Synthetic intelligence (AI) has advanced from a buzzword to a boardroom precedence. However for many enterprises, scaling AI is proving more durable than anticipated. Not due to a scarcity of concepts or ambition, however due to one silent saboteur: unready information infrastructure.
In conversations with clients throughout industries, I see a sample repeat itself. AI pilots work fantastically in isolation. However in terms of deploying them at enterprise scale, the roadblocks emerge: poor information high quality, fragmented storage, inconsistent governance, and brittle pipelines that buckle below the strain of AI workloads.
Our current Omdia analysis, The State of Fashionable Information Platforms, confirms this. Whereas 82% of organisations have both carried out or are implementing open-standards information platforms, a majority nonetheless battle with accessing and integrating information throughout cloud, edge, and legacy programs. Extra alarmingly, solely 30% have AI-augmented workflows in manufacturing—regardless of AI being a strategic precedence.
Scaling AI wants greater than GPUs
You may now not deal with information as a back-end IT concern. It’s the strategic basis that determines whether or not your AI efforts scale or stall.
AI at scale calls for:
- Unified entry to structured, semi-structured, and unstructured information
- Trusted, ruled information pipelines that remove bias and danger
- Excessive-performance structure that helps real-time inference
- MLOps and DataOps frameworks that allow experimentation and agility
And most significantly, it calls for a mindset shift: from constructing one-off AI use instances to constructing AI-ready information infrastructure that may help steady, organisation-wide intelligence.
What’s breaking right now’s AI ambitions?
In our newest IDC highlight report on AI-ready information, we discovered that 20% of AI tasks in Asia-Pacific fail on account of data-related challenges. That features information belief points, poor lineage, inconsistent entry controls, and outdated integration strategies.
Clients we communicate to floor three recurring issues:
- Legacy information estates that weren’t constructed for AI workloads or vector codecs
- Siloed groups and toolchains that result in redundancy and rework
- Governance gaps that improve regulatory danger and kill AI velocity
The consequence? Slower time to perception. Increased prices. And a rising disconnect between AI ambition and AI execution.
The GenAI shift: Extra information, extra issues?
Generative AI (GenAI) brings a brand new layer of complexity. Not like conventional AI, GenAI fashions demand huge, high-quality, contextual information, and compute programs that may help RAG (retrieval-augmented era), embedding shops, and streaming pipelines.
Most enterprises aren’t prepared. Why? As a result of they’re nonetheless wrestling with foundational points: the place their information lives, the way it strikes, who governs it, and the way it connects to the AI layer.
That is the place the AI-ready information worth chain turns into not simply necessary, however foundational. As outlined in our newest IDC report, the worth chain spans each stage of the info lifecycle—from strategic acquisition and cleaning, to contextual enrichment, to mannequin coaching, deployment, and steady suggestions loops. It’s not nearly shifting information—it’s about activating it with belief, construction, and governance inbuilt.
This worth chain additionally encompasses supporting actions like information engineering, information management airplane governance, metadata administration, and domain-specific annotation, which guarantee AI fashions are skilled on related, high-quality, and unbiased datasets. It brings collectively various roles throughout the enterprise: CISOs guaranteeing information safety, CDOs aligning information with enterprise priorities, and information scientists tuning AI fashions for contextual outcomes.
With out this spine, GenAI turns into an costly experiment. With it, enterprises can scale AI with management, confidence, and measurable worth.
What main enterprises are doing in a different way
Essentially the most profitable organisations we work with are doing 5 issues proper.
- Consolidating platforms to cut back fragmentation throughout cloud, edge, and on-prem
- Embedding governance by design: encryption, lineage, masking, consent, privateness
- Constructing for flexibility: open-source, containerised, multi-cloud deployments
- Operationalising AI pipelines with strong MLOps frameworks
- Partnering for scale slightly than constructing every thing in-house
As our Omdia analysis discovered, solely 12% of firms need to construct their very own platform. 52% choose working with trusted companions that deliver agility, compliance, and innovation collectively.
Platforms like our personal Vayu Information Platform embody this shift. Designed with AI workloads in thoughts, it brings collectively secure-by-design structure, cloud-to-edge flexibility, and lifecycle automation for information ingestion, governance, and AI operationalisation. It’s this sort of architectural readiness that’s enabling our clients to maneuver from remoted pilots to scaled, production-grade AI.
In case your information is siloed, your pipelines are guide, and your governance is patchy, your infrastructure isn’t prepared for AI at scale.
The excellent news is that you just don’t want to start out from scratch. You simply want to start out with intent: Reimagine your information structure. Spend money on AI-ready platforms that unify information and speed up intelligence. Promote a tradition the place information isn’t simply collected—it’s activated.
Click on Right here for the report “ The State of Fashionable Information Platforms”.
