Center People and Processes in Your Next AI Implementation

AI is often hailed as a game-changer for industries, yet the stark reality is that most organizations are unprepared to harness its full potential. The promise of AI isn’t in the technology itself—it’s in the ability to seamlessly integrate it into the organization's culture, operations, and people. And that’s where most will fail.

A recent Forrester report predicts that only 20% of tech executives leading digital transformations will succeed, citing challenges coordinating across business operations, HR, and IT. Similarly, BCG’s insights reveal that only 24% of organizations achieve real value from their AI projects, with 70% of AI adoption success depending on people and processes.

These numbers paint a grim picture.

The Hidden Challenges of AI Adoption

The true complexity of AI adoption lies not in the technology or data but in preparing the people and processes that support it. Overlooking these critical human factors often leads to:

• Poor cross-team collaboration
• Slow adaptability and a lack of continuous improvement
• Inability to address data quality issues.
• Increased employee resistance to future projects due to low ROI or repeated failures

These challenges can derail AI initiatives, diminish morale, and make it harder to build momentum for future efforts. Recognizing and addressing these barriers is key to realizing AI’s value.

Overcoming these challenges requires a shift in focus. Preparing your people and processes is the cornerstone of successful AI adoption. Rather than relying solely on technology, organizations must prioritize team readiness, foster cultural shifts, and improve operational agility. By addressing these foundational elements, you create the conditions necessary for AI to deliver measurable impact, sustain its value, and build an AI-ready organization.

# How To Prepare Your Organization for AI Adoption

# 1. Building Operational Agility for AI

Organizations must go beyond simply deploying advanced tools—they need to create a foundation of operational agility that empowers teams to adapt, experiment, and scale with intention. Building this agility requires a strategic approach that aligns people, processes, and governance to ensure AI integrates seamlessly into core operations.

The pace of change with AI far surpasses that of traditional technology, and organizations recognize that AI solutions could look very different in even two years. This creates an imperative to be able to adapt quickly. Organizations need to experiment faster and test their hypotheses while being intentional with clear guidelines and guardrails.

To help one of our Fortune 500 Tech companies accomplish this, we developed an agile operational framework for rapid iteration and flexibility. This approach allowed the client to adapt workflows, refine data models, and respond to changing market conditions in real time, while intentionally designing intuitive, sustainable solutions.

Key Strategies for Operational Agility:

• Form a centralized AI council: A centralized AI council ensures governance and alignment across the organization. This council will set guardrails and policies that foster scalable, high-performing AI innovations while mitigating risks. Clear governance helps manage the complexity of scaling AI across departments and ensures alignment with business goals while meeting data, security, and design standards.

• Create experimentation and pilot programs: Provide space to experiment with new tools and perform controlled tests. Starting small with pilot programs builds trust in AI, allowing teams to iterate, test, and improve models.

• Adjust workflows incrementally: Define a NorthStar vision for evolving workflows to meet business goals. Make incremental changes based on pilot results to guide real-time adjustments and ensure smooth integration into daily operations.

• Establish feedback loops: Feedback mechanisms help track AI performance and maintain relevance. AI models should be regularly reviewed, retrained, and refined based on data quality and shifting business needs.

Operational agility isn’t just about the early stages. It’s critical to move beyond experimentation and pilot programs to fully integrate AI into core operations at scale. For example, in a recent client engagement, we highlighted the importance of leadership to align pilots around a shared vision and ensure a centralized council-supported governance. This approach helped establish the foundations for scalable AI adoption across teams and workflows.

# 2. Building an AI-Ready Workforce Through Cultural Shifts

While operational agility lays the framework for AI implementation, its long-term success hinges on the people within your organization. The adoption of AI is not just a technological shift; it’s a cultural transformation. Addressing culture is essential because it shapes the values and the way employees interact with new technologies, collaborate across departments, and adapt to change. Even the most advanced AI systems can fail to achieve their intended impact without the right cultural foundation.

By aligning mindsets, behaviors, and organizational values, you can empower your teams to fully embrace AI as a transformative force. Below are the key cultural shifts necessary to prepare your workforce for successful AI adoption.

Key Cultural Shifts for AI Adoption:

• Value-driven mindset: Organize work based on outcomes and value instead of employee tasks

• Continuous learning: Foster a learning-first culture that encourages curiosity and experimentation to support the ongoing adaption that AI requires. Regularly review and update training materials to keep employees informed.

• Cross-departmental collaboration: Solicit input from HR, compliance, business operations, and customer-facing teams to ensure alignment with technical and business objectives. AI solutions should not live solely within IT or data science departments.

• Data-driven mindset: AI thrives on high-quality data. Shifting the organizational mindset to treat data as a strategic asset is essential, particularly for non-technical teams. Employees must understand the importance of data governance in ensuring accuracy, fairness, and the overall success of AI systems.

