This article explores how enterprise AI will realistically unfold, the critical role of data management in its success, and why the AI infrastructure hype misses the mark for most organizations. It argues that simplicity and pragmatism—via SaaS and cloud reliance—will drive adoption, with data unification through consistency, correctness, and durability as the linchpin.
Also Read: Cutting Through Observability Clutter: How CIOs Can Escape the Cost Spiral
A Practical Path Forward
Enterprise AI adoption is poised to kick off with Software-as-a-Service (SaaS) providers and Independent Software Vendors (ISVs) harnessing third-party pre-trained models to deliver generative and agentic AI solutions. These solutions will span use cases like customer service automation, marketing content creation, intelligence analysis, and software development assistance, requiring seamless integration with both on-premises and cloud-based enterprise infrastructure.
To be effective, these AI agents must tap into structured and unstructured data sources, demanding rigorous consistency and up-to-date information for performance and compliance. Robust data management—complete with logging and transparency—will be non-negotiable to provide defensible evidence of AI decision-making. Meanwhile, the hype surrounding “AI-ready” data centers oversells what most enterprises need or can achieve. Facilities, power, cooling, and hardware constraints coupled with scarce availability of software developers with AI model development experience mean most will lean on public clouds or ISV/SaaS rather than build GPU-heavy infrastructure, a feat reserved for hyperscaler-like giants through at least 2025- 2026.
The AI Hype Meets Enterprise Reality
Artificial Intelligence (AI) has become the buzzword of the decade, with every enterprise, SaaS provider, and infrastructure vendor claiming a stake in its future. From chatbots to predictive analytics, AI promises to transform how businesses operate. Yet, after only a few years of tech evolution, the current AI landscape is arguably the most convoluted yet, flooded with marketing spin and lofty promises. Amid this noise, a clearer picture of enterprise AI adoption emerges – it will likely start with SaaS and ISVs leveraging pre-trained models for generative and agentic outcomes, integrating with existing systems and data. But success hinges on more than flashy algorithms—it demands reliable, consistent data and practical deployment strategies.
Meanwhile, vendors hawk “AI-ready” data centers as the holy grail, touting racks bristling with GPUs and next-gen cooling. For most enterprises, this is a fantasy. Power grids strain, cooling
systems lag, and GPU shortages favor hyperscalers. Through 2025 and 2026, only the biggest players will build such infrastructure at any material scale, leaving the rest to lean on cloud and ISV/SaaS solutions. This article unpacks this dual reality: how AI will take root via accessible tools, why data management is the backbone, and why the infrastructure arms race is a distraction for all but a few.
The Shape of Enterprise AI Adoption
SaaS and ISVs as the Entry Point
Enterprise AI won’t begin with bespoke, in-house models trained from scratch—it’ll piggyback on SaaS providers and ISVs using pre-trained, third-party large language models (LLMs). Companies like Salesforce, ServiceNow, Glean, or Palantir will embed generative AI (e.g., content creation) and agentic AI (e.g., task automation) into their platforms, delivering ready-to-use solutions. Picture a customer service chatbot that drafts responses, a marketing tool that generates campaigns, or a coding assistant that speeds up development—all powered by models from OpenAI, Anthropic, Gemini, or xAI, fine-tuned for enterprise needs.
Why SaaS? It’s the path of least resistance. Businesses currently utilize SaaS solutions for CRM, ERP, and additional functions—integrating these systems with AI represents a logical progression. Deployment is fast: plug into the cloud or on-premises servers, connect to existing data, and go. There’s no need to hire AI PhDs or build accelerated compute clusters. ISVs, meanwhile, bring specialized apps—think genomics analysis or seismic modeling—that can also evolve to tap these models, broadening the use case pool.
Agentic AI: From Sensing to Doing
The shift from perception AI (e.g., image recognition) to generative AI (e.g., text creation) and now agentic AI marks a leap in capability. Agentic AI doesn’t just analyze—it acts. A help desk bot doesn’t just log tickets; it resolves them. A sales analyst AI doesn’t just crunch numbers; it forecasts and suggests deals. This requires integration with enterprise systems—ERP databases, file shares, object stores—spanning cloud and on-premises environments. For example, a SaaS agent might pull customer data from a SQL database and call logs from an unstructured NAS to craft a personalized response, all in real-time.
