks are emerging, how teams are adapting to AI and whether people are spending too much time on low-value activities.

Another important signal is sustainability. A team delivering exceptional output because people are consistently working beyond reasonable capacity isn’t necessarily healthy or productive in the long term.

A healthy workforce is one where capacity, workload and outcomes are reasonably balanced, people have room to focus on higher-value work, and the organisation can adapt as the nature of work changes.

“Productivity” has developed negative associations for many employees because it can imply doing more work with fewer people. How can organizations reclaim the concept and use AI-driven productivity intelligence to improve employee experience, reduce burnout, and remove low-value work rather than simply increase output?

Organisations need to move away from defining productivity simply as “more output from fewer people.” That definition is too narrow, particularly in an AI-enabled workplace.

AI should ideally give people back time that is currently consumed by repetitive administration, manual processes and low-value tasks. The opportunity is to use that time for problem-solving, customer interaction, innovation and work that requires human judgement.

If productivity intelligence is used to identify where people are overloaded or spending time inefficiently, it can actually become an employee experience tool. The goal should be to make work better, not simply make people work faster.

Looking ahead, as AI becomes embedded across almost every enterprise workflow, what do you believe will distinguish organizations that successfully augment their workforce with AI from those that deploy the technology but fail to translate it into meaningful productivity gains?

The organisations that benefit most from AI will be the ones that look beyond technology deployment. Having access to an AI tool doesn’t automatically create productivity gains.

The leaders will understand where AI is changing workflows, how roles are evolving and whether the organisation is actually getting better outcomes from the technology. They will also continuously measure what is working and adjust accordingly.

Ultimately, successful organisations will treat AI adoption as a change in how work gets done, rather than simply another technology implementation. They will combine AI with the right processes, skills, workforce visibility and management practices. That is what will determine whether AI becomes a genuine productivity advantage or just another layer of technology.

Thank you for the great interview. Readers who wish to learn more should visit ProHance.