Playing to win: How to compete in the AI era

Any business can make incremental gains from AI, whether that’s through writing emails faster or simplifying how spreadsheets are created. Creating a genuinely distinct competitive advantage, however, relies on your ability to create transformational gains. And that means preparing your people and company for the AI era and adapting at pace.

The majority of small to medium sized businesses (SMBs) I speak with are either already exploring applications or have them in production. What’s more, they’re well-versed in traditional tactics for competing, including initiatives such as cost leadership, product differentiation, customer experience, speed to market, and so on. While these methods for carving out a competitive advantage are tried and tested, AI has an incredible ability to compress time. Because of that, it transforms the competitive dynamics and demands new considerations.

To have a fighting chance, SMBs and enterprises alike will need to assess new dimensions for success. Naturally, the list will evolve in real time with AI, but technical leaders will play a pivotal role in bringing these strengths to fruition:

1. The algorithm advantage of combining unique models and data together to reveal differentiated outputs.
2. Data network effects to create a positive feedback loop where products and services grow more valuable the more users interact with them.
3. AI-human integration to enhance how people work throughout an organization and free time for high-value, strategic tasks.
4. Hyper-personalization where customer needs are anticipated and data drives more powerful experiences.
5. Autonomous operations to pave the way for much greater efficiencies while reducing operational costs.
6. Exponential innovation made possible by AI’s ability to achieve more in much shorter timeframes, setting innovative companies up to proliferate successes.
7. Intellectual property evolution to protect what’s created with AI and establish clear ownership.
8. Talent redefinition based on new skills and strength requirements and changing workforce expectations.
9. Developing swarm learning networks so AI agents can spin up multiple versions of themselves to handle bottlenecks of information and load balancing at a task level.
10. Leveraging multi-modal and/or agentic capabilities to shift to generative AI systems that can reason, plan, and complete tasks on behalf of humans.

Those who seize every chance to learn and experiment across these areas will see the greatest results. But for leaders feeling overwhelmed by where to start, focusing on two core areas will provide an invaluable head start in the AI race: your data and your people.

Getting your data house in order is a prerequisite for reliable and rewarding AI. Yet so many SMBs have been beguiled by the convenience of downloading generic applications, only to reveal vague responses that don’t answer their real business questions. The truth is there’s no short-cut to AI implementation. Models and applications need access to your corporate data—and high-quality data at that. After all, you can only trust the answers AI gives you if you can trust the data it’s trained on, and poor outputs will limit adoption. This makes good governance and a modern data strategy non-negotiable to building trust and engagement.

Rather than relying on point-to-point solutions, maximizing AI’s potential also means creating a smooth flow of data throughout the organization so that all functions can benefit from intelligence. When the technology behind this is built with flexibility, security, and scalability in mind, you can readily embrace new developments such as agentic AI. Opening the door to capabilities like agents can enable your business to grow faster than was possible before, lifting previous talent and cost constraints.

Harnessing AI’s potential also inherently relies on human qualities, such as openness, collaboration, and ability to think differently. This is backed by The World Economic Forum’s latest Future of Jobs Report, which lists creativity, analytical thinking, curiosity, and lifelong learning as core skills for 2030 alongside AI and big data. As AI continues to automate tasks, these skills will be indispensable for individuals while also ensuring that the business can shape AI intentionally and responsibly.

Leaders can assess their team’s AI readiness by asking questions such as: Do our people have the right skills and mindset to maximize the effectiveness of products and services? Have we set clear parameters on what AI should and shouldn’t do? Are the goals of the system designed to augment people and meet our business goals? Ultimately, it’s you and your people who can mitigate potential risks and maximize successes so that AI meets the business’s objectives.

Whenever leaders ask me for advice on implementing AI, I challenge them to think bigger about out the impact it will play on their industry and their ability to compete in the future. How can it solve a problem for their organization? By figuring this out and working backwards from the challenge, leaders can start making the right investments to build resilience. In the fast-paced AI arena, taking iterative steps and continuously learning along the way will help them achieve competitive leaps in the long run.