How Polestar Analytics is Navigating AI’s Next Frontier – From Hype to ROI

Following is an excerpt from TechCircle’s conversation with Mr. Chetan Alsisaria, CEO & Founder, Polestar Analytics.

Q1: Post-fundraise, you've positioned Polestar Analytics beyond traditional services. How do you define your role when enterprises engage you - are you the transformation architect that redesigns their AI strategy, or the execution partner that converts proof-of-concepts into production-grade systems?

Here's what 87% of companies get wrong about AI: they build brilliant strategies and impressive pilots, but can't bridge the gap to real ROI. We bridge that gap by being both the transformation architect and the execution partner—because one without the other leaves money on the table.

Our approach with 1Platform – our convergence data platform - breaks down the silos that kill AI initiatives: disconnected teams, fragmented data, and proof-of-concepts that never see production. We create unified architectures where agentic systems can actually operate, data flows seamlessly, and AI moves from boardroom vision to operational reality.

Consider how we transformed demand forecasting for a leading retailer—moving them from reactive guesswork to intelligent prediction systems. They achieved 40% reduction in development costs, halved their data creation timelines, and optimized trade promotion spend by double digits. These aren't isolated wins; we're driving similar transformations across CPG, Pharma manufacturing, IT/ITeS, and financial services.

What sets us apart is that we don't stop at strategy or settle for pilots. We operationalize AI at enterprise scale, ensuring that innovation translates into measurable business impact. When enterprises engage us, they're not just getting consultation—they're getting a partner who makes AI work in the real world, where it matters most.

Let’s take as an example, a leading automotive manufacturer: demand forecasting was reactive and siloed. By deploying Agenthood AI on 1Platform, varying impact was generated such as 40% Reduction in Development Costs of Data Platform; 70% Reduction in Data estate creation time; 50+ Specialized agents ready to be deployed and 8-12% optimization in trade promotion spend. That’s the difference; we don’t just advise, we operationalize AI. 

Q2: The industry is littered with AI pilots that never scale. Your 1Platform promises enterprise-grade deployment. Walk us through what makes an AI solution truly production-ready, and how you've solved the "pilot purgatory" problem that plagues most organizations?

The industry is littered with AI pilots that never scale - more than 80% stall out. Why? Because most are built in silos, lack governance, and fail to integrate with enterprise systems. This leads to what we call “pilot purgatory” - a cycle of experiments with no clear ROI, no processes, and no path to production. From our experience, there are two big reasons pilots fail:

1. Missing enterprise-grade processes, governance, and security. Scaling requires more than just a working prototype - it demands consistent DataOps, compliance, and LLMOps/AIOps methodologies from day one. Without clear guardrails and lifecycle processes, what works in a pilot rarely survives the jump to production.
2. Relying on legacy data estates. Many organizations expect their historical data and processes to serve future AI use cases. This “lift and shift” approach fails because outdated systems and fragmented datasets cannot support enterprise-scale AI. Simplified frameworks, harmonized tools, and a unified approach to the data estate are essential for scaling.

1Platform is designed to break that trap. From day zero, it’s engineered for scale; embedding lifecycle management, governance, deployment, monitoring, and security into the foundation. We don’t just run pilots for the sake of technology; we align them to business objectives with clear guardrails in place. Risk, regulation, and operational workflows are built into the pilot itself - so scaling isn’t an afterthought, it’s the natural next step.

By converging fragmented datasets into a single source of truth, 1Platform powers predictive insights, intelligent automation, and faster decision-making.

Q3: Traditional BI has been around for decades, and every tech giant now offers AI tools. What fundamental market gap does 1Platform address that existing solutions miss, and why should a CIO choose you over established players or internal development?

Traditional BI tools and today’s AI offerings often add complexity instead of reducing it. Entreprises end up juggling multiple platforms, hyper-scalers, and data tools which slows down time to value.

1Platform addresses this gap by converging tools and simplifying the data architecture. For example, PySpark and Iceberg can run seamlessly on the same platform; no re-engineering or platform trade-offs required. We don’t compete with existing solutions; we complement them, enabling organizations to leverage their current investments while streamlining operations.

Think of it like Databricks Lakehouse pipelines, but with greater simplicity, speed, and ease of use. Here’s what we bring to the table.

• Faster time to value: AI assistants translate requirements in natural language into pipelines in minutes, removing multi-step processes and enabling insights faster.
• Operational efficiency: Up to 50% reduction in pipeline creation time without changing cloud providers or legacy infrastructure.
• Proven scale: We’ve served 350+ clients, including Fortune 500 companies, co-building solutions that are tailored, tested, and ready to scale.