Data: The Make-or-Break Factor
AI agents are only as good as their data. If a radiology AI uses an outdated MRI scan, it might miss a tumor or flag a ghost. If a sales AI works off stale forecasts, it misguides strategy. Enterprises need structured data (e.g., CRM tables) and unstructured data (e.g., PDFs, seismic files) to be current, consistent, and accessible. This isn’t just about performance—it’s about compliance. Regulated industries like healthcare or finance demand audit trails proving why an
AI made a decision. A customer denied a loan by agentic AI needs to know it wasn’t biased or based on bad data—enterprises must show their work.
Also Read: Power Stored, Power Secured: Why Cybersecurity Is Central to Battery Storage
The Data Management Imperative
Consistency and Correctness
Agentic AI thrives on trust, and trust demands data integrity. Take a geophysical firm using AI to analyze seismic data for oil exploration. If the AI pulls inconsistent datasets—say, one rig’s readings lag by a week—it might misjudge a reservoir’s size, costing millions. Enterprises need mechanisms like atomic updates (ensuring all parts of a dataset sync at once) and strict consistency, where distributed systems align instantly and never represent stale data, to keep data reliable. For unstructured data—think petabytes of seismic scans or customer emails— tools like Qumulo’s Cloud Data Fabric can maintain a global namespace, ensuring every AI agent sees the same, latest view, whether it’s querying from Houston, Ghawar, or an offshore rig in Brent Crude.
Logging and Provenance
Beyond correctness, enterprises need defensible decision-making. When an AI denies a claim or flags a drill site, regulators or auditors might ask, “Why?” Robust logging tracks what data the AI consumed, how it transformed it (e.g., feature extraction), and what it produced (e.g., a risk score). This provenance—think of it as a digital paper trail—must be durable and stored for years in some cases. A SaaS provider might log queries in a cloud database, but unstructured inputs like raw scans need a file system that can handle petabyte-to-exabyte scale durability and offer the ability to run on any hardware system (for supply chain risk mitigation) and any cloud.
Volume and Velocity
AI agents don’t sip data—they gulp it. A single NovaSeq X genomics run produces 8-26 TB; training a model like Grok 3 might ingest 5-15 PB; however, these pale in comparison to the exabytes we are seeing in national intelligence and signals processing or the 300-500 petabytes in reservoir simulation and media and entertainment. Agentic AI in enterprises will consume structured datasets (gigabytes of CRM data) and unstructured pools (terabytes-to-petabytes of logs or media), transforming them into outputs like reports or predictions. This data firehose demands scalable storage—SaaS can lean on cloud elasticity, but on-premises systems need flexibility. Qumulo’s ability to run on any hardware and scale to the cloud bridges this gap, while normalizing the total cost of ownership for both cloud and on-premises makes it a fit for AI’s voracious data appetite.
The “AI-Ready” Infrastructure Myth
The Hyperscaler Divide
Vendors love to pitch “AI-ready” data centers—racks humming with NVIDIA H100 GPUs, liquid cooling, and requiring 150-200 kW power per rack with liquid cooling. Hyperscalers like AWS, Google, and Microsoft are building these, planning for AI’s compute boom. But the average enterprise data center? It’s stuck at 8-10 kW/rack, and rarely above 30 kW1. Retrofitting for GPU clusters means new power lines (grids are maxed), liquid cooling (not yet commonly deployed), and redundant generators — turbine orders booked today are being scheduled for delivery in 2032. Most enterprises can’t tolerate an AI/data center with no redundant power for business-critical workloads, especially if they depend on the AI system for critical business processes.
GPU Scarcity and Tariffs
Then there’s hardware. GPUs are the lifeblood of advanced AI training, but hyperscalers snap them up—NVIDIA’s H100s are gold dust. Tariffs on Chinese components (25% + under U.S. policy) jack up costs for enterprises already squeezed. A single rack of H100s might cost $1-2 million, excluding power and cooling upgrades. Only giants like Fortune 100 energy sector leaders or major financials, with deep pockets and global facilities, can play this game through 2025-2026, and their setups will mimic hyperscalers, not traditional IT shops.
