Organisations are racing to deploy tools and announce initiatives. While the pressure to demonstrate progress is intense, the window for experimentation is closing. This year, expect markets to demand bottom-line results to justify spending. There’s lots of action, but not so much traction.
According to the Boston Consulting Group, only one-quarter of executives say their companies have created significant value from their AI initiatives.1
The organisations pulling ahead aren’t necessarily the ones deploying fastest. They’re the ones who did the work to get their data right first.
The old rules still apply to AI
“This truth is nothing new, but AI amplifies both sides of it. Quality data can rapidly accelerate results, but with fragmented or unsecured data, AI accelerates risks too.”
Generic AI models trained on public data will give you generic results – by definition, they’re designed to provide predictable, middle-of-the-road answers.
The competitive advantage comes from using your proprietary data and information, properly organised and secured.
Four ways to build AI-ready data foundations
1
Data quality and management
AI feeds on up-to-date information. That means automating data collection and validation where possible, establishing consistent formats and standards, and managing data lifecycles. Modern tools can monitor quality automatically and flag anomalies before they affect AI outputs.
2
Governance and compliance
Without robust governance, AI exposes organisations to significant risk. Role-based access controls, multi-factor authentication, usage policies and audit trails are cybersecurity fundamentals, but AI amplifies both the need for them and the consequences of gaps.
3
Integration and scalability
Data trapped in silos creates blind spots. AI tools need information to flow between systems, which means connecting platforms and breaking down barriers between datasets.
4
People and processes
Clear roles and responsibilities, ongoing training, collaboration between IT and business units and executive sponsorship all determine whether AI initiatives succeed or stall.
“Success with AI starts with understanding what you’re trying to achieve. Once you have clear business outcomes, you can build your data foundations to support them. Get this right, and you create a sustainable competitive advantage,” explains Himanshu Soni, Analytics & AI Practice Manager at Nexon.
In 2026, this means identifying specific use cases where AI can automate routine processes, freeing people to focus on higher-value work.
The hidden cost is trust
When people encounter inaccurate outputs, they lose confidence in the analysis. That erosion of trust can undermine decision-making and broader transformation efforts.
Gartner estimates poor data quality costs organisations an average of $12.9 million annually.2 While 73% of executives believe AI agents will give them a significant competitive advantage,3 data quality may stifle those plans before they start.
Building AI-ready foundations after a merger
When two major financial institutions merged, they needed to unite 11 separate data systems supporting 1,600 staff and half a million customers.
Nexon helped them unify their systems and create a digital foundation that enabled AI-powered customer service, automated workflows and real-time analytics across the merged organisation – in just months. The investment in standardising data and processes enabled them to focus on innovation.
For many organisations, the opportunity is to spend differently, not spend more.
Build on what you already have
If your organisation already runs on Microsoft tools such as Outlook, Teams, SharePoint and Dynamics 365, you have a foundation to build on. The ecosystem spans the data lifecycle from Azure for unified data and Fabric for analytics, to Purview for governance. Copilot pulls it all together.
As a Microsoft Solutions Partner with specialisations across cloud, security, data and AI, Nexon delivers end-to-end managed services that help organisations consolidate complexity and build capability for the future.
“With AI, you want to ensure you’ve got the data governance and data modelling in place,” says Josh Sampson, Solutions Architect at Nexon. “You’ve got the processes in place to effectively leverage AI for your organisation.”
See Elliot and Josh discuss how to build AI-ready foundations in our fireside chat.
The path forward starts with an honest assessment of current data foundations – where quality issues exist, where data is siloed, where governance gaps create risk. Many organisations discover that what they thought was a technology challenge is actually a data issue.
“The key is balancing your long-term vision for AI with quick wins that demonstrate value and executive buy-in. This builds momentum and support for larger transformation initiatives,” says Soni, Nexon’s Analytics & AI Practice Manager.
The AI tools will keep evolving. There will always be something newer and faster on the horizon. The organisations investing in foundations now will be positioned to take advantage.
Not sure where you stand? Take Nexon’s AI Readiness Assessment to quickly gauge your data maturity and identify where to focus first.
eBook: Data Foundations for AI Success
A practical guide to building the data quality, governance and integration capabilities that determine whether AI initiatives deliver value or stall at pilot stage
For a no-obligation discussion about our end-to-end managed services contact Nexon today.
Elliot Jurd is General Manager Cloud at Nexon Asia Pacific
Josh Sampson is Solutions Architect at Nexon Asia Pacific
References:
Boston Consulting Group: From Potential to Profit: Closing the AI Impact Gap, 2025
Gartner: Data Quality: Best Practices for Accurate Insights, 2025
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