As CFO of data science company Tredence, Pratap Daruka exemplifies how financial leaders can harness AI's transformative potential while maintaining strategic oversight.
In this wide-ranging interview, he discusses the success of Tredence's FALCON platform in achieving 98 per cent forecast accuracy, outlines a pragmatic approach to GenAI investments, and emphasises how ethical AI practices create lasting value through enhanced trust and reduced risks.
Daruka's insights reveal how CFOs can navigate the intersection of financial stewardship and technological innovation in an AI-driven business landscape.
Tredence is dedicated to improving decision-making and operational efficiency for its clients through data science and AI. How do you apply these same capabilities internally to enhance your own decision-making and operational processes at Tredence? Can you share specific examples of measurable improvements?
One of the major challenges that CFOs face today is the disconnect between operational data and financial metrics, which hinders effective decision-making. Therefore, to tackle this problem, we have developed the Org 360AI platform, FALCON, which is designed to unify financial and operational data. This integration enhances visibility and enables real-time, data-driven decisions across the organisation. We have seen the impact on productivity and profitability. For example, our AI-driven demand forecasting tool has achieved a 98% accuracy rate by eliminating revenue leakage and optimising resource allocation proactively. By consolidating financial and operational data on a single platform, FALCON has enabled us to act with agility, improve forecasting, and drive better outcomes. By using AI, we have been able to strengthen our financial health and operational resilience within Tredence.
As the CFO of a leading data science and AI company, what do you see as the biggest shifts happening in the AI and analytics industry over the next five years? Which technological advancements or market trends do you believe will have the most significant impact on businesses, and how is Tredence positioning itself to capitalise on these trends?
In the next five years, Generative AI is going to proliferate and transform industries by automating complex tasks, creating personalised customer experiences, and bringing powerful applications to generate content, product design, customer service, and supply chain optimization.
At Tredence, we are investing heavily in GenAI to help our clients leverage its transformative capabilities. Our tailored GenAI solutions address industry-specific challenges in retail, consumer packaged goods, healthcare, manufacturing, BFSI, etc., focusing on building adaptable models that integrate seamlessly into workflows to enhance decision-making and streamline operations.
We prioritise responsible and explainable AI, ensuring that our GenAI implementations align with business goals and compliance requirements. Tredence empowers organisations to unleash AI's full potential, fostering innovation, efficiency, and improved customer experiences at the intersection of AI and business transformation.
Generative AI has garnered a lot of attention recently. From a CFO’s perspective, how should businesses approach investments in generative AI technologies? What key factors should be considered when assessing the long-term financial impact and potential ROI of adopting GenAI?
As a CFO, I believe the key to maximising the return on investment (ROI) on Generative AI (GenAI) investments lies in a use-case-driven approach. Businesses should prioritise GenAI applications where time to market is faster and value realisation is more immediate, such as automated content creation or customer engagement. By focusing on these high-impact areas as a priority, companies can establish a self-sustaining engine that generates returns, which can be reinvested to fund future GenAI investments with longer ROI cycles.
At Tredence, we have implemented this strategy to accelerate the adoption of GenAI, establishing a scalable foundation that supports both short-term wins and long-term innovation.
Ethical AI is a growing concern across industries. How should companies evaluate the ethical implications of their AI investments? From a financial standpoint, how do you balance the costs of implementing ethical AI practices with the potential for long-term value creation and mitigating reputational risks?
Ethical AI is important for building trust and creating lasting value for any company. Hence, companies should start looking at it through a lens of transparency, fairness, and privacy. This involves assessing the AI applications based on their impact on society, ethics, and the industry at large, alongside financial metrics.
From a financial standpoint, investing in ethical AI can increase the cost, but in the long term, it can reduce reputational and compliance risks by building a brand reputation and winning client trust. Hence, the cost of investment is outweighed by building a brand reputation, securing customer loyalty, and growing the brand sustainably by aligning business practices with our core values and client expectations.
