ketteCrew Spotlight with Bhuva Mirdha

When supply chain leaders talk about modernization, the conversation usually starts with technology, AI automation, visibility, and faster decision-making. But underneath all of it is a much more practical reality: most organizations are still trying to solve deeply human problems inside increasingly complex planning environments.

Disconnected systems. Institutional knowledge trapped in spreadsheets. Slow decision-making. Workarounds layered on top of workarounds.

That is why I was excited to sit down with Bhuva Mirdha, Sr. Director of Solutions at ketteQ, for the latest edition of our ketteCrew Spotlight series. Bhuva brings a rare perspective to the conversation because before helping companies modernize supply chain planning environments, he lived the reality of running them himself. Prior to joining ketteQ, he led large-scale supply chain operations and experienced firsthand the pressure planners face every day when systems fail to keep pace with the business.

Over the last four years at ketteQ, his role has evolved from implementing planning solutions for customers to helping shape the next generation of Intelligent Planning Agents inside ketteQ’s AI Studio. In our conversation, Bhuva shares what companies are still getting wrong about AI in planning, why human judgment remains essential, and what the future of human-agent planning teams could look like much sooner than most people realize.

Here is our conversation:

Q: Tell us about your background and your role at ketteQ today.

I come from the same world our customers live in today. Before joining ketteQ, I was leading large-scale supply chain operations and living the same challenges planners face every day. That experience changed how I approach solution design because when customers describe a problem, I have usually experienced some version of it myself.

When I first joined ketteQ, my role focused heavily on implementing planning solutions for customers across different industries. Over time, that evolved into more of an advisory role where I now help customers and partners improve workflows, drive adoption, and get more value from their planning environments.

More recently, I also became involved with our AI Studio, which has been incredibly exciting. Now I get to help build the AI tools I used to wish existed when I was running supply chain operations myself. That shift from being the user to helping create the solution has been one of the most rewarding parts of my career.

Q: When you first engage with a customer, what are the biggest signs that their planning environment has become too complex or disconnected?

The signs are usually very consistent.

One of the first things we notice is that people start describing workarounds instead of workflows. They explain how they export data into spreadsheets, manually adjust plans, or rely on disconnected processes outside the system itself. That is usually a sign the planning environment is no longer trusted.

Another major indicator is dependence on institutional knowledge. There is often one person who “knows how everything works,” and if that person leaves, the knowledge leaves with them. That is a dangerous place for any organization to operate from because systems should support repeatable, scalable decision-making.

The third thing we see is slow decision-making. A sales team asks whether an order can be fulfilled, and the organization needs days to respond because the planning process is fragmented. In modern supply chains, those answers should happen in minutes or seconds, not days.

Most organizations do not just have one of these problems. Usually, they have several at the same time.

Q: You’ve run large-scale supply chain operations before building AI tools for them. What do most AI solutions miss because their architects never had to live inside the systems they are designing for?

The biggest difference is understanding the reality of exceptions.

A lot of AI solutions are built around ideal conditions: clean data, predictable inputs, and controlled workflows. But supply chains do not operate in ideal conditions. They operate through constant disruption and exceptions.

The teams building the best AI solutions are usually the ones who have lived those realities firsthand. They understand what happens when a shipment is delayed at 11 p.m., when a supplier misses a commitment, or when a planner has to make a decision with incomplete information.

The goal is not to build something that demos well. The goal is to solve a real operational problem under real-world pressure.

That is why having functional experience matters so much in AI development. You need people designing solutions who understand both the operational pain and how planning decisions actually happen inside organizations.

Q: ketteQ talks a lot about layering Intelligent Planning Agents into existing ecosystems. What does that actually look like in practice?

What makes this approach powerful is that it does not require organizations to rip apart their existing environments.

Most companies have already invested heavily in APS systems. Replacing everything is expensive, disruptive, and often unrealistic. Instead, Intelligent Planning Agents layer on top of those environments and improve the quality and speed of decision-making without forcing users into entirely new workflows.

The agent receives the data, processes it at much higher speed and intelligence, and sends the improved output back into the existing system. The user still works within familiar screens and workflows, but the quality of the planning output becomes dramatically better.

That is where the “aha” moment happens for customers. They realize they can modernize without creating massive operational disruption or retraining entire teams.

