For much of the past years, artificial intelligence has dominated boardroom conversations across Europe. Yet as the initial excitement gives way to a more measured reality, many organisations are discovering that deploying AI and delivering business value are two very different things. While some companies continue to experiment with isolated use cases, others are beginning to unlock meaningful returns by focusing on the foundations that make AI work at scale.
Shiraz Mishra is the Business Head for International Markets at Polestar Analytics. Having spent more than two decades helping global enterprises navigate technology transformation, he believes the future of AI will not be defined by the sophistication of algorithms alone, but by the quality of the data, governance, and leadership that sit behind them.
In this interview with TechBullion, Mishra shares his perspective on why Europe may be entering its most important phase of AI adoption yet, what separates organisations that achieve measurable outcomes from those stuck in perpetual pilot programmes, and why data convergence is emerging as one of the most critical competitive advantages in the modern enterprise. His insights offer a timely reminder that successful AI transformation is ultimately less about technology and more about building organisations capable of turning intelligence into action.
Please tell us more about yourself, what unique services and solutions you provide at Polestar Analytics?
I’m Shiraz Mishra, Business Head for International Markets at Polestar Analytics. I’m based in Germany and have spent the better part of two decades working with global enterprises, including stints at ABB and Wipro, at the intersection of technology, operations, and business transformation. I have been with Polestar Analytics for a little over a year. What drew me to Polestar Analytics was a simple belief that AI without a trustworthy data foundation is just noise. That is a problem that I have seen, ever so often.
Polestar Analytics helps enterprises move from disconnected AI experiments to real, measurable business impact. We do that through 1Platform, our own converged data platform that brings together data management, governed AI, and intelligent automation in one place, all this built on top of the platforms our clients already use, like Databricks, Microsoft, Snowflake, and Anaplan.
In Europe, we are focused on CPG and retail, pharmaceuticals, and manufacturing. We raised growth capital in 2025, and Europe is a central pillar of how that investment is being deployed. The common thread across everything we do is always the same, which is turning data into outcomes for our customers.
With so many AI platforms and solutions competing for attention, what makes Polestar Analytics AI solutions stand out?
This is a fair question, especially when every platform or solution provider claims to be unique and differentiated.
For me, there are a few things that are hard to replicate. The first is that we do not separate data, AI, and enterprise planning into different conversations. We bring all three together through 1Platform in a single governed environment and that convergence is what allows our clients to move from data to decision without the integration overhead which slows most programs down.
How we engage with our clients is also something that we have honed over the last several years. We lead with the business problem, we agree upfront on how success will be measured, and we ensure that the outcome is real. Sounds straightforward but a fundamentally different model from how most of the engagements take place in the market.
And underlying all of this is the 14+ years of domain knowledge built specifically in the industries where we operate, CPG, pharma, and manufacturing. That depth of experience is what makes the conversation at the leadership level different, and ultimately, what makes the outcomes more predictable.
Europe is often seen as cautious in adopting AI. What has changed over the past 18 months that makes this a defining moment for enterprise AI adoption across the region?
Yes, Europe could be considered cautious in its approach to AI, and we see that this is often being misread as being slow. However, the other way to look at it is that Europe is being deliberate in its approach and in some way this ‘deliberateness’ is starting to pay off.
Over the past couple of years, a few things have converged. The regulatory framework is crystallizing. We have more clarity with the EU AI Act’s governance rules for general-purpose AI models coming into effect. Enterprises that actively invested in data governance are already winning, while the lets-wait-and-see crowd races to catch up.
The business case has taken precedence over quick-wins and pilots. The early AI pilots produced enough learnings and failures that boards can now have honest conversations about what works. The question has shifted from “should we do AI” to “why aren’t we seeing returns yet.” And I think that’s a much more productive starting point.
Finally, competitive pressure is intensifying. European enterprises watching US and Asian counterparts operationalize AI at scale can no longer treat it as a good-to-have. The inflection point is already here. And from what I’m seeing on the ground across Europe, organizations are finally treating this with the appropriate level of urgency.
Many AI initiatives never progress beyond the pilot stage. What separates organizations that achieve measurable business outcomes from those that remain stuck in experimentation?
