GenAI entered the public consciousness in a big way in 2023. It’s made headlines, been talked about on chat shows—even my dad has logged on to try ChatGPT! So, unless you’ve been hiding under a rock for the last year or so, you know it’s here and it’s here to stay. The challenge now is how to make the most of it. How can you best harness your data and GenAI to meet your strategic goals?
It’s a question organizations around the world are struggling with. AI technology has moved so fast, and the possibilities around it are so vast, that it can be tough to know where to focus your efforts. Is GenAI an area where leaders are thinking big but failing to deliver? Research from MIT Sloan Management Review found that 40% of organizations making significant investments in AI do not report business gains from AI. This is worrying. We know the technology has huge potential, so what’s causing this gap between investment and success?
One of the biggest issues around GenAI projects is that some organizations are trying to jump to the end. They want the chatbots and the cool products, but any GenAI success is completely reliant on strong data foundations. Without widespread data literacy, high data quality, and a solid data infrastructure event the most powerful GenAI tool will fail.
Simply, data is essential to GenAI success, and it takes a lot of work to get good data. Here are three data lessons that you can’t afford to ignore if you want to make the most of GenAI technology.
1. The value of data lies in informing decisions
Automating and streamlining processes with AI can empower humans to make better decisions—helping to accelerate success. It should enhance their potential rather than replace their expertise. However, it’s easy to get caught up in operationalizing data without clearly defining its potential value in decision-making. You need to understand exactly how and where the data you’re collecting and analyzing can add value. Will it increase revenue, decrease costs, or strategically position your organization for growth? By bringing people together behind a key data purpose and tangible outcomes it becomes much easier to build momentum and trust around data and AI projects.
2. Getting value from data is an organizational effort
Extracting value from data and implementing AI tools successfully isn’t just about technical expertise. It’s about finding the right balance between people, processes, and technology. These three pillars must work in harmony if you want to really unleash your data potential and people are the most important part of the equation. Data isn’t created in a vacuum—it’s generated by and shaped by people. This means the path to AI success starts with creating a culture that values and understands data through increasing data literacy in every corner of your business. You can have the perfect data model. The most cutting-edge GenAI technology. But without that data literacy element, everything falls down. You end up with a situation where people are saying or thinking, “I don’t trust the data, I trust my gut” and that’s hard to fix.
3. Many organizations are failing to maximize data value
I’ve been part of many reorgs and I’ve watched lots of digital transformation efforts from the sidelines. Those that fail often follow a similar pattern; they aren’t providing the right access to data, they lack consistency in their approach, and there is a lack of trust. This often means that people look back on their efforts and conclude that the tools they implemented aren’t great or that they don’t have the right data inside their organization. The reality is that plugging in a tool or solution without doing any foundational or cultural transformation is a flawed approach. Becoming a data-driven organization is something that takes time, experimentation, and a holistic, joined-up approach. You’re probably not going to get everything right the first time you try it, but building a strong data foundation and focusing on key outcomes and cultural change will support long-term success. Agile development is key. This involves starting with manageable projects, learning from them, and evolving over time. It’s not about trying to achieve everything all at once but about embracing iterative and responsible improvements that move with your shifting goals.
As GenAI’s capabilities grow, strong data foundations and a connected data culture become non-negotiable. By focusing on these core data lessons and approaching GenAI with an open, agile mindset, organizations can bridge the gap between investment and results—turning potential into reality.