Q: Many organizations are still trying to understand what “agentic AI” actually means. How do you explain it in a practical, operational way?

The simplest way I explain it is this: traditional software waits for instructions. Agents work toward goals.

A software tool performs a task when a user tells it exactly what to do. An agent operates much more like an intelligent analyst. You give it an objective, and it determines the sequence of steps needed to achieve that outcome.

The agent can analyze data, identify issues, make recommendations, and even execute smaller decisions autonomously within defined guardrails. The human planner still provides judgment, business context, and strategic direction, but the agent handles much of the operational workload in between.

That is the real shift. The relationship changes from humans manually executing every step to humans directing intelligent systems that can operate more autonomously.

Q: How do Intelligent Planning Agents change the relationship between planners and planning systems?

I think planners become elevated into more strategic roles.

Today, many planners spend enormous amounts of time doing repetitive analysis, adjusting parameters, validating outputs, and managing exceptions manually. Intelligent Planning Agents can absorb much of that operational workload.

That allows planners to focus more on business collaboration, scenario evaluation, and decision-making. Instead of spending hours preparing data, they can spend more time discussing tradeoffs with sales, procurement, finance, and operations teams.

The human role does not disappear. In many ways, it becomes more important. Human planners still provide context that AI cannot fully understand, including customer relationships, supplier dynamics, organizational behavior, and broader business priorities.

The agent handles the volume of work. Humans focus on judgment and value creation.

Q: What separates a supply chain transformation initiative that gains momentum from one that struggles to get adoption internally?

Two things consistently matter most: quick wins and strong change management.

A lot of projects fail because organizations focus only on solving the hardest problems first. That usually takes too long, and teams lose confidence before they ever see value. The organizations that succeed tend to identify smaller opportunities early, create measurable wins quickly, and use those successes to build momentum internally.

The second factor is change management. Even the best technology will fail if users are not trained properly or do not trust the system. People naturally fall back to familiar workflows unless organizations actively invest in helping teams adopt new ways of working.

Technology alone does not create transformation. People do.

Q: You are both advising customers and helping build the next generation of AI planning tools. What does the future of human-agent planning teams look like, and how close are we?

Closer than most people think , and more human-centered than most fear.

The pace of AI development right now is extraordinary. Even within the last several months, the progress has been dramatic. We are already reaching the point where AI agents can take on meaningful portions of operational planning work.

But I also think the future will be far more collaborative than many people fear.

AI is incredibly powerful at processing information, identifying patterns, and executing operational tasks at scale. What it still lacks is human context. People understand organizational dynamics, relationships, market realities, and the nuances that influence real-world decisions.

The future is not humans versus AI. It is humans working alongside AI agents as collaborative planning teams.

The organizations that succeed will be the ones that learn how to combine human judgment with intelligent automation most effectively.

Q: What excites you most about ketteQ?

What stands out most to me is the speed, openness, and entrepreneurial culture.

At larger organizations, innovation can move slowly because there are layers of approvals, competing priorities, and organizational politics. At ketteQ, ideas move quickly. If someone identifies a problem worth solving, there is real freedom to experiment, prototype, and improve rapidly.

I also appreciate how collaborative the environment is. People are encouraged to challenge ideas, contribute perspectives, and improve solutions together. That creates an environment where innovation happens much faster because everyone feels connected to the outcome.

For people who enjoy building, experimenting, and solving problems, it is an incredibly energizing place to work.

Abschließende Überlegungen

Talking with Bhuva highlights an important reality about the future of supply chain planning: the technology matters, but understanding the operational reality behind the technology matters even more.

As AI continues reshaping supply chain planning, organizations are discovering that the goal is not replacing human expertise. It is augmenting it. The most effective planning environments will combine intelligent agents capable of handling speed and scale with experienced planners who provide judgment, context, and strategic direction.

Bhuva’s perspective also reinforces something we hear repeatedly across the industry: modernization does not need to begin with disruption. For many organizations, the future will come from intelligently evolving the systems they already have while layering in new capabilities that help teams move faster, plan smarter, and operate with greater confidence.

We are grateful to have Bhuva as part of the ketteCrew and excited to continue sharing the stories of the people helping shape the future of adaptive supply chain planning.

Curious about the principles that shape how we build, deploy, and support adaptive planning? Visit our About Page to learn more about ketteQ’s mission, core values, and the team behind the technology.