How Enterprises Succeed with AI: Data, Trust, and Change

Jan 19, 2026

In today’s rapidly evolving technological landscape, artificial intelligence (AI) is no longer just a buzzword - it has become a critical driver of innovation and operational transformation. But as enterprises navigate their journey to AI adoption, they often discover that the real challenge lies not in the technology itself, but in the human and organizational changes required to make AI truly impactful.

Ben Shriner, Head of AI and Modern Data Strategy at AWS, shared valuable insights on these challenges and opportunities during a discussion at AWS re:Invent. From the importance of change management to the critical role of data strategy and the rise of agentic AI, this article breaks down the essential takeaways for business leaders looking to leverage AI effectively.

The Human Element: Why AI Adoption Is About People, Not Just Technology

One of the most striking themes from the conversation was the emphasis on change management and the human element in AI adoption. Shriner explained that while the technology behind AI is complex, the most significant barrier to successful deployment often comes down to company culture and employee fears.

For leaders, this is a critical "leadership moment" that requires a proactive approach to guiding teams through the transformation. Empathy, as Shriner highlighted, is key. Leaders must acknowledge and address the concerns of employees who may feel uncertain or intimidated by AI-driven changes.

What Does Effective Change Management Look Like?

A Clear North Star: Define a compelling vision for why the transformation is happening and how it benefits everyone - not just the organization, but individuals as well.
Training and Exposure: Offer hands-on opportunities for employees to engage with AI tools, helping to demystify the technology while building skills and confidence.
Transparency About Impact: Be upfront about how AI will change roles, streamline tasks, and create opportunities for innovation rather than simply automating people out of jobs.

Ultimately, Shriner noted, building trust and understanding requires leaders to take an active role in addressing "the elephant in the room."

Building Trust in Agentic AI: Governance and Policies

The rise of agentic AI, where autonomous systems perform tasks and make decisions within enterprises, is a transformative shift. But it also raises a crucial question: how can organizations trust these AI agents to make the right decisions?

Shriner pointed out that trust is built through robust governance frameworks and proactive control mechanisms. He shared AWS’s approach to addressing this challenge, which includes:

Agent Policies: A system that allows organizations to define what tools and actions their AI agents can access. This ensures human oversight and accountability.
Guard Rails and Boundaries: Governance tools such as those available in AWS Bedrock help restrict what AI agents can do, ensuring their behavior aligns with organizational goals.
Agentic Evaluation: A method for continuously monitoring and evaluating AI agents to ensure their outputs remain aligned with predefined objectives.

Furthermore, Shriner discussed the importance of automated reasoning to reduce the risk of "hallucinations" - a term for incorrect or fabricated outputs generated by AI. By improving accuracy and reliability, automated reasoning not only enhances the performance of AI agents but also strengthens user trust and adoption.

Data: The Foundation of Every AI Journey

"Every AI story is a data story", Shriner explained, emphasizing that AI’s effectiveness is fundamentally tied to the quality and availability of data. However, many enterprises struggle with fragmented, inconsistent, or incomplete data architectures - often the result of mergers, acquisitions, or legacy systems.

Best Practices for a Modern Data Strategy

Treat Data as a Product: Instead of viewing data as a passive resource, treat it as an asset designed to serve the entire organization. This mindset shift encourages collaboration and consumption across departments.
Work Backwards from the Problem: Start by defining the specific business problem you want to solve, determine the data needed to address it, and then build your AI strategy around those requirements.
Align Data and AI Strategies: Shriner argued that organizations cannot have an AI strategy without a well-defined data strategy. Misaligned data can lead to inconsistent results and missed opportunities for scaling AI across the enterprise.

By taking a proactive approach to data architecture, enterprises can avoid the common pitfall of deploying AI models that fail to meet their potential due to poor-quality data.

The Future of Work: Human Skills Meet Agentic AI

Looking ahead, Shriner predicted that the workplace of the future will involve managing both humans and AI agents. This shift is already underway, as automated tools increasingly take on repetitive, low-value tasks, freeing up human workers to focus on creativity, critical thinking, and innovation.

Interestingly, this evolution places greater emphasis on uniquely human skills. Shriner referenced a World Economic Forum report that highlights the importance of skills like curiosity, lifelong learning, communication, and influence in the workforce of 2030.

"This technology doesn’t replace our core human qualities", Shriner remarked. "Instead, it can liberate us from the mundane, allowing us to focus on higher-value, intellectually rewarding work."

A Real-World Example

Shriner shared how his own team used AI to automate time-consuming data entry tasks, saving each team member about one day per month. This newfound time was redirected toward engaging with customers - a more fulfilling and impactful use of their skills.

The lesson? When implemented thoughtfully, AI can improve not just productivity but also employee satisfaction and overall quality of work life.

Key Takeaways

Empathy is Essential for Change Management: Leaders must address employee fears and communicate how AI will benefit both the organization and its people.
Trust in AI Requires Governance: Use frameworks like agent policies, guard rails, and continuous evaluation to ensure autonomous AI systems behave as intended.
Data Drives AI Success: A robust data strategy is inseparable from an AI strategy. Start with a clear business problem and ensure your data is prepared to address it.
Human Skills Are the Future: As AI handles repetitive tasks, skills like critical thinking, communication, and creativity will become even more valuable.
AI Can Improve Quality of Work Life: Thoughtfully implemented AI liberates employees from mundane tasks, enabling them to focus on higher-value work.

Final Thoughts

The journey to AI adoption is a multifaceted one, requiring not just technical expertise but also a thoughtful approach to change management, data strategy, and governance. As enterprises embrace agentic AI, they stand to unlock new levels of efficiency, creativity, and innovation. But success depends on building trust - both in the technology and among the people who will use it.

By prioritizing empathy, aligning AI and data strategies, and preparing for the future of work, business leaders can position their organizations to thrive in this exciting new era.

Source: "AWS's Ben Schreiner on How Enterprises Succeed with AI" - TechVoices, YouTube, Dec 31, 2025 - https://www.youtube.com/watch?v=l8FIxvoD5jk