Sarah Edwards, Chief Product and Strategy Officer, Kantata – Interview Series

Sarah Edwards, Chief Product and Strategy Officer, Kantata, brings more than 27 years of experience in professional services, consulting, product strategy, and business leadership. Throughout her career, she has worked across both North America and Europe, building and scaling services organizations while developing deep expertise in project delivery, resource management, and operational excellence. Prior to joining Kantata, Edwards held leadership roles at Hitachi Consulting, where she managed global teams, and earlier helped grow consulting firms including Fulcrum Solutions and Edenbrook. At Kantata, she is responsible for guiding product and strategy initiatives across the company’s portfolio, helping professional services organizations improve visibility, forecasting, resource allocation, and overall business performance through purpose-built technology solutions.

Kantata is a leading provider of professional services automation (PSA) software, focused exclusively on the needs of consulting firms, agencies, IT services providers, and other project-based organizations. Formed through the merger of Mavenlink and Kimble, the company offers an AI-powered platform that connects project planning, resource management, financial operations, forecasting, collaboration, and business intelligence within a unified environment. Its technology is designed to help organizations improve project profitability, optimize workforce utilization, increase forecast accuracy, and gain real-time visibility across the entire service delivery lifecycle. By combining operational data with AI-driven insights, Kantata helps professional services firms make more informed decisions, improve delivery outcomes, and scale efficiently in increasingly complex business environments.

You began your career as an Oracle consultant before leading Oracle practices at Edenbrook and Hitachi Consulting and later helping shape Kantata’s product strategy over more than a decade. How has that journey influenced your view of where AI can create the most value for professional services firms, and what lessons from those earlier consulting environments still apply today?

Looking back at my career, the professional services industry didn’t really substantially change for 30 years. The rulebook was to grow the business by growing headcount; the more employees, the more projects could be taken on. But now, that playbook is being completely rewritten thanks to AI and new tech. I’m hearing a lot more uncertainty in the industry right now than ever before. What does our talent look like? What do I need across people and agents? How do I bill AI agents? No one knows the true answers to these questions yet.

What we do know is that if companies stick to the old operating model of trying to grow revenue with headcount, it will just be a race to the bottom. From my experience in premier consulting environments, I know that that consulting is still all about human relationships, but in the age of AI, professional services firm are under more pressure from clients to do more with less and produce stellar results. They’re constantly having to prove their value. This is where AI can help, by leveraging data and integrated expertise to build repeatable and consistent intelligence.

You’ve argued that many organizations are measuring AI success incorrectly by focusing on productivity and efficiency. Why do you believe “expertise compounding” is a more meaningful metric for evaluating AI ROI?

Focusing solely on productivity and efficiency gains does not adequately measure long-term AI success. AI activation is about more than just automating the status quo; it’s about fundamentally changing the way we operate. To me, an organization’s expertise compounding rate – its ability to capture, synthesize, scale, and continuously build on institutional and tribal knowledge – is much more meaningful as it allows firms to enact expertise at scale. Expertise is the primary currency of the consulting world, but if those specific skills and specialized knowledge remain siloed or in one person’s brain, it’s not an effective system.

Historically, the professional services industry has run on heroics – standout employees that have innate skills or knowledge that clients can tap. In today’s day and age, firms won’t survive operating that way. Expertise must be shared widely, accurately, and quickly, which AI is already doing to an extent. Take, for example, AI tools that automatically take notes during meetings and disseminate them after. The challenge for professional services firm is that this output is not directly connected to providing services to clients. That’s why there’s a need for an AI tool that takes the next step and activates on this intelligence within the context of the specific organization. If AI can learn from and compound the expertise the firm is delivering across projects, that’s when it truly becomes a transformational technology.

What are the biggest misconceptions executives have when they attempt to calculate the return on investment of AI initiatives?

Executives primarily talk about AI ROI in terms of cost saving and efficiency, but it’s a misconception to think that this is the only place where AI can drive value. They should also determine how AI is driving revenue growth by developing new revenue pipelines, helping retain more clients, or allowing the firm to take on more projects.

How can organizations begin capturing and scaling institutional knowledge that currently exists only in employee conversations, project experiences, and tribal knowledge?

Many professional services firms still rely on spreadsheets, siloed data, and tribal knowledge to run their business. Slapping a one-off AI agent with generic training on top of one project or data stream is not going to have much impact. An integrated platform with AI and predictive insights embedded that can act across projects and workflows will. But not all platforms are created equal. To have impact, they must be built on a unifying knowledge graph that captures institutional knowledge at scale, puts it in context and gives AI agents the autonomy to take action based on the insights, with humans in the loop.

