Artificial intelligence has moved past the pilot phase. Most large companies have run experiments. Many have made real investments. But as AI systems become more capable, and business leaders more familiar with what they can and can’t do, it’s about putting it to work in organizations. And this is a strategic, not tactical, move.
In 2024, corporate investment in AI reached $252 billion globally. Nearly four out of five organizations now report using AI in at least one function. But the distribution of results is uneven. Some companies have found their footing, as they’ve embedded AI into core operations, customer interactions, or product development. Others are still stuck at the experimentation stage, struggling to generate value or make the technology relevant to strategic priorities. And for both, it’s a question of justifying the investment to their stakeholders.
Strategy, not systems
A few years ago, AI was mostly associated with efficiency. Truth be told, no one considered this to be “AI”, but saw this rather as automation.
Automating low-level tasks, cutting costs in call centers or back-office operations. That’s still important, and many of those gains are real. But the leading organizations today are pointing AI at higher-value targets.
In some industries, this shift is already well underway. Pharmaceutical companies are using AI not just to optimize processes, but to accelerate discovery. Biopharma firms like Genentech are applying machine learning to identify molecules with therapeutic potential faster than traditional methods. In aerospace, AI helps engineers explore more complex design options, supporting new product development. In both cases, the value comes not just from efficiency, but from expanding the solution space.
There’s a similar pattern in consumer-facing sectors. Companies like Walmart are embedding AI into search, recommendations, and supply chain logic. They’re using AI models trained on internal data to support both employee workflows and customer experience, effectively making AI part of the operating model. The point isn’t to replace employees. It is to do more, with less resources.
Moving from experiments to capabilities
Many companies still treat AI as a project. Something managed by a technical team, layered onto a workflow, and tracked for ROI. Ah yes, the favorite word of stakeholders. The ROI.
But for firms that are now seeing the most value, AI has become part of how the organization functions. That shift usually requires a few things:
- Clear use-case prioritization. The most mature companies don’t try to apply AI everywhere at once. They start with a few high-leverage areas , often supply chain, sales forecasting, or customer engagement and focus on making those work at scale.
- Cross-functional integration. Technical performance isn’t enough. To be useful, AI needs to be wired into the organization: its systems, people, and decision flows. That means collaboration across product, tech, legal, operations, and leadership. Not developing solutions in isolation.
- Leadership commitment. Where AI has become a capability, the C-suite is involved not just in approving budgets, but in setting expectations, managing risk, and reshaping how performance is measured. It’s a strong selling point not only to customers, but to employees as well.
In many cases, it also means treating AI like a business capability, not an IT system. This includes building supporting infrastructure, defining ownership, and creating processes for testing, deployment, and governance. Several large companies — including Amazon, Morgan Stanley, and Regeneron — have begun building internal teams to develop, evaluate, and operate AI models across the organization.
Cost cutting isn’t… cutting it
There’s growing recognition that the business case for AI needs to go beyond cost efficiency. In retail and consumer services, personalization powered by AI is driving real revenue gains. In industrial companies, predictive maintenance and dynamic scheduling are improving uptime and throughput. And in many sectors, AI is helping teams respond faster to customer needs.
A recent BCG study found that only 4% of organizations have reached full-scale, cross-functional deployment of AI with clear strategic impact. But those that have are seeing significantly higher revenue growth. Many have also shifted their investment focus, allocating more budget to people, process, and integration than to software or infrastructure.
The takeaway is simple. Treating AI as a capability requires investment not just in tools, but in how the company operates. That includes hiring and training, redesigning roles, and developing governance structures that can manage AI systems at scale.
The next
Companies are still early. Most are somewhere between experimentation and scale. But the direction is clear. As the technology matures, and as use cases become more repeatable, more organizations will start thinking about AI less as a project, and more as part of how they work.
In practical terms, this means building the systems and teams that can sustain AI use over time. It means shifting from “where can we use AI?” to “how does AI help us compete?” And it means making AI part of the strategic agenda.
AI doesn’t solve strategy. But for companies that are ready, it can become a key tool in executing it.
- Stanford Institute for Human-Centered Artificial Intelligence (HAI)
AI Index Report 2025
https://aiindex.stanford.edu/report/ - Boston Consulting Group (BCG)
Where’s the Value in AI? (AI Radar Report 2024)
https://www.bcg.com/publications/2024/wheres-the-value-in-artificial-intelligence - McKinsey & Company
The State of AI in 2024: Generative AI’s Breakout Year
https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2024 - Deloitte Insights
Tech Trends 2025: A Compass for Generative Business
https://www2.deloitte.com/us/en/insights/focus/tech-trends/2025/overview.html - Harvard Business Review
Artificial Intelligence for the Real World (classic framing still widely referenced)
https://hbr.org/2018/01/artificial-intelligence-for-the-real-world - MIT Sloan Management Review
The Cultural Factors Behind Successful AI Adoption
https://sloanreview.mit.edu/article/the-cultural-factors-behind-successful-ai-adoption/ - Innovation Leader
How the 10 Biggest U.S. Companies Are Using Generative AI (2025)
https://www.innovationleader.com/generative-ai/how-the-10-biggest-us-companies-are-using-generative-ai/ - World Economic Forum / IBMThe Future of Jobs Report 2023 – sections on AI workforce planning
https://www.weforum.org/reports/the-future-of-jobs-report-2023/