Human-Centric AI Strategy Helps Laboratories Balance Technology and People

As laboratories introduce AI into data analysis, inventory management, and quality systems, many still treat adoption as a technology project rather than a people strategy. A Forrester’s State Of AI survey found that 38 percent of AI decision-makers cite improved personal productivity as their organization’s top AI benefit, yet those gains depend heavily on human readiness and trust.

Forrester vice president J. P. Gownder stresses that “today’s AI remains intimately tied to human users,” meaning that deployment success hinges on how people interact with, understand, and monitor AI tools. Employees need both technical skills and ethical awareness—a combination the report calls “AIQ.” Without it, even well-intentioned initiatives stall.

Risks of ignoring people in your lab’s AI adoption

When labs rush to deploy automation or predictive tools without sufficient training, they risk creating new blind spots. Up to 60 percent of employees licensed for tools such as Microsoft 365 Copilot rarely use them, forcing organizations to reassign access or cancel contracts altogether.

Other warning signs include:

Skill gaps and misuse: Forrester notes that inadequate AI training leads to “quality breakdowns and ethical lapses,” especially when employees over-rely on generative outputs
Cognitive dependence: A referenced academic study found that frequent AI users showed lower critical-thinking scores, a pattern researchers labeled “cognitive laziness”
Low trust: Gallup data cited in the report show 75 percent of US workers believe AI will reduce jobs, and 77 percent don’t trust companies to use AI responsibly

In scientific environments, these perceptions can erode compliance culture and precision. Lab leaders must pair new systems with clear guardrails, retraining, and transparent communication about how AI supports—not replaces—scientists.

Turning AI into an opportunity for your laboratory team

To overcome skepticism, Forrester urges organizations to frame AI as an “opportunity builder.” In 2024, 56 percent of business and technology professionals reported their firms had at least one active generative-AI deployment, but success correlated with how employees saw personal benefit.

Practical steps include:

Showcase internal wins: Highlight small successes where AI reduces routine work or improves accuracy—such as automatic data transcription or experiment scheduling
Invest in career development: Redefine job descriptions to reflect emerging AI competencies and reward digital upskilling
Promote skill-building culture: KPMG’s Ruth Svensson told Forrester that “celebrating successes helps employees see that the work you put in to master these tools is genuinely worthwhile”

Building a culture of trust and learning around AI in labs

A human-centric culture ensures AI complements, rather than displaces, scientific expertise. Forrester identifies four pillars of AI-ready culture that translate directly to laboratory operations:

Shared purpose: Co-design AI tools with the scientists who will use them, ensuring they feel ownership rather than competition
Behavioral norms: Establish peer “AI champions” who model best practices and reinforce responsible use
Rituals: Host recurring learning sessions—one oil and gas firm saw success with lunch-and-learns where staff practiced Microsoft Copilot prompts
Artifacts: Maintain prompt libraries, visual SOPs, or internal dashboards showing measurable AI benefits

Embedding these habits helps scientists and technicians view AI as a collaborator that amplifies human judgment and discovery.

The human edge in an automated future

Technology may accelerate science, but people determine its impact. When laboratories build AI literacy alongside empathy, curiosity, and accountability, they create an ecosystem where innovation is both faster and more ethical. The most advanced model still needs what every lab already has—the human mind behind the machine.