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Businesses are facing the challenge of moving beyond AI pilot projects to achieve measurable results at scale. Despite the rise of generative AI, many struggle to translate experiments into real business value.
AI has transformed legacy modernization into a strategic initiative to enhance business intelligence. Organizations must adapt their work processes, incorporate AI in decision-making, and develop flexible systems to respond to technological changes.
As AI adoption increases, the focus is shifting from efficiency to value-driven outcomes, such as improving customer experiences and making faster, more informed business decisions. Additionally, establishing an effective governance framework is essential.
In the latest episode of Analytics Insight Podcast, host Priya Diyalani speaks with Rajshekar Datta Roy, CEO at Sonata Software, to explore how enterprises can scale AI-driven transformation, modernize legacy systems, and drive meaningful business impact. Here are the excerpts of the interview:
The greatest challenge does not come from a technological perspective but rather an issue with legacy. Companies can conduct successful pilot implementations in their own silos but fail to scale amid fragmented and inflexible processes. The systems were simply never designed to operate in real-time or make autonomous decisions. Cultural challenges and a lack of alignment between different business and technical groups only exacerbate the situation. The key to success is viewing AI as a business transformation project.
Legacy modernization isn't merely the upgrading of outmoded systems anymore. Modernization now involves unleashing the intelligence of these legacy systems. The advent of AI transforms modernization into a strategic capability that helps with agile responses, effective decision-making, and enhanced competitiveness. As opposed to being a discrete event, modernization becomes a perpetual process. AI serves as a layer that integrates legacy systems with modern systems. This implies that the objective of modernization shifts from mere upgrading to enabling enterprise intelligence.
The scope of experiments is restricted to discrete use cases, whereas transformation is ingrained in core processes. It all comes down to how aligned the AI initiatives are with organizational goals, such as scaling the company, improving the customer experience, or achieving agility. The key features of real transformation include the redesign of processes, the incorporation of AI into decision-making, and easy access to information. It must also involve an adaptable architecture that adapts to technological changes.
ROI should not be measured only through cost reduction. Value creation comes in the form of enhanced customer experience, quicker decision-making, and even increased sources of income. AI creates value through effectiveness and not just efficiency. Although costs such as computing power should be managed, they can be optimized in due course. What matters most here is business results and value creation for stakeholders.
Governance should be built into the foundation, not added later. Proper guardrails must be in place from the beginning for matters like data privacy, security, and computing. Responsible-first thinking will help ensure that innovation occurs in an environment where the company takes no unnecessary risks. Governance needs to adapt alongside technologies, ensuring that both innovation and control occur.
Listen to the full discussion on the Analytics Insight Podcast.
