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Now AI is everywhere in businesses, is anyone actually using it?

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  Published in Business and Finance on Monday, September 29th 2025 at 8:19 GMT by TechRadar
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AI in the Office: Hype, Reality and the Reality Gap

In the age of generative language models, cloud‑based analytics, and self‑learning chatbots, the buzz around artificial intelligence (AI) has become almost ubiquitous. Every press release, every tech‑conference keynote, and every corporate quarterly now mentions AI – sometimes as the cornerstone of a company’s strategy, other times simply as a marketing buzzword. Yet a recent TechRadar Pro feature—“Now AI is everywhere in businesses – is anyone actually using it?”—tells a more nuanced story. While AI tools are everywhere, real, measurable usage remains patchy, and most firms still struggle to move beyond the “AI‑enabled” label.

The Ubiquity of the AI Label

The article opens with a striking statistic: over 80 % of surveyed enterprises claim to have an AI strategy. That figure, sourced from a Gartner 2024 AI adoption survey, mirrors similar findings from McKinsey’s “The state of AI 2024.” The sheer number of AI‑branded solutions—CRM systems that flag “smart” lead scoring, HR platforms that promise “AI‑driven talent insights,” finance software that offers “AI‑enhanced forecasting”—has made the term a default for new technology investments.

Yet the same TechRadar Pro piece points out that only about 30 % of those companies report a tangible increase in efficiency or revenue attributable to AI. That gap suggests a difference between having an AI tool and leveraging it effectively. In many cases, the tools are more “augmented” than truly autonomous: they use rule‑based logic under the hood or rely on static data sets rather than real‑time learning.

How Businesses Are (Or Aren’t) Using AI

The article breaks down AI usage by functional area:

While customer‑facing AI tools are the most common, they tend to be the easiest to justify because gains can be measured in metrics like net promoter score or average handling time. In contrast, operational or strategic AI use—such as predictive maintenance or strategic portfolio analysis—requires deeper data integration and a culture shift that many organisations are not yet ready for.

The Real‑World Stories Behind the Numbers

The feature includes a handful of case studies that illustrate both successes and shortcomings.

1. Retailer “ShopNow” – Implemented an AI‑driven recommendation engine on its e‑commerce platform. Early results showed a 22 % lift in average order value, but only after two months of data cleansing and feature engineering. The CEO notes that the “AI hype was real, but the data prep took longer than expected.”

2. Finance Firm “CapitalFlow” – Adopted an AI anomaly‑detection system for fraud monitoring. The system reduced false positives by 35 % and saved the company roughly £1.2 million in manual review time over the first year. However, the system required an in‑house data science team and ongoing model retraining, which many mid‑size firms lack.

3. Manufacturing “IronWorks” – Introduced an AI‑based predictive maintenance tool that cut machine downtime by 12 %. The challenge, as highlighted by the article, was the legacy data infrastructure: without a unified data lake, the model had to rely on disparate sources, limiting its accuracy.

These stories underscore a recurring theme: AI implementation is as much about organisational readiness as it is about technology. Firms that invest in data governance, talent, and iterative experimentation tend to reap the most benefits.

The Barriers That Keep Most Companies Stuck

TechRadar’s piece dives into several hurdles that prevent AI from becoming a mainstream business engine:

1. Data Quality and Availability – AI thrives on clean, well‑labelled data. Many organisations still store data in siloed spreadsheets or legacy ERP systems, making it difficult to feed AI models.

2. Talent Shortage – According to the article, over 60 % of respondents say they lack in‑house data scientists or AI specialists. Outsourcing or managed services can mitigate this, but the cost and integration risk remain.

3. Change Management – Even with a functioning model, getting employees to trust and use AI tools is a cultural shift. The article cites a Deloitte study that found only 28 % of employees feel comfortable using AI in their daily tasks.

4. ROI Measurement – Pinpointing the return on investment for AI projects is notoriously tricky. The TechRadar piece notes that many firms struggle to tie AI outputs to financial KPIs, leading to cautious or stalled projects.

The Future: Generative AI and AI‑First Architectures

The article concludes on a forward‑looking note. As large language models (LLMs) and generative AI become more mature, many companies are shifting toward an “AI‑first” mindset. Tools like OpenAI’s GPT‑4, Google Gemini, and Microsoft’s Copilot are being integrated into Microsoft Office, Salesforce, and other enterprise suites, offering pre‑built AI capabilities that bypass the need for building models from scratch.

Yet even with these plug‑and‑play solutions, the underlying challenges persist. The real question, the TechRadar article posits, is whether organisations are ready to move from “AI‑enabled” to “AI‑driven.” That transition requires not just technology but also data strategy, governance, talent, and an appetite for experimentation.

Bottom Line

The TechRadar Pro feature paints a realistic picture of AI’s current state in business: widespread enthusiasm and significant deployment, but uneven, modest, and often constrained by data, talent, and culture. For companies that are truly “using” AI—meaning they have end‑to‑end pipelines, continuous model improvement, and measurable business impact—the benefits are clear. For the majority, however, AI remains an alluring buzzword that has yet to translate into consistent value.

As AI continues to evolve, the next few years will likely see a sharper divide: firms that invest in the infrastructure and mindset required to harness AI effectively will reap outsized rewards, while those that treat AI as a marketing fad may find themselves left behind. The challenge for business leaders is to move beyond hype, embed AI into processes, and create a culture that is comfortable with data‑driven decision‑making.