Crystal Ball 2026: Prediction Roundup Part 2

Our roundup authors this year had a lot of input on data: “clean data,” data governance, archives, “digital arks,” etc.

Obviously, more than anything, to a manufacturer, organized and accessible data is vitally important for making use of artificial intelligence—for example, using agentic or Generative AI requires organizations to be diligent about their data and put best practices in place.

Here are our SMEs’ insights for this second chapter of our Crystal Ball 2026 roundup:

Nathanael Powrie, senior director, knowledge management and data analytics, Maine Pointe

Data governance will emerge as the new bottleneck breaker. As AI ubiquity grows, trusted data becomes the linchpin. Investments will shift toward cleaning, integrating, and securing datasets over flashy algorithms. Companies with reliable data foundations will outpace those bogged down by inconsistencies.

Great Question: A Manufacturing Podcast: It's ALL about your data

AI “accountability” will hit full stride. By 2026, manufacturers won't tolerate AI that's not tied to hard metrics. The focus will sharpen on deployments that directly boost margins, cut downtime, or optimize assets. Tools without proven ROI will get sidelined as leaders prioritize impact over innovation for its own sake.

Aron Brand, chief technology officer, CTERA

Archive is having a renaissance. It’s becoming a core system for resilience, security, and future intelligence.

For years, “archive” meant cost control or a compliance checkbox. Old PLC logs, machine telemetry, maintenance records, and video were pushed into cold storage and rarely revisited. That model is breaking.

See also: Why AI is quickly becoming essential manufacturing infrastructure

Industrial data volume is exploding and keeping everything online and writable is getting harder to justify. Storage spend rises, operational complexity grows, and the attack surface expands with every additional dataset.

Deletion is one response, but it’s the wrong one for many environments. Historical industrial data is increasing in value. Long-term equipment behavior, failure modes, process changes, and operator interventions are exactly what you need to build domain-specific AI and improve reliability.

That’s why archive is returning in 2026, and why immutability is becoming the default for inactive data. Moving historical data into WORM-protected storage reduces the blast radius of ransomware and operational attacks.

Data that isn’t writable can’t be silently altered, re-encrypted, or manipulated after the fact. Archive becomes an active defense layer, not an afterthought.

Leading industrials organizations will increasingly treat archive as a “digital ark”—or a protected, immutable memory of how systems actually behaved over time, ready to support recovery, audits, and the next generation of industrial AI.

Nicole DiNicola, global VP of marketing, Smartcat

Scaling globally means coordinating complexity, not just creating more. In 2026, marketing teams will continue to face the pressure to “do more with less,” but the challenge will go beyond volume. Operational complexity is becoming the bigger obstacle.

Marketers have learned how to scale volume with AI, but many haven’t figured out how to connect the systems and workflows behind it. That’s where teams still lose time, managing duplicate versions, correcting inconsistencies, or navigating disconnected tools.

See also: Intelligent robots are bridging the gap from automation to autonomy

The best workflows will be global-ready by design. In 2026, more organizations will redesign their content operations so that multilingual and multi-market readiness is built in from the start. High-performing teams are already anticipating regional needs earlier in the process, removing the need for late-stage rewrites or rushed fixes.

This shift is especially visible in industries like life sciences, where expansion into APAC and China has raised the bar for speed and accuracy. Addressing cultural and regulatory nuances upfront reduces delays and duplication—clearing the path for faster, more reliable launches. As expansion accelerates, designing for global readiness upfront will become a competitive advantage for organizations.

The rise of the high-AIQ marketer. In 2026, marketing roles will continue shifting toward system shaping, strategic judgment, and relationship-building. As automation takes on more execution, teams with high AIQ—those who can turn brand and business priorities into scalable workflows and quality standards—will lead the way.

At the same time, declining trust in paid channels and rising demand for authenticity and cultural relevance will raise expectations for localized content that feels genuine.

Audiences have little patience for generic messaging, mistranslations, or content that feels detached from their market. Marketers who combine AI-enabled scale with strong judgment and cultural fluency will help their brands show up credibly and consistently in every market they serve.

See also: The data-driven answer to manufacturing's $50 billion problem

Velocity and risk as core KPIs. In 2026, go-to-market velocity will become one of the clearest indicators of AI’s value to organizations. The pressure is especially high in manufacturing, CPG, and life sciences, where companies are entering new markets faster, refreshing product lines more often, and responding to region-specific regulatory updates on tighter timelines.

But speed alone isn’t enough, especially in industries where inaccuracies mean millions of dollars lost in regulatory fines. Marketing leaders will need to track risk alongside velocity, monitoring accuracy, compliance, and correction rates to ensure that speed doesn’t compromise quality.

Velocity shows whether teams can keep up. Risk reveals whether they’re staying in control. Together, these KPIs will shape how marketing teams measure success in an AI-powered organization.

Editor's Note: The Crystal Ball Series will continue on Friday, Jan. 9, 2026.