The race to harness AI is accelerating across almost every sector. In boardrooms around the world, AI is being hailed as the next great productivity revolution. It promises to redefine how businesses operate and compete, yet behind the hype lies a sobering statistic: around 70% of enterprise AI projects fail.
The problem isn’t the technology itself. It’s that most organisations are trying to launch rockets without first building the launchpad.
The mirage of progress
Every major enterprise wants to be seen as “doing AI.” Pilots are being spun up, teams are formed, and proof-of-concepts abound. On paper, it looks like innovation in motion. In practice, most of these initiatives are hamstrung by the fundamental flaw that their data foundations simply aren’t ready.
Years of market consolidation, M&A, digital transformation have left a trail of fragmented systems and incompatible data sources. Within a single company, information can sit trapped in dozens of silos: legacy databases, departmental servers, cloud platforms, and storage solutions that don’t speak to one another. There has been a shift in most markets to the cloud – and yet, public clouds can cost customers as much as $1m a year to store a single petabyte of data, and companies struggle with the complexity of extracting value from it, leaving these ‘big rocks’ dormant and waiting to be mined.
Add to that the explosion of unstructured data like web chats, customer calls, social media posts, emails, images, and videos, and the huge challenge of bringing order to data chaos is obvious.
AI depends on connected and consistent data. Without it, even the most advanced models will fail to deliver meaningful results. Too many organisations are discovering this only after investing heavily in AI programmes that never make it past the pilot stage.
Infrastructure is the new competitive edge
Real AI readiness starts long before the first model is trained. It begins with data orchestration, which is the ability to govern information seamlessly across the business. Companies that have built this capability are pulling ahead because they can experiment and deploy AI initiatives at scale with confidence.
Data infrastructure is no longer a back-office concern and it should be thought of as a strategic weapon. The ability to move and manage data fluidly across on-prem systems and multiple cloud providers in real-time determines whether an organisation can turn AI into a commercial advantage. Those that fail to modernise will find themselves outpaced by competitors who can.
Known as ‘data hydration’, businesses should aim to have ‘one view’ of all its information in accessible formats, regardless of the different systems, applications, or databases involved in the migration. This automated reorganisation of the data into the cloud ensures that files are not lost, corrupted, or misinterpreted, aiding decision-making and analysis while providing a disaster recovery solution.
Many C-suites underestimate the rigour required to make data AI-ready. Governance, data quality, standards and visibility are essential. Data must be treated as a core business asset, not a by-product of day-to-day operations. Leading enterprises are investing in technologies that automate the movement of data so that it’s always accessible without any disruption and zero risk of downtime. When these systems are in place, AI tools can finally be deployed at scale with data fully leveraged.
Setting up for success
There are countless highly-skilled experts out there who are about to embark on their company’s data migration journey. But what they don’t yet realise, is that manual approaches to migration require significant management overhead, which also takes time and increases the risk of human error.
Downtime disruption can cause millions of dollars of lost revenue opportunities, yet companies sometimes underestimate the risks and employ error-prone custom approaches.
The best chance of success, especially for smaller teams, is to explore an automated technology solution which can remove some of these risks and accelerate project timelines, significantly reducing operational costs. This also allows IT teams to focus on other things and not the migration itself. You could compare this to moving house without having to pause your life by using a superfast removals service, where there’s no need to even unplug the fridge.
Unlocking AI at scale
The organisations that will thrive as the AI adoption curve sharpens are those that have mastered their data infrastructure. Once the launchpad is built, innovation can accelerate exponentially. AI will not and should not replace people, but it will supercharge those who know how to use it. To realise its potential, enterprises must stop chasing short-term experiments and start building the solid data foundations that make real transformation possible. No matter how powerful the rocket, it won’t reach orbit if the launchpad isn’t ready.