• Ethical AI use: AI brings complex ethical issues, such as bias, data privacy, and decision transparency, which require trust from both employees and customers. Building a culture that emphasizes ethical use, transparency in decision-making, and accountability is crucial.

• Innovation: Employees should feel safe to share ideas without fear of judgment and view mistakes as learning opportunities. Collaboration with startups, academic institutions, or customer input can introduce fresh perspectives and uncover opportunities internal teams might miss. Additionally, emphasizing reusability in AI solutions, such as reusable code, can accelerate development by 30-50%, making innovation more scalable.

• AI as a team member: Encourage employees to view AI as a collaborative partner who can augment their work, be a brainstorming partner, and complete repetitive work.

Related Content: A Leader’s Guide: The Interrelation Between Change Management and Culture Change

# 3. Building Trust and Transparency with Change Management

Cultural shifts and change management are essential partners in driving successful AI adoption. While cultural shifts reshape organizational values, beliefs, and behaviors, change management provides the structure to guide employees through transitions smoothly. Together, they foster acceptance, adaptability, and sustained transformation, creating an environment where employees feel empowered to embrace AI as a catalyst for growth.

Building trust and transparency through effective change management is key to sustaining these changes. Employees need clarity about how AI will impact their roles, support to navigate changes, and reassurance about their place in the organization’s future. Thoughtful strategies can address skepticism, reduce resistance, and instill confidence in AI’s potential.

Employees have mixed feelings of trust when it comes to working with AI. A recent Accenture report notes that 60% of employees worry that gen AI may increase stress and burnout, 58% feel insecure about their jobs, and 57% need clarity on what this technology means for their careers.

At Propeller, we’ve seen similar dynamics with our clients. To address AI adoption challenges for an HR Tech client, we developed change management strategies that included leadership engagement, AI skills assessments, tailored communications plans, and flexible training programs. A key focus was tying employees’ work directly to business goals, ensuring they understood how their contributions aligned with broader organizational objectives. By focusing on how AI could enhance employees’ work—rather than replace it—we helped reduce anxiety, improve productivity, and accelerate tech adoption timelines.

Related Content: Navigating the Impacts of AI as a Change Management Leader

# 4. Accelerating Learning

Cultural shifts create the foundation and change management supports a smooth transition; a robust learning strategy equips teams with the skills to integrate these tools into daily workflows. Employees must be empowered with clear, actionable training to bridge the gap between interest and practical use.

In a sentiment analysis conducted with an HR Tech client, we found that while employees had a positive attitude about generative AI and expressed interest in using it, most were not utilizing it regularly—or at all. The enthusiasm was evident, but a lack of resources and support to translate interest into practical, everyday use was missing. Propeller addressed this by building an AI learning strategy with ongoing accessible training.

While AI may have been around for several years, it is still new to many employees, so concise, easy-to-digest training materials that demonstrate best practices and applicable use cases are essential. Effective learning programs must remain dynamic as AI continues to evolve. Training materials may quickly become outdated within months, so organizations should continually update resources to reflect advancements. Leaders are critical in driving these initiatives, ensuring employees stay prepared to scale AI effectively and responsibly.

Potential AI Learning Topics:

• Beginner Topics:
  • Generative AI best practices: Basics of AI, use cases, real-world applications, and key terminology.
  • Prompt engineering: The role of prompts in AI, writing effective prompts for various scenarios, and tips for writing bias-free and inclusive prompts.
  • Ethics & responsible AI: Identifying bias in algorithms, privacy concerns, and ethical considerations in applications.

• Advanced Topics:
  • Technical skills: Machine learning, natural language processing (NLP), cloud computing, and data science are foundational to AI.
  • Data-driven decision-making skills: Analyzing and interpreting AI-generated insights to inform decisions.
  • Interdisciplinary knowledge: Blending and upskilling knowledge in statistics, data science, ethics, business acumen, and domain-specific knowledge improves cross-functional expertise

# Conclusion: Operations, Culture, and Change Management as Catalysts for AI Success

Achieving lasting impact with AI requires more than deploying cutting-edge technology—it demands a holistic focus on the people and processes that will sustain its growth and utility. Organizations that thrive in an AI-driven world recognize that operational agility, cultural transformation, and robust change management are the pillars upon which true innovation is built. These elements not only drive AI success but also create a foundation for continuous learning, equipping employees with the skills and confidence to leverage AI effectively.

By putting people and processes at the heart of your AI strategy, you can position your organization to adapt and lead in a changing landscape. At Propeller, we’ve seen organizations succeed by taking intentional steps to unlock AI’s transformative power.

Need support? Connect with our team to discuss how we can help your organization build operational agility, foster cultural transformation, and create sustainable change management strategies that drive AI success.