As we look toward 2026 and beyond, Gartner’s top strategic predictions point to an unmistakable truth: AI is no longer a technology trend — it is the new economic infrastructure. From sovereign AI ecosystems to agent-driven decision making, the next wave of transformation will reshape how organizations scale, compete, and govern data.
At DDN, we see these predictions converging around a single, unavoidable imperative: real-time data intelligence is now the foundation of enterprise advantage. The winners of the AI economy will be those who pair rapid innovation with disciplined data control, infrastructure readiness, and responsible governance.
Below is DDN’s perspective on the future Gartner describes and what businesses must do to not just prepare, but lead.
1. The AI Skills Paradox Will Reshape the Workforce: Data Will Determine Who Thrives
Gartner predicts that by 2027, 75% of hiring processes will require AI proficiency — while at the same time, overreliance on AI will force 50% of companies to conduct “AI-free” skills assessments.
This is the new skills paradox: Enterprises need both AI fluency and independent human judgment.
That balance will only be possible if employees — and the AI systems they rely on — are trained on high-quality, trustworthy, governable data. Organizations that lack visibility into data lineage, integrity, or performance will struggle to build the workforce they need and the automation they envision.
AI readiness starts with data readiness. The businesses that invest in platforms capable of sustaining low-latency, high-volume AI workflows — while preserving data quality and traceability — will lead in both talent development and productivity.
2. AI Sovereignty and Regional AI Stacks Will Fragment the Global Landscape
According to Gartner, 35% of countries will be locked into region-specific AI platforms by 2027. The era of borderless AI is ending.
For global enterprises, this means:
- Different regions will require different models
- Data residency will dictate architecture
- Compliance, performance, and sovereignty will increasingly conflict
Enterprises need a data platform strategy that is modular, portable, and sovereignty-aware—allowing AI to run optimally in the U.S., EU, Asia, and emerging regions without rebuilding or re-architecting.
High-performance, multi-cloud, and on-prem AI infrastructure is no longer optional; it is the operating system of global business continuity.
3. Multiagent AI Will Dominate Customer Operations — but Only If the Data Layer Can Keep Up
By 2028, organizations that use multi-agent AI across 80% of customer-facing processes will outperform their peers.
These systems rely on AI “subagents,” specialized models that collaborate to resolve tasks, accelerate service, and personalize experiences.
But multi-agent systems only work when:
- Data flows in real time
- Models access consistent contextual information
- Infrastructure prevents bottlenecks at massive scale
AI agents cannot outperform the storage feeding them. High-throughput, low-latency data systems will determine whether multi-agent AI becomes a competitive advantage or a costly experiment.
4. B2B Buying Will Shift to AI-Agent-Driven Commerce — Trust Will Run on Data Transparency
Gartner predicts that 90% of B2B purchases will be initiated, evaluated, or completed by AI agents by 2028—driving more than $15 trillion in spend.
This machine-to-machine economy will require:
- Verifiable performance data
- Transparent telemetry
- API-first architectures
- Real-time benchmarking
In an AI-mediated marketplace, trust is earned through data integrity and performance transparency. Vendors that cannot provide verifiable, high-fidelity operational data will simply not surface in AI-agent discovery flows.
5. AI Risk Will Become a Board-Level Issue — and Infrastructure Will Be the First Line of Defense
Gartner anticipates over 2,000 “death by AI” legal claims by 2026 due to insufficient guardrails.
Enterprises must assume that:
- AI safety is an infrastructure problem
- Explainability begins with data governance
- Risk mitigation requires auditable pipelines and deterministic performance
- The infrastructure layer must enforce boundaries, not just accelerate models
AI safety is impossible without data safety. Infrastructure must be engineered to detect anomalies, preserve lineage, ensure reproducibility, and prevent model drift or hallucination from becoming business—or human—risks.
The Throughline: AI Winners Will Be Defined by the Strength of Their Data Infrastructure
Across all of Gartner’s predictions, one pattern emerges:
The model no longer determines AI success—it is the data architecture behind it that determines success.
Data sovereignty, skills development, agentic AI, autonomous commerce, compliance, and safety all depend on:
- High-performance data movement
- Scalable and cost-efficient storage
- Transparent, governed, sovereign-ready architecture
- Infrastructure that gives AI agents exactly what they need, when they need it
This has been DDN’s mission for more than two decades: to deliver the world’s most performant and reliable data platforms for AI at a global scale.
The organizations that succeed in the next decade will be those that treat AI infrastructure not as a backend system — but as the strategic engine of growth, governance, and competitive dominance.
The future belongs to those who build for it now. Let’s get to work.
