Anand Srinivasan Featured in SandHill to Discuss AI and Analytics in Building Competitive Advantage

Anand Srinivasan is a Managing Partner at Kaizen Analytix, a leading provider of AI, data analytics, and technology services and solutions.

Headquartered in Atlanta, Kaizen is recognized for its speed, flexibility, and ability to rapidly deliver actionable insights that drive sustainable business benefits across the value chain. The company has been spotlighted by Gartner, NPR, Forbes, and Entrepreneur, and named to the Inc. 5000 list as one of America’s fastest-growing private companies.

In this interview, Anand discusses how Kaizen combines business acumen, deep subject matter expertise, and technical know-how to help organizations unlock measurable results, and he shares his perspective on the evolving role of AI and analytics in building competitive advantage.

M.R. Rangaswami: Why is Agentic AI so important for companies right now and what are solutions you’re seeing that are different from traditional generative AI implementations?

Anand: Research from Gartner has named Agentic AI the top tech trend for 2025 and Kaizen is seeing this trend firsthand with our clients.  Agentic AI is becoming essential for companies because it addresses many of the shortcomings of traditional AI deployments that simply produce outputs without context or actionability, to now providing recommendations on how to act upon the data, driving decisions and actions in real time – and doing it all autonomously and at scale.

We’ve entered a new era where AI agents don’t just analyze data–they act on it. At Kaizen, we take a value-first approach to AI. We don’t chase hype, but we recognize that hype often precedes real value creation. Our agentic AI solutions intentionally combine predictive analytics, agentic frameworks, and generative AI, leveraging each for its strongest strengths, rather than relying on any one technology in isolation.

Predictive analytics provides the traceability and visibility enterprises depend on, while agentic and generative AI unlock new levels of automation and action. By curating solutions that balance these strengths and weaknesses, we deliver systems that are both explainable and adaptive.

M.R.: As enterprises adopt agentic AI at scale, what are the biggest challenges companies face in integrating these intelligent agents into existing workflows?

Anand: Enterprises typically encounter challenges on three fronts: technology, process, and people. On the technology side, legacy systems often struggle to support the API-driven integrations that agentic AI agents require. From a process perspective, some workflows are well-suited for automation, while others need to be redesigned or adapted before AI can add real value.

On the people side, organizations face a shortage of skilled AI engineers and change-management expertise needed to sustain adoption at scale. It’s no surprise that research from IDC indicates that 88 percent of AI pilots fail to reach production, highlighting the difficulty of scaling AI initiatives beyond initial experiments. 

Equally important when considering Agentic AI solutions is making sure there is a human element. We find that successful AI deployments include a subject-matter expert who serves as a “human in the loop.” This role provides critical oversight to ensure responsible guardrails, contextual understanding, and alignment with business objectives. The result is agentic AI that is pragmatic, trusted, and designed to drive measurable outcomes–whether in pricing, anomaly detection, or extracting value from unstructured data.

M.R.: How is agentic AI reshaping enterprise decision-making beyond traditional automation?

Anand: Agentic AI is helping enterprises rediscover their “ikigai”—a renewed sense of purpose in how decisions are made and value is created. Post-COVID workforce shifts led to a loss of tribal knowledge about how things actually work inside organizations. Agentic AI fills that gap by capturing institutional wisdom and enabling employees to focus on elevated outcomes and meaningful insights, not just ‘doing the work.’

The result is decision-making that is faster, more adaptive, and more customer-focused. Instead of relying on 5 or 10 times the number of software developers to hard-code complex processes, agentic AI finds easier, more efficient ways to add value to the end customer and scales without legacy constraints. It’s not just automation—it’s a way for companies to scale intelligence and impact across the enterprise.

Agentic AI represents a turning point in how enterprises approach intelligence, automation, and decision-making. Unlike earlier waves of AI that focused mainly on efficiency gains, this new generation of AI agents is reshaping how organizations respond to disruption, scale knowledge, and create customer value. The companies that succeed will be those that balance automation with human judgment, build adaptability into their workflows, and embrace AI not as a standalone tool but as an embedded capability across the enterprise. In many ways, agentic AI is less about replacing work and more about redefining it—elevating the role of people while enabling organizations to compete at an entirely new level.

Over the next five years, agentic AI will move from early adoption to enterprise necessity. Organizations that embrace it will not only streamline operations but also unlock new levels of resilience, adaptability, and customer value. Those that delay risk being left behind as decision-making itself becomes a core source of competitive advantage.

M.R. Rangaswami is the Co-Founder of Sandhill.com