TL;DR: Despite generative AI’s dramatic adoption surge—with UK ChatGPT spending up 835% year-on-year—only 9% of organisations use AI for data analysis. Industry experts argue analytical AI remains essential for converting data into investor-grade insights, and that combining both technologies delivers up to 40% more economic value than either approach alone.

Since ChatGPT’s meteoric rise, generative AI has dominated headlines and boardroom conversations. Its ability to analyse large unstructured datasets and produce human-like content has rapidly lowered barriers to innovation across marketing, coding, and product design. However, this focus risks overshadowing analytical AI—the prediction technology that helps businesses anticipate behaviours and forecast outcomes.

Research shows UK companies have dramatically increased ChatGPT usage, with enterprise spending rising 835% year-on-year as of late 2024, representing one of the most dramatic AI adoption surges ever recorded. In stark contrast, only 9% of organisations use AI for data management and analysis, despite consistent UK government and industry surveys identifying this as AI’s most valuable application.

Complementary Technologies, Not Competitors

The two AI approaches are not mutually exclusive but complementary when used strategically. In website performance, for example, generative AI streamlines content creation and delivers personalised experiences, whilst analytical AI enhances strategic decision-making by identifying which content drives engagement, optimal timing for offers, and likely drop-off points.

McKinsey research indicates generative AI use cases could add up to 40% to the economic benefits projected for analytical AI applications. Rather than viewing generative AI as marginal improvement, it functions as a powerful multiplier of AI’s overall economic potential—but only when built upon solid analytical foundations.

The Investment Case for Analytical AI

The investment landscape has fundamentally shifted. Investors now demand deep, granular data and clear metrics demonstrating operational efficiency, customer retention, and revenue forecasting accuracy. Simply presenting growth figures is insufficient without detailed analytics and robust evidence.

Analytical AI enables businesses to make sense of data at scale, converting raw metrics into meaningful narratives about revenue predictability, customer lifetime value, and retention rates. These evidence-backed stories build investor confidence. Whether predicting churn, evaluating supply chain performance, or analysing product usage, analytical insights are essential for assessing both current health and future potential.

Many private equity firms now use AI themselves to evaluate prospective investments, leveraging advanced analytics to assess risk, benchmark performance, and model exit scenarios. Firms not investing in analytical AI risk falling behind as robust AI due diligence becomes standard practice.

Integration Strategy

For organisations beginning analytical AI adoption, experts recommend a five-step approach: identify high-impact opportunities aligned with business goals; define clear, measurable objectives; assess data readiness and address quality gaps; build robust data foundations with modern platforms and pipelines; and invest in employee training whilst fostering experimentation culture.

When analytical and generative AI are integrated, analytical AI monitors performance and identifies patterns from massive datasets, whilst generative AI transforms findings into automated reports, visualisations, and natural language summaries. This combination creates significant competitive advantage, particularly when pitching to private equity investors seeking both analytical depth and compelling storytelling.

Source: TechRadar