Prepare for Obstacles and Detours Yet Know The View Is Worth The Climb
It may seem that all roads lead to AI. But a new technology is not a destination, it’s the vehicle to get there. After years of experimentation, businesses now face the challenge of scaling AI from isolated proofs of concept to enterprise-wide impact. This shift is complex, with obstacles such as misalignment with business goals, poor coordination, and data quality issues. However, experienced AI and data leaders have paved the way, identifying best practices to navigate these challenges and achieve sustainable AI adoption.
Mapping The Journey To Effective AI
Instead of focusing on obstacles, early adopters of AI have embraced the process—evangelism, experimentation, operationalisation, expansion, and transformation—building both technical and cultural foundations. This includes governance, fostering cultural shifts, and expanding AI across organisations.
The journey begins with leaders raising awareness and educating teams on AI’s potential at all levels, from the shop floor to the boardroom. Every role interacts with AI differently—some collect data unknowingly, while others rely on AI-driven insights. For instance, sales teams at a large European wine and spirits producer use AI to guide customer interactions, such as introducing new cocktails or negotiating product placements. AI literacy programmes must cater to all audiences, a requirement now reinforced by the EU AI Act.
As interest grows, organisations must enable structured experimentation. Rather than a free-for-all, AI leaders create controlled environments like sandboxes or hackathons to generate use cases. Not all ideas will succeed, 80% of AI projects don’t make it to production, but this isn’t failure; it’s part of the maturation process. A clear prioritisation strategy helps focus on projects aligned with business goals, balancing cost and complexity while justifying ROI.
Operationalising AI requires alignment between technology and business, resource coordination, and standardised policies. The infrastructure built at this stage supports future projects, ensuring transparency, accountability, and compliance. The EU AI Act mandates governance, but many leaders see regulation as a driver of collaboration. Shared AI models and pooled data enhance accuracy and efficiency rather than stifling innovation. AI success lies in preparation, strategic focus, and collective effort.
The Long Haul
For AI to scale, organisations must build strong foundations in people, processes, and technology. Success lies in making AI tools and data accessible across departments, enhancing skills, and fostering collaboration. With the right governance, AI becomes a shared resource rather than a siloed tool.
Many companies then scale their responsible AI practices through cross-departmental collaboration. AI councils bring together stakeholders from different business units, along with representation from data and IT teams to promote coordination and continuous learning. In addition to improving governance and literacy, these mechanisms facilitate model and data sharing, which increases returns on AI initiatives.
AI transformation is an ongoing journey. When data and AI are embedded into an organisation’s DNA, they drive decision-making and innovation. Continuous education, collaboration, and best practice sharing improve data quality, efficiency, and business value—while fostering cultural change.
Map in Hand, Now Prep The Journey
Transformation to effective AI and through effective AI is a journey. And, that journey requires deliberate planning. By applying the best practices of the intrepid pioneers, organisations can avoid some of the obstacles along the way. Here are some tips:
Pack what you’ll need (hint: it’s data): It’s important to ensure access to more of your data. Make sure you build the foundation you need for a steady supply of quality data, and then break down internal silos to improve access. Transform previously inaccessible data to enable use. Imagine being an electric car owner and starting the journey with your battery only 10% charged. IDC estimates that 90% of data in the enterprise today is unstructured.
Plan the route but embrace the unexpected: The prioritisation process sets an itinerary for the journey, but interesting detours might be worth the investment. Ideation and experimentation keep you open to pleasant surprises along the way. There will be bumps along the way but planning will minimise the impact.
Understand the rules of the road: Ensuring that your data is well governed. Check under the hood to make sure it’s all in good working order. But also keep in mind the specific path and the destination goals. Keep asking, “Where are we going?” — or in this case, “What is the problem we’re trying to solve? How do we define success? How do we measure success? Did we make progress?” Those common guardrails need to be applied here, just as with other technology journeys. This is likely not the first you’ve undertaken.
Bring a companion along for the ride: A road trip is certainly more fun if shared with others. Similarly, the AI journey should not be a solo pursuit. Enlisting partners, customers and data partners in your AI ecosystem will help deliver richer insights and mitigate risks of hallucination and bias.
Learn what you’ll see along the way: Knowing what you, your colleagues and your peers are getting yourselves into helps smooth the ride. Start evangelising early. Then maintain continuous education across all roles. Develop a comprehensive communications plan — a travel log — to share where you’ve been and what you learned.
Keep a scrapbook to remember the highlights (and learn from the obstacles): Document the journey by capturing the details from which data was used, to the models employed, the challenges faced along the way and the outcomes achieved. Just like a travel log, success stories encourage others to reuse data models and increase the ROI. When done correctly, you’ll be planning your next trip before you finish the first. It is an ongoing journey.
When approached with a clear roadmap and a commitment to data excellence, AI evolves into a transformative force that drives sustainable growth and innovation across the organisation. AI and data leaders have put the foundations in place for organisations to succeed and it is high time business leaders follow their lead.
About the Author
Jennifer Belissent is Principal Data Strategist at Snowflake. Snowflake delivers the AI Data Cloud — a global network where thousands of organizations mobilize data with near-unlimited scale, concurrency, and performance. Inside the AI Data Cloud, organizations unite their siloed data, easily discover and securely share governed data, and execute diverse analytic workloads. Wherever data or users live, Snowflake delivers a single and seamless experience across multiple public clouds. Snowflake’s platform is the engine that powers and provides access to the AI Data Cloud, creating a solution for data warehousing, data lakes, data engineering, data science, data application development, and data sharing. Join Snowflake customers, partners, and data providers already taking their businesses to new frontiers in the AI Data Cloud.
