IBM Think 2025: Data Becomes the Directive – IBM’s GenAI Lakehouse Reframes Enterprise Data Readiness

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In 2025, enterprise AI isn’t struggling because of a lack of ambition — it’s suffocating under a glut of unusable data. While the narrative around generative AI has primarily centered on model innovation, enterprise leaders are now confronting a hard truth: models don’t perform magic on broken, siloed, or brittle data foundations. They amplify whatever they’re fed — and that means disjointed datasets, outdated governance rules, and fragmented pipelines turn AI dreams into hallucination-laced risks.

At IBM Think 2025, this tension took center stage with the launch of the IBM GenAI Lakehouse — a strategic platform built not just to warehouse data, but to prepare it, govern it, and serve it in real time for generative AI consumption. Unlike traditional lakehouse architectures that treat structured and unstructured data as parallel universes, IBM’s GenAI Lakehouse unifies both domains into a single operational surface. This shifts the narrative from “data storage” to “data readiness” — delivering a fabric where insights aren’t just stored, they’re sculpted for use across IBM’s existing enterprise data stack.

This is not a minor announcement — it’s IBM’s most consequential move yet in bridging the widening chasm between AI capability and enterprise implementation. As enterprises face rising expectations around AI governance, real-time decision-making, and compliance, the bottleneck is no longer talent or tooling — it’s the architecture of data operations itself.

According to Greyhound CIO Pulse 2025, 68% of Global CIOs say their GenAI deployments have stalled not because of model immaturity, but due to “inconsistent, ungoverned, or unprepped” data inputs. Another 59% say they’re already running dual data stacks — one for transactional needs and another for analytics — and are now paying the price in operational friction and budget duplication.

The GenAI Lakehouse answers this with what IBM calls a hybrid by design infrastructure. It doesn’t force enterprises to re-platform or adopt a single cloud; instead, it embraces multi-cloud, multi-format data estates and provides a unified layer of transformation and observability. Whether deployed on IBM Cloud, Azure, or on-prem, the Lakehouse stack meets data where it lives — and prepares it for where AI is going.

In one Greyhound Fieldnote from a large manufacturing enterprise, the CIO shared a frustration familiar to many: “We’ve spent millions building analytics pipelines over the past decade, and now we’re being told to redo everything for GenAI. IBM’s message — that we don’t have to start from scratch — is not only welcome, it’s the first time we’ve heard it from a vendor we trust.”

At Greyhound Research, we believe the GenAI Lakehouse represents a pivotal step in making generative AI real for the enterprise, not in proof-of-concept pilots, but in production-grade outcomes that are observable, governed, and scalable. This isn’t just IBM delivering a new product. It’s IBM redefining the substrate of enterprise intelligence.

Data Has Become the Hard Problem in Enterprise AI. IBM Just Made It a First-Class Citizen.

While much of the AI conversation in 2024 was dominated by model-centric headlines — from foundation model size wars to multimodal hype — IBM has taken a decidedly contrarian stance in 2025: it’s not the model that’s broken. It’s the data plumbing beneath it.

The launch of the GenAI Lakehouse reframes IBM’s AI strategy from model horsepower to data stewardship. It marks a decisive elevation of data architecture to boardroom priority and signals a bet that real enterprise value will come not from bigger models but better pipelines.

In Greyhound Research’s conversations with CIOs, a recurring frustration has emerged: AI is being asked to do backflips on infrastructure that wasn’t built to crawl. Most enterprise data lakes — hastily assembled over the past decade — are bloated, brittle, and siloed. They were never designed to support high-context, low-latency AI use cases that demand real-time ingest, dynamic governance, and cross-format agility.

The GenAI Lakehouse addresses this with a modular, governable approach. Rather than introduce speculative tooling, IBM is building on proven foundations — extending its data stack through the newly released DB2 v12.1.2.0, the launch of the DB2 Intelligence Center, and its expansion of DB2 Warehouse SaaS to Azure. These updates reflect a pivot from fragmented data operations to observable, policy-aware, and hybrid-compatible data readiness — the true prerequisite for GenAI at scale.

The message is clear: governance, lineage, and compliance must be embedded at the point of query, patched in after the fact. In one Greyhound Fieldnote, a Global insurance firm described its failed attempt to fine-tune a large language model using document data scattered across disconnected SharePoint instances. “The model wasn’t the problem,” their data lead admitted. “It was the total absence of schema, context, and lineage. We gave it fog and asked for insight.”

