Don’t let AI strategy forget the people who make it work

A successful AI strategy must prioritize people over mere technological advances

Technology changes fast. What doesn’t change is the need for people, employees and customers, to drive its success. Too many companies chase the next big AI tool, hoping it will solve all their problems. That approach fails because technology alone doesn’t create sustainable progress. The real shift happens when you design your AI strategy around the humans who use it and are affected by it.

J.P. Gownder, VP and Principal Analyst at Forrester, warned leaders not to “hitch their wagon to one technology” and believe it will define the future. His point is simple: flexibility beats fixation. The best AI strategies evolve. They focus on the context in which people actually use technology, training, workflows, communication, and customer engagement.

Danny Fisher, CTO at West Shore Home, shared a practical example. His team built an AI system to handle customer support outside office hours. The result wasn’t about replacing people but expanding their capacity. Customers got real-time responses instead of voicemail delays, and employees could spend their time solving higher-value tasks. That’s the mindset leaders need, using AI to extend human capability.

For executives, this means looking beyond the hype. Choose AI tools that make your employees and customers stronger. Make the technology fit your people. Companies that do this build long-term resilience rather than chasing short-term ROI.

Transparency and proactive communication are crucial for maintaining employee trust amid AI-driven workforce changes

Workforce anxiety around AI is growing. Employees worry about automation cutting jobs, and that fear can quietly slow down transformation. A recent survey by the Stanford Institute for Human-Centered AI found that one in three companies expects AI-led workforce reductions in the next year. These numbers explain why employees are uneasy. Without clarity, fear drives resistance.

J.P. Gownder highlighted that “the base case for most employees right now is fear.” When staff operate from fear, productivity and innovation suffer. Leaders must confront this directly through transparency. This means explaining what AI will do, how it will affect roles, and what support systems are in place. It’s not enough to share a vision; employees must see a plan.

For business executives, communicating early and often is key. Share updates about new initiatives, involve teams in pilot projects, and offer forums for open discussion. This mindset shifts the narrative from uncertainty to shared purpose. When employees trust leadership, they become participants in transformation rather than spectators.

This is not just about morale, it’s a leadership strategy. Clear, proactive communication establishes credibility. Employees who understand the “why” behind AI are more likely to find ways to use it productively. For C-suite leaders, this clarity keeps teams aligned, reduces adoption friction, and builds operational confidence in the face of rapid technological change.

Social learning and structured educational initiatives

AI adoption fails when learning is limited to formal training sessions. For real impact, people need to learn from one another, through discussion, experimentation, and shared experience. J.P. Gownder, VP and Principal Analyst at Forrester, emphasized that social learning carries more weight than classroom-based instruction. When employees see how peers use AI in real work situations, they start to understand the value for themselves.

Companies that invest in continuous learning programs see faster adoption and fewer missteps. This can mean weekly team sessions where users share insights, short video testimonials explaining how AI has improved operations, or mentorship structures that pair early adopters with hesitant staff. These initiatives create a unified understanding of what AI can and cannot do, helping to close knowledge gaps before they become barriers.

For executives, the message is clear: learning strategies must evolve as quickly as the technology itself. Relying on one-off training efforts is not enough. Encourage networks within the company where employees can exchange ideas and real-world use cases. This keeps AI exploration grounded in results and helps scale success across the organization.

In practice, social learning builds a culture that is naturally curious and adaptable. When this mindset embeds itself across a company, AI adoption stops being a program run by the IT team and becomes a normal part of decision-making at every level. That is what operational maturity looks like in an AI-driven enterprise.

Measuring employee engagement with AI tools

Measurement drives improvement. The same applies to AI adoption. At West Shore Home, CTO Danny Fisher tracks “token usage”—a measure of how employees interact with the company’s AI tools, to see who’s engaging with the technology and how often. This data helps leadership identify power users, skill gaps, and areas where additional support is needed. It transforms vague assumptions about adoption into specific data that drives targeted learning and growth.

Fisher explained that these insights enable “very educated conversations” with employees and reveal skill clusters across the organization. Leadership can use this information to design teams more strategically and pair people with complementary abilities. The result is better collaboration and more deliberate progress in integrating AI into daily work.

For executives, tracking engagement metrics should go beyond compliance checks. It’s about understanding the relationship between people and technology. An open, data-informed culture encourages employees to experiment without fear of failure. The more transparent the adoption process, the easier it becomes to build trust and identify talent that can champion future AI efforts.

Culturally, encouraging openness to technology during recruitment also matters. When new hires already show curiosity and enthusiasm for using AI tools, organizations spend less time overcoming resistance and more time advancing capability. This combination of measurement and mindset sustains momentum, ensuring that AI delivers both productivity gains and a smarter, more engaged workforce.

Responsible AI utilization requires targeted training

AI can amplify productivity, but only if users know how to apply it responsibly. Poor use of automation leads to “workslop,” a term introduced by J.P. Gownder, VP and Principal Analyst at Forrester, to describe low-quality outputs that damage efficiency and credibility. When employees rely too heavily on AI tools without critical judgment, the result is often rushed, unreviewed content that adds more rework than value.

The issue is not the technology itself, it’s how people interact with it. Employees need to learn when to use AI and when to step back and apply human insight. Effective training should focus on context-based decision-making, emphasizing quality control, ethical use, and data accuracy. This ensures that AI enhances performance rather than creating noise that senior teams later need to clean up.

For executives, this means establishing clear standards for AI usage across all departments. Encourage teams to pause and question the relevance of AI recommendations before acting on them. Doing so builds a culture of responsibility and preserves the integrity of your brand’s output. It also ensures the organization maintains speed without sacrificing quality.

Responsible use of AI also helps protect long-term productivity. While automation can reduce manual effort, unchecked dependence on it can erode skills and critical thinking. Leaders should treat training as a continual process, not a one-time event. The more employees understand how to apply AI thoughtfully, the more their output reflects human intelligence, backed by machine precision.

Gownder’s advice is direct: employees should have that pause, the moment to ask if AI serves this particular scenario. That balance between human judgment and technical capability defines whether AI remains a productivity driver or becomes a liability.

Key executive takeaways

• Prioritize people before technology: Leaders should center AI strategies around employees and customers. Focusing on practical use cases and flexibility ensures long-term value and sustainable adoption.
• Build trust through transparency: Executives must communicate openly about AI’s role in the business. Clear plans, honest dialogue, and proactive communication reduce fear and strengthen workforce confidence during transformation.
• Invest in continuous learning: Combine formal training with peer learning to embed AI into everyday operations. Encourage knowledge-sharing and mentorship to accelerate adoption and support consistent skill development.
• Measure engagement and foster openness: Track usage data and participation to gauge adoption and identify skill gaps. Recruit and develop employees who are curious about technology to sustain momentum and innovation.
• Train for responsible AI use: Establish clear standards to prevent “workslop” and maintain quality. Empower employees to assess when AI adds real value, ensuring productivity gains align with organizational credibility and efficiency.