TechnologySynitiPrint articleMay 18, 2026 – 8.55amAustralian organisations are racing to use cloud platforms, automation and artificial intelligence. They want faster services, lower costs and better decisions.But many are learning a hard lesson, AI is only as good as the data behind it. And for many businesses, that data is not ready.Data quality is shaping AI outcomes as organisations confront scaling and governance challenges. iStockClean, trusted data has become the real advantage. Without it, AI does not create value, it creates risk.Data integrity means having information that is accurate, complete and consistent. Once a background task, it is now a core business requirement. If leaders cannot trust their data, they cannot trust the decisions AI makes on their behalf.James O’Donnell, head of GTM at Syniti, part of Capgemini, says the rush to adopt AI is exposing problems businesses have ignored for years.Advertisement“Your data reflects your business processes and how your organisation actually works,” O’Donnell says. “When the data is poor, the decisions are poor. And fixing those mistakes later is expensive.”When AI scales, data problems explodeMany organisations have lived for years with duplicated records, missing fields and outdated information. These issues often stay hidden when people review data manually.AI changes that.Automation works at speed. It does not stop to question whether data looks right.“AI proof-of-concepts often look amazing,” O’Donnell says. “The data used is usually a small, clean subset. Everything works perfectly.”The problem comes later.“When that same AI is rolled out across the whole business, it suddenly sees all the messy data too. That’s when things break.”The scaling trapTake procurement as an example.An AI system may find four vendor records that look similar. Two might be old. One might not exist anymore. Another might never have been approved.“The AI doesn’t know that.” O’Donnell says. “It treats them all vendors as active suppliers.”The result? Orders get placed four times over. Inventory piles up. Cash is locked into open orders. Finance teams scramble to unwind the damage.“Money starts going everywhere,” he says. “And it happens very fast.”The key issue is not that AI makes mistakes. It is that AI magnifies existing data problems.Data quality becomes a risk issueAs AI systems make decisions quicker and at larger scale, data quality is no longer an IT concern. It is a governance and risk issue.This shift is now recognised at a national level.The Australian government’s Data Governance Framework says data governance must sit at the foundation of AI adoption. Data needs to be accurate, secure and responsibly managed to maintain trust and deliver value.Poor data quality also explains why many digital services feel frustrating for users.“If your old systems were siloed and fragmented, and you never fixed the data, self-service just makes the problems visible,” O’Donnell says.“If processes didn’t work well when people handled them, why would they work when they’re automated?”Data is a business asset, not an IT problemOne of the biggest mistakes organisations make is treating data as a technical clean-up job.Data is a business asset. When ownership sits only with IT, transformation programs often fail to deliver.Syniti’s approach is what it calls “Data First Transformation”.That means starting with data early in any transformation – not leaving it until the end.“On major programs, data is often the longest pole in the tent,” O’Donnell says. “The sooner you assess it, standardise it and fix it, the less disruption you create later.”Moving from rules to active governanceStrong governance matters, but rules alone are not enough.O’Donnell says organisations need active data governance, where data is checked before it enters systems, not corrected after the damage is done.“Nothing should get into your systems unless it meets agreed standards,” he says. “That means aligning people, process and technology from the start.”This also requires a shared data language.In many organisations, even simple terms mean different things to different teams. Finance, sales and operations may all define a “customer” in different ways.Without agreement, consistency is impossible.“You can’t trust reports or AI outputs if everyone is working from a different rulebook,” O’Donnell says.The real test of AI readinessGood data shows its value in everyday operations.Processes run smoothly. Maintenance becomes proactive. Leaders trust the numbers they see. Decisions get easier.Bad data does the opposite. Inefficiency becomes normal. AI simply makes the mistakes happen faster.Syniti works with organisations that understand this difference.“Our role is to help businesses define what ‘good’ looks like,” O’Donnell says. “We help align data strategy to business goals and build the capability to improve data over time.”As AI becomes embedded across Australian enterprises, data capability will separate leaders from laggards. Those who treat clean, governed data as a long-term discipline will capture the upside.Those who don’t may find AI turning promise into chaos.For more information on Syniti’s Data-First Approach, please visit Syniti.Sponsored by SynitiThis content has been funded by an advertiser and written by the Nine commercial editorial team.SaveLog in or Subscribe to save articleShareCopy linkCopiedEmailLinkedInTwitterFacebookCopy linkCopiedShare via...Gift this articleSubscribe to gift this articleGift 5 articles to anyone you choose each month when you subscribe.Subscribe nowAlready a subscriber? LoginLicense articleFollow the topics, people and companies that matter to you.Find out moreRead MoreSynitiFetching latest articles