IBM’s response is to make those fundamentals observable and explainable at scale. With the DB2 family now upgraded to support workload introspection, AI-aware telemetry, and hybrid deployment, the GenAI Lakehouse becomes more than a storage framework — it becomes an execution environment for trusted data operations.

At Greyhound Research, we view this as a major inflection point in IBM’s enterprise AI playbook. The company is no longer framing itself as just a model provider or an infrastructure vendor — it’s positioning as the data operations layer for AI-native enterprises.

And CIOs are listening. According to Greyhound CIO Pulse 2025, 64% of surveyed leaders now say their biggest 2025 priority is “transforming the readiness, observability, and compliance of enterprise data estates” — up from just 37% in 2023.

This is the moment IBM has been waiting for.

CIOs Are Done Stockpiling Data. They Want It Activated, Audited, and AI-Ready.

Across closed-door sessions at IBM Think 2025, a distinct pattern emerged in executive sentiment — one no longer shaped by AI ambition, but by AI exhaustion. Many CIOs who had enthusiastically funded GenAI pilots through 2023 and 2024 are now asking tougher follow-ups: Where’s the business value? Why is data prep still 80% of the work? How do we govern this at scale without breaking the bank — or the law?

For this audience, IBM’s GenAI Lakehouse landed with resonance, not because it promised magic, but because it acknowledged the mess. The roadmap wasn’t framed in hyperbole. It was rooted in reality: fragmented data, decentralised teams, unpredictable governance, and a widening gap between where data lives and where insights are needed.

In one Greyhound Fieldnote, the CTO of a large telco summarised the fatigue this way: “We’ve got four clouds, three data warehouses, and a data lake that’s more like a swamp. IBM’s pitch wasn’t, ‘Replace it all.’ It was, ‘We’ll help you make it usable.’ That’s what makes this real.”

In another Fieldnote, a CIO at a U.S.-based healthcare firm explained how their AI compliance review had flagged over 60% of training datasets as “lacking explainability metadata.” Their team had invested heavily in LLM tooling, only to be held back by legacy ETL and poor data classification. “We needed lineage, observability, and governance in the same place our data lives.”

That’s the gap IBM is attempting to fill, and the market is taking notice. According to Greyhound CIO Pulse 2025, 72% of enterprise tech leaders say their current AI bottleneck lies in “data readiness and real-time policy enforcement,” not model accuracy or inferencing speed.

IBM’s hybrid deployment commitment also struck a chord. In one Greyhound Fieldnote from a Fortune 100 bank, the CISO put it bluntly: “We’re fine with SaaS — but not at the cost of auditability. Our regulators don’t want to see APIs — they want to see trails.” The GenAI Lakehouse, which runs across IBM Cloud, Azure, and on-prem, was praised for offering governance without forfeiting deployment choice.

Equally resonant was IBM’s decision to extend, not abandon, its DB2 base. With the launch of DB2 v12.1.2.0 and the DB2 Intelligence Center, IBM didn’t posture with product theatre. It offered architectural continuity. As one CIO from a government ministry told us, “Everyone wants to talk about copilots and assistants. IBM is talking about metadata strategy. That’s the conversation I need to have.”

At Greyhound Research, we interpret these reactions as more than validation — they are a signal that the market is entering a new AI maturity phase. One where data governance is not an operational afterthought, but the core strategy for AI deployment.

Technology Building Blocks – A Lakehouse Anchored by DB2 and Built for Hybrid Realities

IBM’s latest update to DB2 isn’t a surface-level patch. Version 12.1.2.0 delivers AI-aware query management, improved workload introspection, and telemetry built for compliance — all of which now serve as core infrastructure for the Lakehouse. These enhancements are not about speed alone; they are about explainability, enabling enterprises to trace not only data movement but query impact and policy adherence in real time.

In a world where regulatory expectations demand more than black-box acceleration, IBM is equipping data teams to answer the following questions: What was queried? By whom? Under what governance rule?

DB2 Intelligence Center – Observability as Architecture

In parallel, IBM launched the DB2 Intelligence Center — a control plane designed to help DBAs, architects, and compliance leads visualize data health, usage, and performance patterns across environments. This isn’t a traditional admin console. It’s a strategic observability layer for a world where AI outputs are only as defensible as the data flows that power them.

With built-in alerting, historical trends, and anomaly detection, the Intelligence Center enables teams to shift from reactive tuning to proactive governance, surfacing the exact signals regulators and audit committees increasingly expect: Has the data been touched? Altered? Queried under abnormal load?