The Practical Alternative
For 90% of enterprises, building AI infrastructure is a pipe dream. Instead, they’ll:
- Rent/Lease Public Cloud: Hyperscalers offer GPUs on tap—train a model, then shut it down. AWS’s P5 instances or Azure’s NDv5 series deliver H100-grade power without owning the metal. Data flows in via high-speed links, tapping on-prem files, and leveraging technologies like Qumulo’s cloud native filesystem to store the data cost-effectively in the cloud, or rapidly express it to the cloud when and where the business needs it with the Qumulo Cloud Data Fabric.
- Deploy ISV/SaaS: When providers like ServiceNow roll out an agentic AI module— deployed on-prem or cloud – they connect it to your SQL database and NAS, and let it run on standard x86 servers. No GPUs, no fuss. Accelerate its performance with technologies like Qumulo’s predictive caching to lower cloud costs and improve recursive read/write performance.
Why Most Enterprises Won’t Build Hyperscaler-Like Infrastructure
Capital and Talent Barriers
Hyperscaler-grade AI infrastructure isn’t just about hardware—it’s capital and expertise. A new data center might cost $500 million, plus $50-100 million yearly to run at 200 kW/rack. Cooling alone—say, immersion systems—adds millions. Talent’s another choke point: enterprises struggle to hire AI architects or power engineers when hyperscalers poach them with life-changing salaries. Only the top tier can stomach this and have the scale and expertise to operate these systems at the utilization rates necessary to own versus rent the infrastructure.
Use Case Mismatch
Most enterprises also don’t need in-house GPU clusters. Training a bespoke LLM is rare—SaaS uses pre-trained models fine-tuned on enterprise data, a task hyperscalers already handle. Agentic AI for customer support or sales doesn’t demand real-time inference at hyperscaler scale; it runs on CPUs or modest cloud instances. Seismic analysis may justify the use of GPUs for an exploratory geophysics firm, but even then, cloud leasing often outperforms building based on runtime percentages.
Risk vs. Reward
Retrofitting risks outages—rewire a data center, and legacy apps might crash. New builds lock in capital for years, relying on AI trends that might shift and going against Moore’s Law, where every two years we will see a doubling of GPU and CPU performance. SaaS or cloud sidesteps this with “pay monthly”, pivot if needed. For most, the reward of “owning” AI infra doesn’t outweigh the risk.
Data Management’s Role in the Real Enterprise AI Revolution
Bridging Data to AI
Quality data management fits where AI adoption actually happens in a SaaS and cloud construct. With software-defined storage that runs on any hardware, perfect for legacy on-prem systems like the Cloud Data Fabric, can scale to AWS, Azure, Google, or Oracle, linking unstructured data (e.g., seismic scans, customer files) to agentic AI. Key to capabilities like this is a predictive caching architecture that slashes latency—think millisecond access for a sales AI pulling forecasts, crucial when petabytes flow daily and real-time responsiveness empowers agentic AI to meet customer expectations.
Ensuring Trust
Consistency delivered by a global namespace coherently extends data across sites—Houston HQ can now view the same rig logs as an offshore edge, which is vital for AI accuracy. Atomic updates prevent version skew (e.g., no stale MRIs), and durable storage logs every transformation, proving an AI’s logic to auditors and risk managers. For regulated sectors, correctness is gold.
Scaling with Simplicity
No enterprise wants another vendor silo. Qumulo integrates with SaaS APIs, traditional file protocols, and object stores to feed petabyte-scale data without bespoke headaches. It’s not about GPUs—it’s about making existing infrastructure AI-ready, now.
Conclusion: Pragmatism Over Hype
Enterprise AI won’t storm in with futuristic data centers—it’ll creep in via SaaS and cloud, powered by pre-trained models and practical deployments. Agentic AI will simplify jobs, from support to analytics, but only if it drinks from a clean, current data well. Qumulo and enterprise infrastructure peers will win by mastering this, not chasing hyperscaler dreams. The “AI-ready” hype is loud, but the reality is quieter: most enterprises can’t—and won’t—build GPU fortresses by 2026. They’ll lease or license, leaning on vendors who solve data grids, not power grids. In this shift, reliable data management isn’t a supporting actor—it’s the star.
