2026, the year AI breaks through: 3 trends data leaders can’t ignore

4-minute read

After a year of nonstop announcements, pilot projects, and board-level pressure, executives are asking the same question as they enter 2026:

Is our business ready to turn AI ambition into measurable business value?

As we plan for the coming months, I want to share three of the most impactful trends of data and AI to watch in 2026. They’re less about shiny tools or specific models and more about the uncomfortable decisions leaders will need to make.

Trend #1: The AI bubble doesn’t pop but becomes more uncomfortable

Everyone is concerned that we’re either investing too much in AI Infrastructure or not nearly enough.

Key drivers

The underlying hypothesis is that the business models / justification behind AI will catch up, more compute means better inference, and the future will be built on top of the current technology.

This is flawed thinking. AI growth won’t follow a straight line based on compute and inference. Growth will be driven by smarter and more efficient usage. Like past cycles in cloud adoption, we’re entering the phase where efficiency, discipline, and cost control matter more than ambition.

Key themes leaders should watch

Mid-term: Constraints drive efficiency gains

• AI companies will drive model efficiency faster than expected (e.g. DeepSeek algorithms) and targeted inference will move to the edge (i.e. device), making massive investment unnecessary.
• Excitement and hype will settle and the hyperscalers can meet customer demand with far less than planned as companies streamline their use of AI.

Short term: Everyone engages in infrastructure

• Immediate efficiency gains will come from IT improving their understanding of how different AI infrastructure choices impact the enterprise AI strategy.
• At a minimum, IT will take the lead implementing the proper cost controls and management to avoid cost overruns and align with enterprise AI policies.
• Business power users will become savvier, asking for specific hardware to develop their own agents and bespoke models.
• Right-sizing and customizing infrastructure for AI and data workloads will be a more common and more critical discussion between business lines and IT.

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Trend #2: AI’s ROI judgement day is coming

We’re moving from experimentation to accountability. Most AI projects are not hitting their ambitious ROI goals. Executives are demanding results, and many AI programs are struggling to justify their budgets. The time to experiment will be ending, and businesses will need to show impact.

Key drivers

AI implementations can struggle for many reasons: immature IT teams, low AI Literacy, poorly understood AI governance, change fatigue, poorly documented processes, etc. AI ROI is captured when AI is considered a capability, not a tool with a model.

Key themes leaders should watch

Mid-term: Platform modernization + AI literacy friction points addressed

• Legacy business tools and systems will be modernized to ensure data can be integrated with cloud foundational models and data scattered across the ecosystem.
• AI literacy training will become essential for everyone, just like data literacy programs when dashboards rolled out.
• Focused experimentation and intentional training programs will decrease change fatigue and build AI capabilities.
• To ensure coordination across teams, most companies will centralize their AI efforts with light federation across lines of business.

Short-term: AI + data governance is solidified before AI scales

• AI projects quickly expose weaknesses in core data capabilities in governance, integration, and quality.
• Companies will pause their move from pilot to production, focusing on improving MLOps, defining AI roles and responsibilities, implementing model accuracy and data quality checks, and ensuring accurate lineage mapping.

Trend #3: Data strategy drives AI strategy

Executives are demanding AI strategies, but often without a clear data strategy. Without clear data ownership, quality standards, privacy considerations, and operating models, AI initiatives collapse under their own complexity.

Key drivers

Data strategy is a common phrase, but the key pillars and objectives behind it often reflect individual’s roles and background. Data strategy definitions are often incomplete and defined with different pillars across lines of business. Data strategy goals must include enabling ease of use for data consumers and designing better user experiences.

Key themes leaders should watch

Mid-term: No more AI black boxes as transparency becomes non-negotiable

• Depending on your industry, regulation will come at different paces (e.g. the EU has already established an AI regulatory framework, but it is not aging well).
• At a minimum, companies should be able to provide transparency with explainability, traceability, and auditability for their AI systems.

Short-term: Data quality + operating models provide lift to AI programs

• Each data governance initiative needs to drive towards a unified approach to delivering the data strategy.
• A first step will be ensuring the current data catalogs and data quality tools are meeting the current business needs and able to scale to future needs.
• Operating models like data mesh will address the core challenge of business ownership of data quality.

Final thoughts

2026 won’t be defined by who experiments the most with AI. It will be defined by who operationalizes it best. Organizations that win will stop chasing hype and start building durable data capabilities that survive scrutiny, budgets, and regulation.

If you’re trying to turn AI ambition into measurable outcomes, that’s exactly the kind of work we do at Logic20/20.

If you’re not sure where to start to advance your data initiatives, I’m happy to discuss your challenges and run a workshop to help kick off your 2026.

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Mick Wagner is responsible for leading the AI and Analytics Practice to exceed client expectations, develop innovative solutions, and achieve organizational growth. Mick has 20 years of data analytics consulting experience in data strategy, modern data platforms, and AI.