DB2 Warehouse SaaS on Azure – Hybrid, Finally Made Operational

IBM also announced the expansion of DB2 Warehouse SaaS to Microsoft Azure, enabling organizations to deploy managed DB2 services within their preferred cloud environments. This is not just about compatibility — it’s about eliminating blockers to operational rollout.

For many enterprises, particularly in regulated sectors, Azure is already a governance default due to compliance mandates, geography, or procurement precedent. IBM’s move lets these firms run DB2 workloads where their data governance already lives, not where the vendor strategy dictates.

The bring-your-own-cloud (BYOC) model reflects a mature understanding: multi-cloud isn’t a trend — it’s a survival strategy. As one global bank CIO noted in a Greyhound Fieldnote, “We’re not just hybrid for resilience — we’re hybrid because every business unit has a different compliance perimeter.”

At Greyhound Research, we believe the GenAI Lakehouse isn’t aiming to dazzle with disruption. It’s designed to anchor — to stabilize the enterprise data layer before GenAI ambitions spin out of control. And in an industry obsessed with newness, IBM’s bet on continuity, observability, and proven platforms may well prove more radical than another AI wrapper.

Enterprise CXO Playbook – Ten Points to Ponder

1/ Your Data Lake Is Not Ready for GenAI Unless It’s Observable. AI models don’t hallucinate because of hardware — they fail because upstream data is incomplete, stale, or undocumented. With the DB2 Intelligence Center, IBM is signaling that observability is no longer a DBA feature — it’s a CXO accountability layer.

2/ Query Governance Starts with Your Database, Not Your Model. AI explainability needs to start at the query layer. With DB2 v12.1.2.0, IBM introduces policy-aware query handling and workload introspection. CXOs should be asking: Can we trace every AI-relevant query the same way we trace financial transactions?

3/ Hybrid Isn’t a Buzzword — It’s a Baseline for Compliance. IBM’s expansion of DB2 Warehouse SaaS to Azure respects enterprise cloud strategies instead of fighting them. If your data stack doesn’t match your regulatory perimeter, your GenAI plans are already non-compliant.

4/ Real-Time Insights Without Real-Time Lineage Is a Liability. Speed without traceability is no longer innovation — it’s exposure. CXOs must insist that any AI workload built on data from the Lakehouse includes embedded lineage reporting, audit trails, and policy access controls by default.

5/ Incremental AI Modernisation Beats Rip-and-Replace Every Time. IBM’s strategy to layer GenAI capabilities into DB2 — rather than demanding re-platforming — is a deliberate stand against unnecessary disruption. For CXOs, this is an opportunity to modernise without rewriting everything.

6/ Auditability Is No Longer Just a Legal Concern — It’s a Procurement Filter. Enterprises are learning the hard way that what can’t be audited can’t be defended. The DB2 Intelligence Center gives IT leaders the ability to show, not tell, their boards how data is governed.

7/ Standardise Data Governance Metrics Before You Standardise Models. Before deploying LLMs, enterprises need agreement on how to measure data quality, access controls, and usage anomalies. IBM’s investment in database-level telemetry provides the substrate for this conversation.

8/ Choose Vendors That Prioritise Existing Investments — Not Just Greenfield Visions. With the latest enhancements to DB2, IBM is sending a clear message: we’ll meet you where you are. Enterprises that are already DB2-heavy now have a GenAI runway, not a replacement roadmap.

9/ Operationalise Data Security with Platform-Level Visibility. Security posture doesn’t stop at encryption. What’s needed is the ability to detect drift, outlier usage, and suspicious query patterns. That’s what the Intelligence Center is built for.

10/ Treat Your Data Platform as a GenAI Enabler — Not Just a Passive Store. The GenAI Lakehouse isn’t just a way to unify structured and unstructured data. It’s IBM saying that AI starts not with prompts, but with pipelines. The work begins below the model layer — in how you design, document, and deliver enterprise data to your AI stack.

IBM’s Strategy Isn’t to Disrupt the Data Layer. It’s to Inherit It.

In a market full of shiny GenAI wrappers and cloud-first proclamations, IBM’s move with the GenAI Lakehouse feels like the most unusual strategy of all: stability. Rather than selling a breakaway vision, IBM is staking a claim on the foundation most enterprises already stand on and quietly making it GenAI-capable.

IBM is competing, but on terrain others have ceded: the governance-heavy, hybrid, brownfield data estates of the Fortune 2000. In this world, re-platforming is a dealbreaker. Auditability is not a premium feature — it’s the starting point.

