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Building an AI-first foundation
HPE’s approach to scaling AI across the enterprise
Many organizations are already seeing gains from AI. But most are incremental. The reason is simple: They’re applying AI to existing workflows instead of redesigning how work gets done around AI. That distinction — between a traditional approach and an AI-first approach — determines whether AI delivers marginal improvements or enterprise-wide transformation. HPE’s solution? A foundation built for hybrid environments, distributed data, and AI-driven operations at scale.
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Where AI breaks at scale
Most AI adoption challenges stem from the same root issue: AI is being layered onto systems and workflows that weren’t designed for it. In many cases, it’s still delivering value. But as organizations push beyond early wins, most hit a wall that limits how far AI can actually scale. These challenges don’t exist in isolation — they compound. As AI adoption accelerates, organizations that continue operating within the limits of their existing infrastructure, workflows, and culture will struggle to keep pace.
Infrastructure and environment complexity
Operational and governance gaps
Data and pipeline fragmentation
Organizational resistance to change
AI workloads create sustained demand across compute, storage, and networking — often across cloud, on-premises, and edge. As models grow and usage scales, many organizations struggle to maintain performance, control costs, and ensure consistency across increasingly distributed environments.
As AI systems scale, organizations struggle to track how data, models, and agents are used — and by whom. Governance must extend beyond data to include model behavior, access, and risk management across the full life cycle, especially as solutions move from pilot to production.
Enterprise data is distributed, constantly moving, and rarely structured for AI at scale. As more tools and systems are connected, pipelines become harder to manage, secure, and explain. This introduces latency, increases cost, and limits visibility across workflows.
Even when AI works, organizations don’t always change how work gets done. Without alignment across teams and leadership, AI remains underutilized, limiting its ability to drive meaningful transformation.
HPE commissioned a survey to understand how successfully businesses were adopting AI. The results showed a strong sense of confidence in both their progress and approach to AI. Closer examination, however, suggested an unsettling reality: Critical gaps in AI implementation and insight could jeopardize future business success. The common driver behind these gaps? A narrow, “traditional” approach to implementation. This latest research builds on similar findings from 2024, offering an updated perspective on the progress and stumbling blocks that have shaped the AI journey for organizations over the past year. Key findings include: 86% of the participating IT leaders reported confidence in their AI strategy, yet gaps in execution and insight persist. Only 22% of organizations have fully operationalized AI, whereas 41% are still transitioning from pilots to production. 98% reported measurable benefits from AI, but most gains remain incremental, focused on efficiency, speed, and decision-making. Data readiness remains uneven, with capabilities varying widely across key stages such as processing and recovery. Fewer than half of the responding organizations have mature data and governance practices, including real-time data access, shared models, and advanced analytics.
Architecting an AI advantage — by the numbers
Traditional approach Apply AI to existing processes
AI-first approach Redesign workflows around AI
At a certain point, every organization faces the same decision: Do you apply AI to your existing workflows, or do you redesign your workflows around AI? Those taking the first approach will unlock some value from AI but will ultimately fail to unlock their organization’s full potential. Only a true AI-first approach can harness AI at scale.
Two paths — and two very different outcomes
AI is embedded into end-to-end processes. Workflows are rearchitected from the ground up. Systems operate continuously, not episodically. Outcomes shift from efficiency gains to significantly higher impact, often several times that of incremental approaches.
100%
40%
AI accelerates individual tasks. Existing workflows remain largely unchanged. Tools are layered into current systems. Gains are meaningful but incremental.
HPE’s vision for operationalizing AI
AI-ready data foundations
AI-driven operations
Unified governance across the AI system
Flexible infrastructure for AI workloads
Simplified hybrid operations
AI-first culture and operating model
An AI-first approach isn’t about adding AI to existing systems. It requires redesigning how infrastructure, data, operations, and teams work together from the ground up. Traditional environments weren’t built for continuous, distributed, AI-driven workloads. As a result, organizations that don’t evolve their foundations struggle to move beyond incremental gains. HPE’s approach focuses on the core capabilities required to operate AI as a system — at scale, across the enterprise.
Support dynamic, compute-intensive workloads across hybrid environments. This enables organizations to scale AI without performance bottlenecks or unpredictable cost spikes.
Create consistency across distributed environments without increasing complexity. A unified approach reduces operational overhead and helps ensure reliability as AI expands across environments.
Ensure that data is accessible, curated, and usable across the enterprise. High-quality, well-governed data enables AI systems to deliver accurate, consistent, and trustworthy results.