Enterprises move beyond experimentation and turn AI into production-ready, business-driving solutions; faster, simpler, and more reliably than internal builds or traditional vendors.

Q4: In a landscape dominated by hyperscalers like AWS, Microsoft and Google, plus hundreds of AI startups, how do you build defensible competitive advantages? What's your strategy for the next five years as competition intensifies?

In a landscape dominated by hyperscalers, alongside hundreds of AI startups,1Platform occupies the middle ground. We complement hyper-scalers and operate where the rubber meets the road, delivering vertical convergence and execution speed that few can replicate.

Here’s what our strategy looks like for the next five years:

• Vertical Depth: Build specialized AI practices in CPG, manufacturing, pharma, and retail, creating domain expertise beyond generic solutions.
• Agentic Ecosystems: Deliver enterprise-grade AI agents that not only analyze data but act on it, embedding automation and decision-making directly into workflows.
• Global Scale with Indian DNA: Combine affordable, agile engineering with consulting-grade expertise, enabling faster GTM, higher probability of success, and long-term client relationships.
• Complementing Hyperscalers: Seamlessly integrate with platforms like Microsoft and Databricks while helping clients achieve cloud independence and functional AI solutions that accelerate outcomes.
• Innovation & Co-Creation: Dedicated innovation teams focus on customer-driven features, process reimagination, and research. We co-build with partners and clients, ensuring solutions are tailored, tested, and production-ready.

Convergence is Key

The true winners in enterprise AI will bring fragmented systems, data, and tools together. 1Platform and Nexus achieve this by:

• Delivering vertical expertise and applying hyperscaler architectures to the right use cases.
• Bridging data gaps to scale pilots reliably.
• Enabling agentic ecosystems that act on insights, not just present them.
• Co-creating solutions with clients and partners to ensure rapid deployment and long-term value.

Our combination of vertical expertise, execution speed, agentic AI, and collaborative co-creation is difficult to replicate. We plan and iterate quarterly, not yearly, keeping us ahead of competition and deeply aligned with client needs.

“We bridge the gap between AI strategy and real ROI — because one without the other leaves money on the table.”

Q5: AI agents, SLMs, LLMs - there's no shortage of emerging tech. Which specific technologies are you betting on for your roadmap, and how do you separate genuine innovation from market noise?

Our philosophy of innovation is simple: it must add significant value to clients. Innovation for novelty alone is meaningless; the goal is measurable business impact. We categorize our innovation in two ways:

1. Productivity & Efficiency Gains: Automating repetitive tasks and reducing friction.
2. Transformation & Reimagination: Collaborating with clients and partners to fundamentally improve processes. This requires patience, persistence, and continuous recalibration, guided by metrics that matter; revenue impact, operational efficiency, and strategic outcomes.

Enterprises are exhausted by dozens of point tools that don’t communicate. That’s why we’ve built an integrated stack of intelligent components that work seamlessly together:

• Data Nexus: Consolidates fragmented data into a clean, AI-ready layer.
• Agenthood AI: Enterprise-grade autonomous agents that move from insight to action.
• P.AI (Polestar + AI): Embeds intelligence into every workflow.
• Profit Pulse – our RGM Suite: Precision and AI augmented revenue growth engine for CPG and retail.
• Supply Chain Control Tower: Real-time resilience and visibility.

Together, these form the building blocks of a converged, AI-first enterprise, helping organizations scale outcomes. Our roadmap focuses on three clear bets:

• Agentic AI: Autonomous agents that take workflows from insight to execution.
• Decision Intelligence: Prescriptive, automated decision-making beyond dashboards.
• AI-Native Governance: Ensuring trust, auditability, and lifecycle management.

In terms of strategic partnerships, we offer:

• Databricks & Microsoft: Leveraging Unity Catalog, Agent Bricks, Azure, Fabric, Power BI, and Co-Pilot for scalable, enterprise-grade AI.
• Anaplan & Pigment: Strengthening enterprise performance management and planning capabilities.

GenAI is part of our roadmap, but without governance and context, it’s just noise. A recent MIT study shows that 95% of generative AI pilots fail to deliver financial impact due to poor integration with workflows. Our goal is to make AI democratized, usable, scalable, and trustworthy - from boardroom strategy to shop-floor execution.

Q6: With fresh funding in hand, you face classic growth decisions. What's your priority sequence and the strategic rationale behind it?

With fresh funding in hand, 1Platform is focused on transforming from a services-led company to a transformational platform company. Our goal is to deepen AI offerings, enable advanced use cases, and redefine how enterprises operate in the AI era. Recent growth includes global expansion, strong client adoption, and the addition of senior leaders, including Michel Combes (ex-CEO of Sprint and Vodafone) to our board.

