Since the meteoric rise of ChatGPT, generative AI has dominated headlines and boardroom conversations, and for good reason.
With its remarkable ability to analyze large unstructured data sets, identify patterns and extract valuable insights into content that genuinely appears human-like, many things that were once difficult to do have become much easier.
From crafting marketing content to writing code and even designing product prototypes, generative AI has rapidly lowered the barrier to innovation across a range of business functions. In many respects, the hype is real – along with its immense potential to reshape entire industries.
CTO and Co-Founder of JMAN.
However, as alluring as this exciting innovation leap appears, it’s vital that business leaders do not overlook an equally vital aspect of AI: analytical AI.
Analytical AI, often referred to as traditional AI, focuses on analyzing data to extract meaningful insights to support decision-making processes. It involves using machine learning, natural language processing and data mining techniques to interpret data, and make predictions or recommendations.
In essence, it’s a prediction technology – helping businesses to anticipate behaviors and forecast potential outcomes.
Why business adoption still lags behind
Still, despite consistently proving its business value through well-tested techniques refined over decades, analytical AI risks being overshadowed by the more showy and novel applications of its generative counterpart.
Research shows that UK companies have dramatically increased their use of ChatGPT, with spending on the tool rising by 835% year-on-year as of late 2024.
This constitutes one of the most dramatic increases in enterprise AI tool adoption ever seen. In contrast, only 9% of organizations 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 analysis is the most common application of AI, but overall adoption remains low.
This could be a costly mistake on numerous fronts. Foremost, leaders should recognize that generative AI and analytical AI are not mutually exclusive. In fact, they complement one another when used strategically.
Take website performance, for example: generative AI streamlines content creation and delivers dynamic, personalized experiences tailored to specific audiences and demographics, while analytical AI enhances strategic decision-making – such as identifying which content drives engagement, the best time to display offers, or where users are most likely to drop off.
According to research from McKinsey generative AI use cases could add to the economic benefits projected for analytical AI use cases by up to 40%. In this way, generative AI shouldn’t be seen as a marginal improvement, but rather as a powerful multiplier of AI’s overall economic potential.
AI at forefront – A compelling narrative to attract investments
This notion is even more critical for firms seeking investment. The investment landscape has fundamentally shifted – investors now demand more than a compelling story or an innovative idea.
They expect deep, granular data and clear metrics that demonstrate operational efficiency, customer retention, revenue forecasting accuracy, and real market traction. It’s no longer sufficient to simply present the “what.”
Today’s investors are even more focused on understanding the “how” and “why” behind performance and trends. Saying you’ve grown profitably by X% year-over-year is no longer enough – it must be backed by detailed analytics and robust evidence.
Analytical AI is critical. Put simply, it enables businesses not just to collect data, but to make sense of it at scale. It turns raw metrics into meaningful narratives – evidence-backed stories about revenue predictability, customer lifetime value, and retention rates – that help build investor confidence in management’s claims.
By converting large datasets into actionable intelligence, analytical AI reveals trends, uncovers inefficiencies and identifies opportunities for optimization. Whether it’s predicting churn, evaluating supply chain performance, or analyzing product usage, these insights are essential for understanding a company’s current health and future potential.
This kind of clarity helps investors assess scalability and reduces perceived risk. In our experience, this approach makes a significant difference to investor appeal.
In a crowded market, differentiation is everything – and an analytical AI-driven narrative transforms a hopeful pitch into a compelling, evidence-backed investment case that’s far more likely to secure funding – and on favorable terms.
Lastly, it’s important to note that many PE firms are now using AI themselves to evaluate prospective investments. They’re leveraging advanced analytics to assess risk, benchmark performance, and model different exit scenarios.
This means that if your firm isn’t already investing in analytical AI, you risk falling behind the curve as the importance of robust AI due diligence in private equity (PE) continues to grow.
And so, the business case for analytical AI may be clear. But, for firms that haven’t yet made it a priority, the big question is – where to begin? Here are five simple steps for integrating analytical AI into your tech firm:
1. Identify the AI opportunities:
Understanding where analytical AI can deliver the most value is the first step. Common starting points include customer segmentation, retention rates and pricing optimization. Always ensure that chosen use cases align closely with your broader business goals and long-term vision.
2. Define clear objectives:
With your high-impact AI use cases established, the next step is to set clear, measurable goals. Whether it’s improving customer experience, reducing inefficiencies or making cost savings, having clear success metrics in play will help to define your efforts, keep teams aligned and ensure the impact of analytical AI can be tracked and communicated.
3. Assess your data readiness:
Analytical AI is only as effective as the data it draws from. That’s why, before starting any AI initiative, it’s crucial to assess the quality, accessibility, and relevance of your existing data. Identifying and addressing gaps in accuracy, consistency, or availability will ensure your AI efforts are built on a solid, reliable foundation.
This should be supported by practical safeguards and defined procedures so that employees understand how to manage and use data responsibly and effectively.
4. Build the data foundations:
A robust data foundation is the bedrock of any successful analytical AI initiative. This involves setting up the right data architecture, engineering pipelines, and governance to ensure data flows seamlessly, remains secure, and is readily available for analysis.
At the core of this is a modern data platform to enable structured, analysis-ready datasets. Data engineering teams with the right business acumen play a critical role in this phase – designing automated data pipelines and ensuring data integrity across systems.
This scalable and secure data foundation creates the data platform from which analytical AI models can be trained and continuously improved.
5. Invest in training and education:
Equipping your employees with skills in AI tools and data analysis is essential to fully capitalize on the potential of analytical AI. This requires not only investing in training but also fostering a culture that encourages experimentation and continuous improvement, with leadership actively championing AI’s innovation and growth potential.
Start small: Start by allowing employees to experiment with small pilot projects to evaluate their impact. Set clear KPIs for each initiative and conduct frequent reviews.
Even on a small scale, these projects will provide valuable insights that can inform larger efforts. To support your progress, it’s always advisable to consider partnering with a specialist data consultancy with experience that can help support your goals.
Power in partnership: when analytical meets generative AI
Though the wide-reaching appeal of flashy generative AI may be undeniable, analytical AI remains essential to fully unlock AI’s economic and strategic potential. Think of it as the engine room rather than the showroom. It’s the behind-the-scenes powerhouse that turns data into the insights driving smart decisions.
Yet, when analytical AI and generative AI are integrated, analytical AI can monitor performance, detect trends, and identify patterns from massive datasets – while generative AI transforms these findings into automated reports, visualizations, investor briefings, or natural language summaries.
Especially when pitching for PE – a strong AI foundation supports both analytical depth and generative storytelling – creates a significant competitive edge, and, ultimately, can be the deciding factor in a company’s long-term competitiveness and attractiveness to investors.
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This article was produced as part of TechRadarPro’s Expert Insights channel where we feature the best and brightest minds in the technology industry today. The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/news/submit-your-story-to-techradar-pro
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