Vendors like Databricks and Snowflake lead in analytics ecosystems. Google Cloud and AWS are expanding GenAI infrastructure. But IBM’s edge isn’t trend velocity, it’s alignment with operational, regulated, compliance-first enterprise realities that others abstract away.

The DB2 v12.1.2.0 update reinforces this. Instead of asking enterprises to retire legacy systems, IBM is helping them upgrade for AI alignment. The message is clear: govern what’s already running, don’t start over.

Likewise, the DB2 Intelligence Center positions IBM to win on observability — a function most GenAI architectures still bolt on late. While competitors debate model trust, IBM is solving for pre-inference control: drift detection, lineage enforcement, and anomaly flags — before a single token is generated.

Its expansion of DB2 Warehouse SaaS to Azure also proves IBM isn’t clinging to a proprietary stack. This isn’t cloud nationalism — it’s cloud realism. Enterprises committed to Azure no longer have to choose between control and compatibility.

Where others obsess over benchmark wins, IBM is playing a longer game — one where governance, policy alignment, and operational trust are the real battlegrounds. In a regulated world, the winner isn’t the fastest demo. It’s the platform that survives the audit.

This also reflects a broader recalibration. The Lakehouse doesn’t stand alone — it complements IBM’s stack across watsonx.governance (as referenced in the Think press release), automation via Concert, and secure infrastructure with LinuxONE. IBM isn’t building a product — it’s stitching an operational layer where all AI activity can be made observable, compliant, and production-ready.

In that sense, the Lakehouse isn’t a disruptor. It’s a market absorber. Its purpose is to coordinate what enterprises already have across data, infrastructure, and AI, and convert it into a system that can be measured, managed, and governed.

At Greyhound Research, we believe IBM’s greatest competitive edge isn’t its stack. It’s its stance. While others overpromise and underdeliver, IBM is meeting enterprises in the trenches, not pitching them from the clouds.

Why Governability, Not Novelty, Will Define the Next Phase of Enterprise AI

At Greyhound Research, we believe IBM’s GenAI Lakehouse announcement is a critical milestone — not because it unveils radically new technology, but because it formalises a posture the enterprise AI world urgently needs: one of continuity, control, and composability.

Where others chase GenAI novelty, IBM is solving for survivability. This isn’t a race to the biggest model or most abstract architecture. It’s a response to enterprise leaders asking: How do we make AI accountable? How do we scale it without losing visibility? And how do we do it without starting from zero?

The answers lie in what IBM has extended — not replaced. The updated DB2 v12.1.2.0 embeds policy-aware intelligence at the query layer. The DB2 Intelligence Center gives teams a real-time lens into data behaviour. And DB2 Warehouse SaaS on Azure reflects the geopolitical and operational realities of enterprise cloud commitments.

But more than components, it’s the architecture of intent that matters. IBM isn’t building for experimentation — it’s building for endurance. It’s betting that the winners in this market won’t be those who launch fastest, but those who govern best.

At a time when boards demand audit trails, regulators tighten enforcement, and cloud lock-in draws scrutiny, IBM’s Lakehouse offers a rare proposition: governability by design, not as an afterthought.

That said, the Lakehouse is not without risk, and CIOs must be clear-eyed. IBM is asking enterprises to deepen their commitment to DB2 at a time when many are struggling to rationalise sprawling data platforms. For organisations already locked into multiple data systems, this extension of DB2 may feel more like vendor reinforcement than ecosystem flexibility. While hybrid and multi-cloud support is welcome, the operational effort to federate metadata, align policy rules, and standardise lineage models across fractured estates will be far from frictionless.

Moreover, IBM’s pitch risks being read as “AI-by-infrastructure” — a view that could underplay the cultural and operational upheaval required to make GenAI real. In our field conversations, many CIOs acknowledge that while the technology stack is sound, scaling it will demand coordinated investment across architecture, skills, and governance, and not every enterprise is equally positioned. For those still consolidating data estates or maturing their AI oversight frameworks, adoption could lag strategy. This isn’t an IBM flaw alone — it’s a broader industry problem. But it does mean the Lakehouse, despite its pragmatic design, may still overshoot the operational runway of many enterprises in 2025.

At Greyhound Research, we believe the GenAI Lakehouse reflects a more mature, operationally grounded approach to enterprise AI — one that builds on what works, upgrades what’s needed, and governs what’s essential. But maturity must meet enterprise readiness head-on. For CIOs already investing in governance and data observability, this architecture offers a timely blueprint. For others still consolidating foundational capabilities, IBM’s success will depend on helping bridge the gap between promise and practical execution.

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