The recent USD 12.5 million growth capital raise will be deployed to reinforce the development of our proprietary 1Platform, recognized as one of the industry’s leading solutions for data convergence and enterprise-scale analytics. While growth is the north star, our focus is on creating compounding platform value, not just linear service growth.

Our priorities, in sequence are:

1. IP Development: Doubling down on 1Platform and vertical accelerators to deepen our product moat.
2. Enterprise Deepening: Expanding our footprint within the Fortune 1000 clients we already serve.
3. Global Expansion: Targeted growth in North America and Europe, leveraging our proven model and AI capabilities.

Our strategic rationale is this: Capital is first invested in platform and IP development, which generates compounding value and differentiates us from services-only models. Once the product moat is reinforced, we scale; expanding client adoption and entering new geographies efficiently.

Q7: AI hype is everywhere, but CFOs demand hard numbers. How do you structure engagements to deliver measurable ROI, and what mechanisms ensure clients stay engaged beyond the initial excitement phase?

This varies from case to case. AI initiatives deliver real value when structured around measurable outcomes, not just short-term ROI. Take Netflix as an example: when they started, they went digital, their focus was on efficiency and long-term transformation rather than immediate ROI. Here, a risk-reward mechanism is critical. It’s important to carefully plan before starting, as you are changing existing models and want to do things differently, such as by creating intermediate success milestones, example - small pilots or improvements in user experience. This helps manage risk and prepare for larger-scale change.

Q8: Five years from now, how do you measure Polestar Analytics’ success? Is it market valuation, platform adoption metrics, or something more fundamental about how enterprises approach AI decision-making?

Five years from now, our success will be measured by how effectively enterprises adopt 1Platform to transform their AI decision-making. Success is achieved when implementation is seamless, processes become more agile, and organizational silos are reduced. Today, many enterprises face fragmented systems or overly rigid processes. 1Platform helps overcome these challenges. When the platform enables smoother, more integrated operations and drives measurable improvements, our job is done. That’s precisely why we’ve evolved into a platform-driven organization; this approach compounds value: faster problem-solving, lower costs, continuous innovation, and smarter, leaner and more seamless organizations.

“Innovation for novelty alone is meaningless; the goal is measurable business impact.”

Q9: As AI reshapes entire industries, what excites you most about leading in this space? How do you balance the pressure to move fast with the responsibility to build sustainable, ethical AI solutions?

What excites me most about leading in the AI space is shaping how enterprises operate for the next decade. AI is becoming the new business operating system, and the opportunity to guide organizations through this transformation is both inspiring and consequential.

As a leader, you always juggle multiple dimensions of each priority and one must do it with speed with responsibility by focusing on long-term value for all stakeholders; which is that we should be able to de-risk our clients and create sustainable solutions for them. Innovation must be rapid, but it cannot compromise ethics, compliance, or sustainability. We achieve this balance by:

• Experimenting safely: Pilots and internal experiments are conducted in controlled environments, with awareness of geo-level compliance and norms.
• Embedding ethical AI by design: Every solution incorporates governance, ethical standards, and sustainability from the outset.
• Cross-disciplinary collaboration: Teams work across functions to ensure fast iteration, compliance, and accountability.
• Feedback-driven agility: Quick feedback loops allow us to pivot efficiently while maintaining trust and alignment with client value.

At the core, our philosophy is simple: if the organization and every individual are committed to delivering sustainable value, speed and ethical responsibility naturally coexist and that’s the sweet spot we have. This approach ensures AI drives meaningful, long-term impact for clients and society alike.

Q10: Everyone's chasing the same AI opportunities. What contrarian bet is Polestar Analytics making - what market blind spot are you addressing that others are overlooking, and why will you be right when the dust settles?

While much of the market is chasing horizontal GenAI use cases, Polestar Analytics is making a contrarian bet on vertical, domain-rich AI convergence. Enterprises don’t need another generic chatbot — they need AI that embeds intelligence directly into workflows with measurable ROI:

• CPG Revenue Management Agents: Not just highlighting which promotions work but linking to S&OP to visualize constraints and next steps.
• Pharma Trial Optimization AI: Driving actionable insights while streamlining complex trial processes.
• Manufacturing Control Towers: Agents that identify delays and suggest alternative operational choices.

Our differentiator is that we embed organizational knowledge capital into our solutions, layering years of domain expertise directly on top of our platform. This ensures scalable data practices and AI that does more than show insights; it guides decisions and actions.

We believe convergence is the key: integrating data, workflows, and domain expertise into a single, actionable AI layer. This approach positions us to deliver sustainable value while the market chases generic, short-lived solutions. 

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