AI in 2025: What was Real, What Worked, and Where It was Used

Just a few years ago, AI was a niche technology - the stuff of research labs, sci-fi novels, or big tech R&D. In 2025, that's no longer the case. AI has moved into the heartbeat of the business world: 78% of organizations now use AI in at least one part of their operations.

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But adoption doesn't always mean mastery. While most companies have dipped their toes in AI, relatively few have unlocked its full potential - and even fewer report large-scale, measurable value. (McKinsey & Company)

This post gives a high-level view of where AI stood in 2025: who used it, why they used it, and where it mattered most. It will also outline the themes we'll explore in the rest of this series.

What “AI” Means Today

When people talk about AI in business, they're not just talking about one kind of tool. AI in 2025 typically includes:

• Foundation models & language AI - Large language models (LLMs) for text generation, summarization, coding, etc.
• Vision models - For image recognition, defect detection, or autonomous vision.
• Predictive analytics - Statistical/machine-learning models that forecast trends, demand, risk.
• Agentic systems - AI agents that plan, act, and execute multi-step workflows.
• Industry-specific platforms - Vertical tools built for finance, healthcare, manufacturing, and more.

These categories are overlapping: for example, a predictive analytics tool might also be an “agent” if it triggers actions based on its predictions.

Why Companies Are Using AI

Companies are investing in AI for several clear reasons:

• Efficiency: Automating repetitive tasks (e.g., customer service, data analysis) saves time and money.
• Insight & prediction: AI identifies patterns humans might miss - in sales, risk, demand, or operations.
• Innovation: Some companies use AI to generate new ideas, design products, or test business models.
• Competitive pressure: As more firms adopt AI, staying passive can feel like falling behind.
• Scalability: AI can scale certain tasks faster than hiring more humans, especially for data-heavy work.

How Widespread AI Is (Adoption & Market Size)

Here's where things stand as of 2025:

• 78% of organizations report using AI in at least one business function.
• According to McKinsey's “State of AI in 2025” survey, 88% of respondents say their organizations use AI in at least one function - but most are still in early-stage adoption.
• On the growth front: the global AI market was estimated at US$ 106 billion in 2024 and is projected to rise to about US$ 178 billion in 2025, with massive long-term expansion.
• Generative AI is particularly popular: 71% of organizations now use it in one or more functions.
• Company-size matters: larger firms are more likely to scale AI. McKinsey finds that ~23% of respondents report scaling “agentic” AI systems. (McKinsey & Company)
• But value isn't universal: in McKinsey's survey, only 39% of respondents saw a measurable enterprise-wide EBIT (profit) impact from AI.

Where AI Is Making Its Mark: Key Industries

AI isn't limited to one sector. Across industries, we see very different patterns of adoption and use. Some of the fastest adopters and popular applications:

• Information Technology / Telecom: High adoption for automation, infrastructure optimization, and software development.
• Retail & Consumer: AI is used for demand forecasting, personalized marketing, and customer service.
• Financial Services: Fraud detection, risk scoring, and algorithmic trading are top use cases.
• Manufacturing: Predictive maintenance, quality control, and supply chain optimization are all powered by AI.
• Healthcare & Life Sciences: Diagnostics, patient monitoring, and drug discovery are common AI applications.
• Education: More institutions use AI for adaptive learning, grading, and administrative tasks.

The Reality Check: Adoption vs. Impact

• AI adoption is high, but it's not a silver bullet. Here are some key tensions:
• Early-stage use: According to McKinsey, most companies are still in the “experiment” or “pilot” phases. (McKinsey & Company)
• Value capture: Only a minority of firms achieve enterprise-level financial impact.
• Scaling challenges: Technical debt, data-quality issues, and lack of alignment across the organization slow down massive rollouts.
• Skill gaps: Many companies still lack the talent or leadership vision to fully leverage AI.
• Governance & strategy: According to BCG, very few organizations (~5%) are “AI high performers” that systematically track value and integrate AI into core workflows. (Business Insider)

What This Series Will Cover

Over the next few posts, we'll explore how different industries are using AI - and where it actually delivers value. Here's a roadmap:

• AI in Healthcare
• AI in Finance
• AI in Retail & E-commerce
• AI in Manufacturing
• AI in Logistics & Transport
• AI in Education
• AI in Entertainment & Media
• Emerging & Niche AI Platforms
• Challenges, Risks & the Road Ahead

Final Thoughts: Why This Moment Matters

AI in 2025 was already past the hype cycle. It's no longer a “future idea” - it's a practical lever for business transformation. But high adoption doesn't guarantee high impact. The real challenge for companies now is not just getting AI, but using AI well: integrating it in workflows, tracking value, building skills, and doing it in a way that scales.