Successful AI adoption begins with identifying high-impact business challenges and opportunities:
– What are the strategic priorities?
– What measurable outcomes would make a difference?
Once you’ve identified a valuable use case, the next step is determining what data is required to deliver results.
Too often, organisations see poor data quality as a roadblock rather than an opportunity. The key question is: What is the ROI of this use case? If the potential value outweighs the cost of fixing or sourcing data, the investment is justified.
A compelling example comes from JPMorgan Chase. The bank implemented the Contract Intelligence (COiN) platform to review commercial credit agreements. Using AI, COiN processes 12,000 documents in seconds—a task that previously took 360,000 hours of legal work annually.
This success wasn’t accidental. It required significant investment in preparing and structuring the data to ensure accuracy and usability. The result? Dramatic cost reductions, fewer errors, and improved compliance—a clear ROI that far outweighed the initial data investment.
The takeaway is clear: data challenges shouldn’t stop you from pursuing high-value AI opportunities. Instead, use them as a reason to invest in capabilities that unlock both immediate ROI and future potential.
If you’re holding back on AI because of data concerns, it’s time to reframe the conversation. Start with the business value, invest in the data required, and let AI drive measurable outcomes for your organisation.
Are data challenges holding your business back? Let’s explore how to turn those challenges into strategic opportunities.
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