How AI Leaders Stand Out: What Successful Companies Do Differently
AI is transforming industries, but not all businesses achieve the same level of success. Some companies maximize AI’s potential, while others struggle to see significant returns.
In 2021, a study by MIT and McKinsey identified five key factors that set AI leaders apart: governance, deployment, partnerships, talent, and data management. By 2023, with the rise of generative AI, the study was updated, revealing three major trends:
1. The AI Performance Gap is Growing
Top AI adopters are now 3.8 times more successful than lower performers, up from 2.7 times in 2021. Their AI capabilities improve over time, compounding their advantage.
2. Faster ROI on AI Investments
In 2021, only leading companies saw AI returns within 6-12 months, while others took 18-24 months. By 2023, even laggards achieved faster payback, thanks to improved governance, better data, and AI software providers reducing upfront costs.
3. Smarter AI Implementation
While AI adoption has surged, successful companies carefully choose low-risk, high-value AI use cases, avoiding wasted investments.
Four Key Factors Behind AI Success
1. Strong Leadership Support
In 2023, over 75% of AI leaders had direct support from CEOs or boards. AI investment often lacks immediate returns, so leadership backing is crucial.
For example, Cooper Standard, an industrial manufacturer, initially struggled with AI. A senior leader took charge, leading to an in-house AI-driven process that became a profitable business unit.
2. A Strong Network of Partners
AI leaders balance in-house capabilities with external expertise. 90% of leaders develop AI internally, but 66% also work with external partners.
Partnerships have shifted from academia and startups (common in 2021) to consultants, vendors, and industry experts by 2023. For instance, Freeport-McMoRan, a mining company, applied AI techniques from pharmaceutical firms to optimize chemical mapping.
3. Effective Cross-Department Collaboration
AI thrives when IT and operations work together. Many companies create Centers of Excellence (CoEs) to oversee AI projects, set standards, and manage cybersecurity.
For example, Target’s CoE built a generative AI chatbot in six months, rolling it out to 2,000+ stores to improve employee training and operations.
Some firms, instead of using a CoE, form specialized teams within business units for more customized AI solutions.
4. High-Quality Data Management
AI success depends on accurate, well-organized data. Leaders invest in cloud-based storage and sensor-driven data collection to optimize operations.
Titan Cement deployed thousands of sensors and centralized its data, using AI to increase efficiency and cut costs, achieving a 500% ROI.
Generative AI also improves decision-making. Panasonic Energy North America developed an AI-powered maintenance assistant, analyzing 1M+ service tickets to reduce downtime and enhance productivity.
The Future of AI in Business
AI is more accessible than ever, even for companies with limited expertise. However, long-term success requires strategic implementation.
Companies that focus on executive support, partnerships, collaboration, and data management will stay ahead. Those that don’t risk falling behind in an AI-driven world.
