Understanding the basics of data is crucial for the success of any AI initiative. Without a solid foundation in data fundamentals, organizations may face challenges in implementing AI effectively. Data quality, availability, and relevance are key components that must be considered before embarking on an AI journey. By prioritizing data fundamentals, organizations can ensure that their AI projects are built on a strong and reliable data infrastructure. Investing in data literacy and governance can help organizations avoid common pitfalls and maximize the potential of AI technologies.
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Just months into 2025, and the global race to innovate around AI has already delivered a slate of major developments. Most recently, the launch of DeepSeek in China set off discussions about the downward trend on the costs of AI for businesses. This was just weeks after the UK government laid out its plans to make the country an AI superpower. We’re seeing a collision of AI policy and business response, and the UK government’s promised infrastructure improvements will create a newfound urgency to create or implement solutions that can take advantage. But are businesses ready?
While organizations may already be using AI in different capacities to boost productivity and make better decisions, for those gains to improve in line with the technology’s rapid development, they need to consider one key factor: the caliber of their data.
Laying the foundations for successful GenAI adoption, the Cofounder and CPO of Revenue AI platform Gong outlines the importance of a robust data foundation in realizing the promises of AI for businesses.
There are three pillars that underpin strong data foundations - quantity, quality, and context - which leaders across business functions will need to understand to reap the benefits AI promises for their teams. As the pace of innovation quickens, the sooner these principles can be applied, the earlier companies can start extracting value from their AI adoption journeys.
Many data stores rely heavily on manually entered data, which opens the door to human error and inconsistencies. These existing data gaps cannot be ignored any longer, or organizations risk letting them continue to widen, putting them further and further behind the pack as others adopt AI. Businesses need to be bridging the data gap today, adopting tools and processes that enable automated data capture across various touchpoints.
Having lots of data means little if it isn’t objective and trustworthy, and this is where human error or unintentional bias can prove a pitfall. Organizations will only be able to take advantage of the rapid pace of AI innovation if they adopt automated data capture to minimize manual inputs.
AI only becomes truly powerful when it can merge lots of high-quality data with the specific context it is being used for. A work landscape where processes are AI-enhanced across functions cannot be achieved if organizations don’t map their discrete data to the relevant business context.
No amount of government support will change the fundamental principle that in order to deliver accurate and actionable outputs, enterprise-grade AI solutions need to have the right data feeding into them. An AI strategy built on strong data foundations lets organizations tap into deeper, more relevant insights with unmatched speed in the short-term, while setting the stage to take advantage of new advancements as they emerge from different parts of the world.
There’s no shortcut to success with AI. Anything built on shaky foundations is at risk of underdelivering from the start. Organizations that get it right will be able to apply AI to existing operations more effectively and give themselves a competitive edge in capitalizing on the global race to build out the necessary infrastructure.
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