Embed intelligence into how systems are managed and optimized. This improves efficiency and responsiveness, enabling organizations to manage increasingly complex environments at scale.
Extend governance beyond data to include models, agents, workflows, and risk. Comprehensive governance provides visibility and control, enabling organizations to monitor system behavior over time, not just at individual points, and helping them manage risk and meet regulatory requirements.
Shift how teams work, make decisions, and adopt AI across the business. Aligning people, processes, and technology ensures that AI is fully adopted and delivers meaningful business impact.
Why traditional approaches fall short — and what’s required to scale AI across the enterprise
Inside HPE’s own AI transformation
HPE’s own AI journey followed a familiar pattern. Early efforts focused on applying AI to existing processes — accelerating tasks, improving efficiency, and supporting teams with new tools. Those efforts delivered value, but they also exposed a ceiling. Optimizing individual steps improved performance without fundamentally changing outcomes. To move beyond those limitations, HPE shifted toward an AI-first approach, by rethinking workflows across the business 1
Finance & Strategy
Technical Teams
Marketing & Sales
Global Operations
In Finance & Strategy, this shift has accelerated planning cycles and improved the speed and quality of decision-making. AI tools now support earnings report preparation by anticipating analyst questions — matching actual questions roughly 80% of the time — and reducing the time required to prepare materials. At the same time, leaders can access real-time performance insights instead of relying on static, backward-looking reports. Together, these changes reduce time spent on manual preparation and increase confidence in high-stakes financial decisions.
In Global Operations, AI has streamlined processes across highly complex, interconnected systems. Conversational AI now resolves a significant share of routine service inquiries, contributing to roughly a one-third reduction in total case volume while expanding support availability to 24/7 without equivalent head count increases. Configuration workflows that once took several minutes can now be completed in under two, with sellers selecting AI-recommended options the vast majority of the time. AI-driven forecasting has also improved accuracy, helping optimize inventory, reduce shortages, and support stronger cash flow.
Within Marketing & Sales, AI has reduced friction across the lead-to-revenue process and improved both speed and effectiveness. AI has significantly cut research time per lead while increasing conversion rates for AI-assisted leads, compared to manual approaches. New sales development representatives reach productivity in a fraction of the previous ramp time, and overall team productivity has increased by roughly 20%. These gains reflect a shift from manual data gathering to more intelligent, insight-driven engagement.
Across technical teams, AI has improved development workflows and increased automation in day-to-day work. Engineers handling large volumes of support cases can now rely on AI to retrieve relevant knowledge and draft initial responses, reducing time spent per case and driving meaningful productivity gains at scale. At the same time, AI is being embedded into products and shared through reusable frameworks, enabling teams to build and deploy new capabilities faster while maintaining consistency across the organization.
1 HPE, AI in action at HPE: Lessons from 700+ enterprise AI initiatives, 2025
What HPE learned scaling AI — and what that means for you
HPE’s experience in scaling AI across more than 700 initiatives revealed a consistent pattern: Organizations that apply AI to existing workflows see progress but hit a ceiling. Breaking through that ceiling requires a shift to an AI-first approach: treating AI as a system that spans infrastructure, data, operations, and the way work gets done. The following principles define what it takes to make that shift.2
2 HPE, AI in action at HPE: Lessons from 700+ enterprise AI initiatives, 2025
Start with real problems
Treat data as a system
Design for trust, not just performance
Rethink workflows, not just tools
Build governance early
Focus AI efforts on high-impact business challenges, not isolated experiments. Prioritize use cases where outcomes are measurable and tied directly to business value so early wins can scale into broader transformation.
Build a clear, organization-wide view of data sources, types, and formats as a starting point. From there, approach data as a shared, enterprise asset rather than a collection of silos. Ensure that it is curated, governed, and accessible across environments so AI systems can operate consistently and deliver reliable results at scale.
Adoption depends on confidence. Build AI systems that are transparent, consistent, and controllable, and embed them into existing workflows so teams can rely on them in real-world decision-making.
Layering AI onto existing processes delivers incremental gains. Redesign workflows end-to-end so AI is embedded into how work actually gets done, unlocking improvements in speed, efficiency, and outcomes.
As AI scales, so do complexity and risk. Establish governance from the outset — spanning data, models, and workflows — so you can move quickly while maintaining control, compliance, and accountability.
@2026 HPE
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Learn how HPE can enable your AI-first transformation
Turning AI into real business impact requires more than models. It demands the right foundation. Explore how HPE can help you build an AI-first foundation, from infrastructure and data readiness to governance, edge, networking, and scaled operations.
