Since ChatGPT’s meteoric rise, generative AI has dominated headlines and boardroom conversations, and for good reason.
Its extraordinary ability to analyze large amounts of unstructured data, identify patterns and extract valuable insights from real human content has made many things that were once difficult much easier.
From creating marketing content to writing code and designing product prototypes, generative AI has rapidly broken down the barriers to innovation across a wide range of business functions. In many ways, the hype is real and has enormous potential to reshape entire industries.
But as enticing as this exciting leap in innovation may seem, it’s important that business leaders don’t overlook an equally important aspect of AI: analytical AI.
Analytical AI, often referred to as traditional AI, focuses on analyzing data to produce meaningful insights that support decision-making processes. It uses machine learning, natural language processing and data mining techniques to interpret data and make predictions or recommendations.
It is basically a predictive technology that helps businesses anticipate behavior and predict possible outcomes.
Because enterprise adoption still lags behind
But analytical AI risks being overshadowed by the flashier and more innovative applications of its generative counterpart, even though AI has consistently proven its business value through techniques tested and refined over decades.
Research shows that UK businesses have significantly increased their use of ChatGPT, with spending on the tool increasing by 835% year-on-year by the end of 2024.
This is one of the most dramatic increases ever recorded in corporate adoption of AI tools. However, only 9% of companies use AI for data management and analysis, despite the technology’s potential value.
This figure comes from official UK government and industry surveys, which consistently show that data management and analytics is the most common application of AI, but overall adoption remains low.
This can be a costly mistake in many ways. Importantly, leaders must recognize that generative AI and analytical AI are not mutually exclusive. In fact, they complement each other when used strategically.
Take website performance, for example: generative AI optimizes content creation and delivers dynamic, personalized experiences tailored to specific audiences and demographics, while analytical AI improves strategic decision-making, such as identifying which content drives engagement, the best time to view offers, or where users are most likely to leave.
According to a McKinsey study, generative AI use cases can increase the expected financial benefits of analytical AI use cases by up to 40%. Generative AI should therefore not be seen as a marginal improvement, but rather as a powerful multiplier of AI’s overall economic potential.
Artificial intelligence at the forefront: a compelling story to attract investment
This idea is even more important for companies looking for investment. The investment landscape has changed dramatically: investors now demand more than a compelling story or an innovative idea.
They expect comprehensive, detailed data and clear metrics that demonstrate operational efficiency, customer loyalty, revenue forecasting accuracy and real market traction. It is no longer enough to simply state “what”.
Today, investors are working even harder to understand the “how” and “why” behind performance and trends. It is no longer enough to say that your profitability increased by X% compared to the previous year: this must be supported by detailed analysis and concrete evidence.
Analytical AI is critical. Simply put, it allows companies to not only collect data, but also interpret it at scale. Turn raw metrics into actionable reports—evidence-based stories about revenue predictability, customer lifetime value, and customer retention—that help you increase investor confidence in management’s claims.
By turning large amounts of data into actionable insights, analytical AI uncovers trends, inefficiencies and identifies opportunities for optimization. Whether predicting churn, evaluating supply chain performance, or analyzing product usage, this information is essential to understanding the company’s current state and future potential.
This type of clarity helps investors evaluate scalability and reduces perceived risk. In our experience, this approach makes a significant difference in terms of attractiveness to investors.
In a saturated market, differentiation is everything, and an AI-powered analytics story turns a hopeful proposition into a compelling, evidence-based investment case that is much more likely to be funded, on favorable terms.
Finally, it is important to note that many private equity firms are now using AI to evaluate potential investments. They use advanced analytics to assess risk, compare performance and model different exit scenarios.
This means that if your company is not already investing in AI analytics, you risk being left behind as the importance of robust AI due diligence in private equity (PE) continues to grow.
Therefore, the business case for analytical AI must be clear. But for companies that haven’t yet made it a priority, the big question is: where to start? Here are five easy steps to integrating analytical AI into your technology operations:
1. Identify AI opportunities:
The first step is to understand where analytical AI can provide the greatest benefit. Common principles are customer segmentation, retention rates and price optimization. Always ensure that the use cases you choose align with your broader business goals and long-term vision.
2. Set clear goals:
After identifying high-impact AI use cases, the next step is to define clear and measurable goals. Whether it’s improving the customer experience, reducing inefficiencies, or saving money, clear metrics of success help you define your efforts, align teams, and ensure the impact of analytical AI can be tracked and communicated.
3. Evaluate the availability of your data:
Analytical AI is only as good as the data it is based on. Therefore, it is important to evaluate the quality, availability and relevance of existing data before launching an AI initiative. Identifying and closing gaps in accuracy, consistency or availability ensures that your AI efforts are built on a solid, reliable foundation.
This must be supported by practical safeguards and defined procedures so that employees understand how to handle and use data responsibly and effectively.
4. Create the databases:
A robust database is the foundation of any successful AI analytics initiative. This includes establishing a good data architecture, design process and governance to ensure that data flows smoothly, remains secure and is always available for analysis.
The focus is on a modern data platform that enables structured, analysis-ready data sets. Business-savvy IT teams play a critical role in this phase: designing automated data pipelines and ensuring data integrity between systems.
This scalable and secure database creates the data platform on which AI analytics models can be trained and continuously improved.
5. Invest in training and development:
To make the most of the potential of analytical AI, it is important to equip your employees with knowledge of AI tools and data analysis. This requires not only investment in education, but also fostering a culture that encourages experimentation and continuous improvement, and where leaders actively embrace the innovation and growth potential of AI.
Start small: Start by giving your employees the opportunity to try small pilot projects to evaluate their impact. Define clear KPIs for each initiative and conduct regular evaluations.
Even on a small scale, these projects will provide valuable insights that can inspire broader efforts. To support your progress, it is always a good idea to work with an experienced data consulting specialist who can help you achieve your goals.
The power of partnership: when analytics and generative AI meet
While the widespread appeal of flashy generative AI is undeniable, analytical AI remains critical to fully realizing its economic and strategic potential. Think of it as an engine room, not a showroom. It’s the powerhouse behind the scenes that turns data into information that helps you make smart decisions.
But when analytical AI and generative AI are integrated, analytical AI can monitor performance, identify trends and identify patterns from massive data sets, while generative AI turns this information into automated reports, visualizations, investor briefings or natural language summaries.
Especially during a PE project, a solid AI foundation creates both analytical depth and generative storytelling – a significant competitive advantage and can ultimately be the deciding factor in a company’s competitiveness and long-term investor attractiveness.