Many companies are deploying AI assistants and agents across the enterprise. What separates organizations that are creating lasting competitive advantages from those that are simply automating existing workflows? 

At the start of the AI boom, many organizations rushed to implement AI tools to avoid being left behind, but this kind of unintentional deployment led to many companies feeling agent bloat (i.e., when agents become slow, expensive to run, or inaccurate due to mismanagement and an overload of tools or information). What’s more, all the agents are working in silos and there’s no long-term strategy about how to operationalize all the agents together.

The organizations that are laying the groundwork for true, long-term competitive advantage are not deploying agents that look at just one specific thing; they are deploying agents that have visibility across the board. Staffing, resourcing, delivery, etc. are all moving parts in the professional services machine that must work together to keep things running smoothly. And they are giving this agent the context, knowledge, and understanding needed to make a real impact.

How do you see AI changing the operating model of professional services firms over the next five years, particularly around consulting, implementation, and advisory work?

AI is changing the talent model. Skills are evolving faster than ever, and there’s a big question mark next to where the next tier of talent is coming from. Some are also concerned that AI will drain human workers of their creativity and cognitive thinking; therefore, I anticipate there will be a shift in how firms engage their employees to ensure they are flexing these muscles and not over relying on AI output. At the end of the day, AI is an enabling force that frees employees from tactical, mundane tasks so that they can focus on strategic work that makes a difference.

As AI becomes more capable, do you expect firms to shift away from billing based on hours and utilization toward more outcome-based pricing models? What challenges stand in the way of that transition?

When professional services firm get a Request for Proposal, it’s become standard for prospects to ask about how the firm is reducing costs using AI and how those cost savings provide value to the customer. What’s often missing is a mechanism for measuring that cost reduction, and a narrative for conveying to prospects how AI is elevating the firm’s existing expertise and the value they deliver.

Ultimately, I do think the billing model is moving toward outcome-based pricing. Today, most organizations lack the delivery discipline, data, and financial readiness to support true outcome-based pricing at scale. Add AI into the mix, and the model becomes even more complex – when work is done by agents, how do you track effort, prove value, or recognize revenue? You can’t price outcomes if you can’t predict delivery. The real transition won’t be a jump straight to outcome-based pricing, it’ll be a progression from effort to fixed fee to value to outcome-based selling as organizations build the foundation that they need to monetize outcomes.

Kantata has been vocal about turning organizational knowledge into a strategic asset through its Expertise Engine. What prompted this focus, and what gaps did you see in traditional knowledge management systems?

Enterprise AI today is largely focused on generic AI tools designed to summarize meetings, answer prompts, or automate isolated tasks. And when it comes to professional services, it doesn’t work. The real challenge professional services firms face isn’t task automation. It’s operational complexity.

Professional services firms run on a constantly shifting web of staffing decisions, delivery tradeoffs, project risk, utilization targets, forecasting models, and financial dependencies. Generic AI tools don’t understand that context, which means they can’t reason across the business in a meaningful way.

Kantata is taking a different approach to AI. Instead of layering a generic agent or chatbot onto existing software, we are building AI tailored to the operational realities of professional services. Our Expertise Engine connects estimating, staffing, delivery, forecasting, and financial management into a single operational model, giving AI agents full business context to act intelligently across workflows.

This is not yesterday’s copilot AI. It’s operational AI.

What role will AI agents play in professional services organizations, and where do you believe human expertise will remain irreplaceable?

As AI agents continue to take some of the workload off of human workers, it will open them up to do the more fun and creative parts of their jobs. Automation isn’t just about efficiency and time/cost savings, it’s about relief and empowerment for employees. This will elevate their creative side and build a new generation of thinkers, who will become the builders of AI tools as opposed to just users. This shift will be essential as AI becomes ubiquitous because every firm will be able to produce output quickly and cheaply, but only those that continue to invest in their human expertise will produce strong, meaningful work.

Looking ahead, what should business leaders be doing today to ensure their organizations are not just adopting AI, but building a sustainable expertise advantage that compounds over time?

Instead of layering a generic agent or chatbot onto existing software, professional services firms should seek out AI superagents that have the ability to understand context across everything that goes into running a professional services firm – from staffing to forecasting to financial management and project delivery – and create purpose-built agents to that can act autonomously.

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