The Will to Succeed

How is data analytics evolving with AI in 2025?

AI is revolutionising data analytics across the value chain from automating tedious tasks regarding data cleaning and labelling to generating automated insights in real time.

AI powered data analytics are taking important roles in multiple industries and are solving use cases like predictive and perspective analytics, fraud detections, cybersecurity, data privacy, data governance etc. thus helping businesses make informed decisions and reducing risks. Its ability to generate powerful insights from millions and billions of data points is going to be a game changer for market players, which can be done manually and real time.

This evolution is particularly evident in mergers and acquisitions (M&A), where AI is replacing manual research and traditional networking with real-time, data-driven decision-making. At Growthpal, AI has revolutionised two critical aspects of M&A. First, in deal sourcing and target identification, AI-powered models analyse vast datasets to identify potential acquisition targets in real time, considering factors such as industry trends, financial performance, and strategic alignment.

Second, AI plays a crucial role in matchmaking and synergy prediction. Sophisticated algorithms continuously optimise matches, factoring in multiple variables to assess compatibility and business synergies with precision. GenAI further enhances this process by predicting synergies between potential buyers and sellers, improving the accuracy of strategic fit assessments.

By leveraging AI, the M&A process has become significantly faster, more data-driven, and precise, giving companies a competitive edge in identifying and closing strategic deals

How accurate is AI-Driven analytics? What is the scope of biases?

AI-driven analytics is becoming more accurate with better data training and machine learning advancements, but it is not without challenges. The accuracy of AI models depends on the quality, diversity, and representativeness of training data. If the data is biased, incomplete, or outdated, it can lead to flawed insights. One common issue is data selection bias, where AI models may over-rely on historical patterns and fail to recognise emerging trends or new disruptors.

At Growthpal, we mitigate biases by validating data across 60+ sources before AI processes it. We ensure diversity in geographies, industries, and deal structures to prevent AI from favoring certain types of transactions or businesses. Additionally, we use a human-in-the-loop approach, where AI-generated insights are reviewed by industry experts before being shared with clients. This combination of AI efficiency and human expertise ensures that our recommendations remain reliable, fair, and insightful.

What challenges do you face in AI-Driven analytics and how do you resolve them?

One of the biggest challenges in AI-driven analytics is data quality and availability. In M&A, private market data is often fragmented and difficult to access as private companies are not mandated to disclose their data in countries like the US and others.

AI models require structured, high-quality data, which is not always readily available. Growthpal addresses this by using AI-powered data prediction and enrichment techniques, combining publicly available insights, proprietary datasets, and industry intelligence to fill in the gaps.

Another challenge is the balance between AI automation and human expertise. While AI can process and analyse vast amounts of data, M&A decisions require a deep understanding of business synergies, leadership vision and market conditions. At Growthpal, we ensure that AI augments rather than replaces human expertise, helping investors and businesses make smarter, well-informed decisions.

Lastly, businesses often demand causative and effective aspects and trust in AI-driven recommendations. To address this, we have built AI models that provide contextual justifications for each deal recommendation. Instead of just showing what deal to consider, our AI also explains why it is a good fit, helping clients build trust in the data-driven decision-making process.

What is your clients’ response to data analytics with AI?

Clients are increasingly seeing the value of AI-driven analytics, particularly in identifying hidden opportunities and accelerating decision-making. Many businesses that previously relied on manual research and industry connections now appreciate how AI can provide deeper, data-backed insights in a fraction of the time. Growthpal’s clients, including private equity firms and corporate M&A teams, have seen significant improvements in their ability to identify, assess, and execute deals more efficiently.

At Growthpal, we have focused on educating clients about AI’s capabilities, offering customised insights and ensuring that every AI-driven recommendation is backed by clear, explainable reasoning.

As businesses experience faster deal sourcing, reduced research time and higher-quality targets, the adoption of AI-powered analytics is rapidly growing in the M&A sector.

How difficult or easy is it for women entrepreneurs to reach the top and stay at the top?

I believe willingness to climb the ladder and stay at the top is more of a personal career choice, given their surroundings, support system and individual preferences.

The women I generally see around me are more family orientated. This takes one of their top priorities, thus not going for a higher promotion. Advancing in one’s career is a constant battle between where and whom to spend time with.

Some companies do have gender bias in leadership roles and some top leaders do question women’s commitment to work given their personal commitments. However, I do believe that if someone is truly ambitious and has proven it, there is no one to stop them.

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