This is a very common topic of discussion across organizations, irrespective of geography or industry. I believe that there are multiple issues holding organizations back and preventing them from progressing beyond AI pilots. Culture and resistance to change are the primary reasons. Then, there is a question of executive support and short-term versus long-term objectives. We need to move away from quick wins and focus on transformational changes which can enable organizations to make the most of what AI has to offer.
But if I have to point to one consistent pattern, the organizations stuck in pilot purgatory almost always have invested in AI tooling but not in the data infrastructure underneath it. They’re building on a foundation that was never designed for what they’re now asking it to do.
The companies that break through treat data engineering as a strategic capability, not a back-office cost. They define success in business terms before they choose technology, like forecast accuracy, margin improvement, working capital release, and work backwards from there.
What we’ve built 1Platform to do is to accelerate that journey. By converging data management, agentic AI, and decision intelligence into one environment, we remove the integration debt that kills most AI programs in their second phase.
How can European enterprises accelerate AI adoption while maintaining the governance, compliance, and trust standards expected by regulators, customers, and stakeholders?
I consistently deliver the same message to CIOs, and that is not to build your AI strategy around the AI tools, rather build it around the data governance layer and let the tools sit on top. Regulation is not going to get lighter from here, and the organizations that have embraced that reality are turning it into a competitive moat. And it is important to acknowledge that getting governance right does not slow you down but essentially removes the friction that slows most AI programs post pilots. The organizations that move the fastest and who see the most impact from their AI programs are the ones who invested early in getting their data in order.
With AI tools becoming widely accessible, why are many organisations still struggling to generate meaningful ROI, and what are they getting wrong?
There is no doubt that there are a number of AI tools available and easily accessible to organizations. The tools are easy to use, but this is where the challenge lies. An organization can complete numerous proof-of-concepts, owned by various teams, each using different data sources, each providing minor benefits. But at the end of the day, these are not business outcomes which convince the board to invest in an AI strategy.
The ROI gap comes from a few structural mistakes. The most common is that organizations are still treating AI as a technology deployment rather than a business transformation. As I mentioned earlier, allowing fragmented data estates to persist takes away any benefits of AI, regardless of model sophistication. Then of course, missing the human layer, like the workflows, change management, and organizational openness and willingness to actually act on AI-generated decisions rather than override them out of habit.
Ultimately, the enterprises getting this right in 2026 are the ones who have stopped asking ‘what AI can do’ and instead are asking ‘what problem are we trying to solve and how will we know when we have solved it?’
Data fragmentation remains a major challenge for enterprises. How important is data convergence in turning AI from a promising technology into a business-critical capability?
Data convergence is the precondition for everything we do. This may sound like a very strong statement but in reality it is quite simple.
The challenge that I repeatedly see is that organizations have data sitting in silos which were never designed to talk to each other. In a CPG, commercial data resides in one system, supply chain in another, financial planning in a third. An AI workflow trying to make decisions across that landscape does not work with the full picture, but instead it is working with whichever slice of reality is available at the moment. This is not a problem solvable by an AI model, however sophisticated it may be.
This is the reason why data convergence is a precondition and at the heart of whatever we do at Polestar Analytics. 1Platform brings these disparate data estates together into a single, governed environment, so that AI works with a coherent and trustworthy data foundation when it is creating insights and recommending actions. This is one of the major drivers to move AI from a promising demo to something that runs your business.
You have spoken about building growth through depth rather than scale. What does that strategy look like in practice, and why is it particularly relevant in today’s AI market?
When everyone in the market is talking about scale and speed, we made a deliberate choice to go deep instead. When we engage with an organization, we lead with the business problem, then agree on how we will measure success and we stay until that outcome is real and repeatable.
For Europe, depth means building genuine credibility in an industry. We are intentional about where we play – CPG and retail, pharma, manufacturing. This is the focus which makes us the partner that a FMCG CTO or a pharma data leader calls when they need someone who has solved their problem at enterprise scale before.
