How data, governance and organizational change define AI success

AI surged forward like a high-swinging pendulum, but now it’s swinging back, bringing focus to the fundamentals that will make 2026 a pivotal year in the world of AI development.

Manufacturers and other companies are pouring record investments into new AI technology. Private investment in the U.S. in AI reached $109.1 billion in 2024, compared to China’s $9.3 billion and the U.K.’s $4.5 billion.

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Despite the spending and adoption, most AI remains in experimentation or piloting stages, and failure rates are high. 70% to 85% of Gen-AI deployments fail to deliver their intended ROI, which is double the already-high failure rate of traditional IT projects.

Gartner predicted that by the end of 2025 at least 30% of Gen-AI projects will be abandoned after the proof-of-concept phase.

The AI dream is real, but the execution is tricky

Keeping pace with AI development is not for the faint of heart. With its rapid evolution, models are becoming more intelligent and less expensive. Running systems that once required significant investment is now dramatically more affordable.

Hardware costs are falling by around a third per year, and energy efficiency is improving rapidly. Open models are also closing gaps with proprietary systems, narrowing performance differences in a remarkably short time.

See also: How digitally mature is manufacturing today?

At the same time, regulatory pressure is rising. In 2024, 59 AI-related regulations were introduced by U.S. federal agencies, which was double the prior year.

Organizations must see these regulations as part of an ongoing governance strategy, with dedicated teams monitoring compliance, enforcing standards, and ensuring AI operations remain secure and aligned with both legal and ethical requirements.

People are also at the center of the adoption challenges. Only 7% of the workforce can effectively leverage AI for meaningful outcomes, while the remaining 93% are experimenting with or not using AI at all. This is underscored by employee concerns about AI risk, as well as organizational fatigue.

In 2026, the companies that win will be those that double down on the fundamentals: governance, data quality and organizational change. These are the three core initiatives that will lead to further adoption, from both an enterprise and people standpoint.

Governance sets ground rules

AI moves fast, and organizations that treat it like a one-off project often fail. Strong governance ensures systems are monitored, maintained and aligned with business goals.

Without it, implementations can quickly break, especially if models are integrated into client-facing operations or critical workflows.

Teams need clear policies, ongoing oversight and a dedicated AI governance function to monitor systems, verify they connect with existing platforms and address issues before they impact users.

Governance also establishes accountability and helps organizations adapt to evolving technology. What worked in January may no longer function by July, so iterative oversight is essential.

Leaders who prioritize governance set the ground rules for safe, reliable and effective AI adoption, thereby reducing the risk of failed projects and helping the organization extract real business value.

Data quality determines the outcome

AI’s effectiveness is only as good as the data feeding it, and poor data quality remains a major obstacle. Even the most advanced models can’t succeed with incomplete, inconsistent or biased data.

Up to 80% of AI project time goes into cleaning and preparing data, and even organizations with large historical datasets often find their information too shallow or unstructured for effective AI use.

See also: When AI understands the plant

The focus should be on high-quality, structured and actionable data. Businesses that invest in data pipelines and clean datasets, as well as ongoing monitoring, will see models perform reliably and predictably.

Organizational change powers adoption

Technology alone cannot transform operations. People matter, too. Their buy-in, trust and confidence in their systems are essential. Many employees remain hesitant, expressing concerns over its use and worried about its impact on jobs. This anxiety slows adoption and limits impact.

Beyond fear, there’s fatigue. With constant transformation comes burnout: 45% of employees reported burnout due to organizational change. Shifting systems and unclear expectations exhaust them. For AI initiatives to take hold, companies need to invest just as much in communication, training and cultural alignment as they do in technology.

See also: Labor deficit remains top hurdle for manufacturers, survey finds

The companies pulling ahead understand this. While 80% of organizations set efficiency as their main goal for AI, the real high performers take it a step further, using AI to drive growth, spark innovation and uncover new value.

Organizational change involves instilling a data-driven culture, training employees to utilize AI effectively and integrating AI into existing workflows.

Leadership alignment ensures that AI adoption is purposeful, measurable and tied to business outcomes, all while making it a cultural imperative to empower employees with its use.

Putting AI fundamentals into practice

Organizations looking to succeed in 2026 should focus on foundational practices across four areas:

• Data and analytics strategy: Conduct maturity assessments across strategy, governance and technology. Tailor a data roadmap aligned with business objectives, processes and people.
• Scalable platforms and insights: Build data platforms that connect sources and maintain quality. Democratize insights through intuitive dashboards and pre-built data models that cover the majority of business needs.
• AI and value creation: Implement AI use cases with measurable business impact, improving forecasting, operations and innovation. Pre-built models and generative AI tools can accelerate results.
• Leadership and change management: Invest in skilled teams, embed change management and enforce governance and compliance guardrails. Align leadership and teams to ensure AI adoption drives measurable outcomes.

AI is now a business imperative, and success in 2026 will not come from the newest tools or model releases. It will come from mastering governance, high-quality data and organizational change.

The real differentiators will be leaders who set clear outcomes, align their organizations and hold cross-functional teams accountable. Those are the companies that will turn AI from a boardroom talking point into measurable results.