AI Is Coming for Your Business — and That’s a Good Thing (If You’re Ready)
I have spent my life working at being both an effective coach and player with our businesses, our clients’ businesses, sports teams, and the people I care about in my personal life. If you know me, you know I am passionate about Indiana University, the Kelley School of Business and Hoosier Athletics. One of my favorite quotes, which gets credited to many sources including Vince Lombardi, Bear Bryant, Bob Knight and others, is “Everyone has the will to win, but few have the will to prepare to win.” I get asked occasionally to talk to groups of people about new, transformational technologies and the impact to our world and businesses. Many technological innovations come and go but some are truly consequential—generative AI is the latter.
Preparing to win has never been more important for any business looking to capitalize on the opportunity that the coming wave of business solutions riding on the Generative AI tools that have been and will be built. These tools, like agentic AI and multi-agent solutions, are designed to work independently of humans. They will learn and evolve with as little human intervention as is practical. That practicality will change over time with the human in the loop becoming less of an actor and more of an observer and validator.
Success depends on much more than the just the algorithms.
At Lucid, we know that success will depend on effective Strategic Data Management (SDM) and Organizational Change Management (OCM) capabilities. These solutions are only as strong as the data that powers it and the people who embrace it. Without a disciplined approach to data strategy and deliberate management of human adoption, even the most promising AI initiatives will fail.
Are you ready?
AI promises transformative value: smarter decisions, faster insights, automated workflows, and entirely new business models. However, most AI projects fail to reach production or deliver meaningful ROI. Why? Because most organizations have not prepared to win by laying the foundation required to succeed.
Data lives in silos, lacks governance, and exists in inconsistent formats. Without a single source of truth, AI models produce unreliable results.
Employees see AI as a threat to their jobs or don’t understand how to use it. Without trust and adoption, even great tools go unused.
Leadership pursues AI without clear business outcomes or integration with enterprise strategy. AI becomes a science experiment rather than a growth engine.
If AI is the brain, data is the blood. Every insight, recommendation, and output AI produces is based on the quality, completeness, and context of the data feeding it. Strategic Data Management (SDM) is the practice of aligning an organization’s data assets, architecture, and governance with its strategic goals. It ensures that the data driving AI is trusted, accessible, and ethically managed.
Key Components of Strategic Data Management:
Data Strategy and Vision
An effective AI program begins with a clear data strategy aligned to business objectives.
Ask yourself:What problems are we solving with AI?
What data do we need to solve them?
How will success be measured?
Data Governance
Good governance makes data usable. It defines ownership, standards, and accountability for data accuracy, privacy, and compliance. Governance also establishes the trust that employees and customers need to feel confident about AI’s outcomes.Data Quality and Integrity
AI depends on clean, consistent, and complete data. A model trained on bad data will produce biased or inaccurate results—a concept often summarized as “garbage in, garbage out.”Data Architecture and Infrastructure
AI workloads require modern, flexible data architectures that can scale and integrate across systems.Metadata and Semantic Layer
AI thrives when data is contextualized. Metadata describes what data means, where it came from, and how it’s used. A semantic layer translates raw data into business terms, empowering both humans and AI systems to interpret it correctly.
Organizational Change Management (OCM) prepares, equips, and supports individuals to adopt change successfully. In the context of AI, OCM helps organizations bridge the gap between technology potential and human adoption.
Key Components of Organizational Change Management:
Build a Compelling AI Vision
Employees must understand why AI is being introduced and how it supports the company’s mission. This includes the business outcomes AI enables, the benefits to employees, and the shared vision for human–AI collaboration.Engage Leadership Early and Often
Visible executive sponsorship is the single most important success factor in change initiatives.Assess Organizational Readiness
Before deploying AI, conduct a change readiness assessment. These insights inform targeted training, communication, and engagement plans.Empower AI Champions
Identify early adopters and AI ambassadors across departments. They become the bridge between technical teams and end users, providing peer support, feedback, and credibility.Build Skills and Confidence
AI success requires data fluency across the workforce. Training should be continuous and role-specific, evolving as AI capabilities mature.Communicate Transparently and Frequently
Communication should demystify AI, not glorify it.Reinforce and Sustain Change
OCM doesn’t end at go-live. Sustained success requires:Ongoing measurement of adoption and sentiment
Continuous improvement loops
Celebrating wins and learning from failures
Integrating AI behaviors into performance metrics and culture
Integrating SDM and OCM is the formula for success. AI success happens when they work together.
This is a three-part framework:
Step 1: Build the Foundation — Data Readiness
Establish a clear data strategy tied to business objectives
Cleanse, govern, and integrate data across silos
Without this step, AI models will struggle to produce reliable, repeatable insights.
Step 2: Enable the Organization — Change Readiness
Prepare leadership and employees for AI-driven change
Communicate purpose and benefits clearly
Equip teams with new skills and confidence to use AI tools
This ensures AI is used consistently and confidently, not resisted or ignored.
Step 3: Accelerate Impact — AI Enablement
Once data and people are ready, the organization can move faster:
Deploy generative AI, predictive analytics, or automation tools
Integrate AI into daily workflows and decision-making
Monitor impact and continuously optimize models and behaviors
In this integrated model, data provides accuracy, people provide adoption, and AI delivers outcomes.
You need to know where you are across both the data and organizational dimensions.
Getting an honest assessment of your AI readiness and maturity level can help. There are maturity models that can help:
| Maturity Level | Data Management Focus | Change Management Focus | AI Capability |
|---|---|---|---|
| Level 1: Ad Hoc | Data is siloed and inconsistent | No structured change process | Experimental pilots |
| Level 2: Developing | Some data governance in place | Reactive communication | Isolated successes |
| Level 3: Defined | Centralized data strategy | Formal change frameworks | Targeted use cases |
| Level 4: Integrated |
Enterprise-wide data quality and access | Active leadership engagement | Operationalized AI |
| Level 5: Optimized | Real-time, ethical, self-service data | Continuous learning culture | Scaled, adaptive AI ecosystems |
The goal is to know where you are now, where you need to be, and when you need to be there. Everyone does not need to be level five day. Different parts of the org can also be at different levels if that is the right path.
AI is a big opportunity and big operational challenge. Nobody gets 5 years to deliver value, so how do we get value quickly, given the need to bolster our foundation?
Answer: Thin slice it.
A thin-slice approach to AI foundation work, SDM, and OCM will fix immediate needs for the foundational changes needed for the first AI initiative to be delivered. Instead of a "big bang" approach, this iterative method delivers a minimum viable product for a specific business issue or issues, allowing teams to test and integrate solutions early, reduce the cost of delay, and adapt to business needs in complex, competitive environments.
Identify a Specific Business Issue: Focus on a single, actionable problem
Develop a "Thin Slice:" Create a small, complete, end-to-end solution that provides just enough functionality to address the identified business issue.
Integrate and Test Early
Deliver Value and Gather Feedback
Iterate and Refine: Review the impact of the slice, identify roadblocks, and then repeat the process to add more features or address new issues in subsequent iterations.
The companies that win won’t be the ones with the biggest models or budgets—they’ll be the ones that prepare to win. They will invest in their data and their people with equal commitment.