The growth capital we raised is being deployed to deepen exactly this, our vertical IP, our AI capabilities and our product, 1Platform. In today’s AI market, where everyone claims transformation, what clients ultimately want is a partner who has done it before in their industry and can show them measurable results. This is what we are building towards in Europe.
As AI becomes embedded across business functions, what will distinguish truly intelligent enterprises from those that are simply deploying AI technologies?
This is probably the most important question facing enterprise leaders today and I believe that the topic deserves more attention.
The organizations that will define the next decade are not necessarily the ones deploying the most AI tools or successfully completing the most AI pilots. They are the ones where data is treated as a strategic asset, actively governed at the leadership level, where AI systems are not just creating point insights but are trusted enough to initiate actions within defined boundaries. And where people are genuinely equipped to work alongside AI, not around it or in spite of it.
The shift from AI as a reporting tool to AI as an active participant in how a business operates, is what we at Polestar Analytics call Agenthood AI. It is a fundamental change in how decisions are made and how organizations are structured around those decisions.
The enterprises getting ahead are not the ones waiting for the technology to mature further, they are building the data foundation, governance frameworks and the organizational readiness right now. This is what separates the leaders from the ones who will follow.
Is enterprise AI transformation primarily a technology challenge or a leadership challenge, and what mindset shifts are needed at board and executive level?
I would say Leadership, without any hesitation. I have yet to meet an organization where technology was the limiting factor. The technology exists, the platforms are mature, but the gap is almost always in the room above the data team.
The most important mindset shift that I see needing to happen at the board level is that they need to stop treating AI as a project and rather treat it as an operating model. Move away from budgets, timelines and end states and move to a constant evolution.
The other shift is around risk. In Europe, we have been cautious on AI, and not without reason as I mentioned earlier. However, the competitive consequence of inaction is now more visible than the risk of action. The leaders on the right track are the ones who have picked one or two high stakes use cases, resourced them appropriately and held themselves accountable to a business outcome, not a delivery milestone.
Europe is often compared with the US and China in the AI race. What unique advantages does Europe have, and how can businesses turn them into a competitive edge?
I believe Europe’s advantages are more structural than people give credit for. Regulatory rigor is producing a trusted AI market. As AI embeds itself into healthcare, financial services, and critical infrastructure, the enterprises that can demonstrate compliance, transparency, and data sovereignty will command a significant premium, not just in Europe but globally.
Europe also has deep domain expertise in industries where AI creates the most value, precision manufacturing, pharma, financial services, logistics, to name a few. These are complex, data intensive environments where depth and trust matter more than speed. This is where Europe is strongest.
The opportunity for European enterprises is to stop treating regulation as a constraint and start looking at it like a market signal. The world is moving towards trustworthy AI and Europe is already there. The businesses that recognize this, and build on this foundation now, will find themselves leading on the global stage.
About the spokesperson
Shiraz Mishra is Business Head for International Markets at Polestar Analytics, based in Germany. With over two decades of experience across technology, manufacturing, and industrial automation, he brings a strategic outlook combined with an on-the-ground understanding of how organizations can drive impactful change with enterprise AI.
Over the past year, Shiraz has been working closely with enterprise leadership teams across Europe, giving him a clear view of where organizations are in their AI and data journeys and what separates the ones making real progress from those still finding their footing.
Prior to joining Polestar Analytics, Shiraz held leadership roles at ABB and Wipro, where he led cross-functional teams focused on new market growth, technology innovation, and ecosystem strategy. Earlier in his career, he managed global partnerships with companies including SAP, Microsoft, IBM, Ericsson, and Dassault Systèmes.
Shiraz is known for turning complexity into clarity, building the relationships, teams, and strategies that translate vision into commercial results. He is a recognized voice on enterprise AI adoption and what it takes to move from pilot to scale. Connect with Shiraz on LinkedIn linkedin.com/in/shirazmishra
About the company
Founded with a mission to simplify complex decision-making through intelligent, scalable solutions, Polestar Analytics develops cutting-edge AI, analytics & planning solutions for enterprises. The company’s flagship 1Platform enables organizations to converge diverse data sources and simplify data to outcome journeys for businesses. To learn more, follow Polestar Analytics on LinkedIn and visit the website – www.polestaranalytics.com
