Beyond the Algorithm: How to Build a Successful AI-Driven Enterprise

Successful AI transformation starts with developing the right mindset, which means fostering an environment where data-driven decisions, continuous learning, and cross-functional collaboration are the norm. This shift starts with leadership and extends to every level of the organization.

In a recent memo to Shopify employees, CEO Tobi Lütke described AI adoption as a "baseline expectation" at the company and noted that AI competencies will be part of performance evaluations. This is a clear signal: the future belongs to those who treat AI not as just a tool or add-on, but as a core capability.

To foster this kind of culture, organizations need a balanced approach to AI adoption that includes the following pillars:

1. Education and Training. To truly integrate AI into everyday work, employees need to understand how it works and how to work with it. Many companies are launching AI training programs to build AI literacy among non-technical staff so they can understand under the hood of AI tools and review and interpret their outputs. 
2. Leadership Support. Companies should create an environment where leaders  champion AI adoption, set a compelling vision, and model the behaviors they want to see. Employees may worry that AI will replace them or radically change their jobs. Successful leaders proactively communicate that AI will augment their teams’ work rather than replace them. It will automate many lower-value tasks, enabling employees to focus on high-value thinking. Importantly, these leaders back this up by investing in training and clear career paths in an AI-driven organization.
3. Employee Engagement. At the same time, AI adoption cannot be imposed top-down. It must be a shared journey. Employees often have the deepest understanding of day-to-day operations. Including them in designing and refining AI systems ensures that solutions are practical and welcomed. Conversely, purely top-down rollouts often meet resistance.
4. Experimentation. An AI-first culture supports experimentation without fear of failure. Organizations should create safe environments for teams to test ideas, build prototypes, and learn. To illustrate, Royal Bank of Canada has launched an internal AI lab, RBC Borealis AI, to explore AI applications for finance workflows.

One compelling example of this kind of cultural transformation is Miro. Once known as a digital whiteboard collaboration tool, it has rapidly evolved into an AI-powered Innovation Workspace used by over 90 million users.

This transformation is grounded in a company-wide shift toward an AI-first mindset. Miro has invested heavily in internal talent, upskilling, and embedding AI across product teams. With this approach, employees use and refine the same features they ship to customers.

Ultimately, the goal is not to have a few AI-powered use cases scattered across the business, but to create a culture where every team sees AI as an enabler, not a disruption. 

The next critical element is cross-domain expertise. The adage "AI is only as good as the data that AI is trained on" holds true. AI models are powerful, but they are not infallible. Without properly curated datasets and expert oversight, companies risk deploying AI solutions that produce unreliable results, which can be costly and even harmful. This is particularly risky in high-stakes fields such as healthcare, law, and finance. 

To mitigate these risks, enterprises should cultivate cross-disciplinary roles. These are professionals who understand both AI and their specific domains. Take healthcare as an example. Organizations implementing AI should have team members who not only understand medical practices, but are also fluent in technology. They act as key stakeholders and co-creators of AI solutions, translating real-world problems into objectives for data scientists. Then act as guides validating datasets and shaping how AI is integrated into workflows. 

As AI technology matures, the demand for these hybrid professionals who can translate technical capabilities into domain-specific needs will continue to rise. You can’t build effective solutions if you don’t understand both business problems and technology.

According to McKinsey, one of the most frequent barriers to AI adoption is a lack of talent with appropriate skill sets for AI work. To overcome this challenge, organizations are increasingly relying on a combination of strategic hiring, upskilling internal talent, and forming external partnerships with AI experts, startups, academia, and broader ecosystems.

Talent Acquisition and Upskilling. Some enterprises take a proactive approach to AI by building internal AI teams, either through direct hiring or using strategic Mergers and Acquisitions as a way to bring in AI talent and intellectual property. Walmart, for example, established its internal tech division, Walmart Global Tech, which has grown from Kosmix, a startup acquired by Walmart in 2011. This team now empowers Walmart to lead the retail disruption.

However, not every organization needs to have an internal AI lab, which is impossible for many companies. As we discussed earlier, what's critical is having people with cross-domain expertise on the ground and bringing in specialized partners for deeper technical capabilities.

Strategic Partnerships with Academia. Collaborating with universities and research institutions is a win-win approach to access cutting-edge expertise. These collaborations can also serve as pipelines for future talent. In return, academia gains real-world data and problems to test theories. 

GE Healthcare, for example, partners with the University of California San Francisco (UCSF) to co-develop AI algorithms for medical imaging diagnostics and treatments of neurodegenerative disease and cancer. 

Start-ups and SMEs can also tap into academic networks. A manufacturer might partner with a university's engineering department to develop an AI model for defect detection, or a hospital might work with an academic AI center on medical image analysis.

Strategic Partnerships with AI Innovators. In some cases, instead of reinventing the wheel and building AI systems from scratch, a company can collaborate with a specialized AI vendor to bring AI expertise. These partnerships offer faster time-to-value, lower R&D costs, and access to specialized models or platforms that would be time-consuming to develop internally.

Turing, for example, helps companies in various industries, from healthcare to automotive, scale their AI efforts. Turing connects them with a global pool of over 4 million vetted engineers and data scientists. This allows enterprises to quickly assemble AI teams focused on model development, fine-tuning, and deployment without the lengthy and costly process of traditional hiring.

This can be seen in the case of a leading global consulting firm, operating across 37 countries. The company partnered with Turing to improve its freight forecasting capabilities using predictive AI. Turing’s AI-driven solution resulted in:  

• 95% accuracy in freight demand predictions, 
• 30% reduction in empty mile runs, 
• 15% annual savings in fuel costs, 
• 25% improvement in on-time deliveries.

This case demonstrates the transformative power of domain-aware AI in operational processes, shifting from reactive planning to proactive, data-driven execution.

Collaboration within an Ecosystem and Network. Strategic partnerships often extend into broader ecosystems such as cross-industry collaborations and public-private alliances. These ecosystems enable shared access to data and infrastructure, set standards and prevent any one player from shouldering all the development burden.

One recent example is the "Billion Cells" project run by the Chan Zuckerberg Initiative. The project unites academic labs and tech firms to map human cells using AI to advance biomedical research.

In essence, building an AI-driven enterprise requires more than technology. It’s a journey of rethinking how business works and creating a strategic framework that integrates technology, industry expertise, a supportive company culture with the right talent on board, and strategic collaborations. Companies that commit to this transformation will not only stay ahead of the competition but will redefine the industries in which they work.