Fostering Innovation While Implementing AI

Despite leaders' excitement about the prospective benefits of AI, the outcomes often fall short of expectations. Why? My latest Gallup story explores the role of trust. It's easy to see the rapid adoption of AI across organizations, but where are the results? A large body of empirical economics research emphasizes that technology performs best when it complements, rather than replaces, human effort. Productivity gains from innovation depend on people-first strategies, e.g. reskilling workers, reorganizing workflows, and fostering trust. As Erik Brynjolfsson put it, “Awesome technology alone is not enough.” True gains come when companies evolve their business models and empower their people alongside the tools - not just procuring the tools. Whereas automation was fundamentally about displacing human effort, AI allows for the possibility of augmentation. And yet, many firms are missing the mark. While 93% of CHROs say their company is exploring AI, only 15% of employees report receiving clear communication about how it fits into their roles. What if the gap wasn't technological, but rather organizational? One of my papers from several years ago using Gallup data with Joo Hun Han - link in comments - showed that technological change has a positive effect on worker well-being, but particularly when employees believe their managers create trust in the workplace. Put simply, there's less scope for creativity and experimentation when there's a lack of trust. As a result, here are some practical recommendations: 1) Invest in cognitive resilience: Equip teams not just with technical know-how, but with the adaptability and mindset to grow with the tools. 2) Redesign work: AI needs more than plug-and-play. Rethink jobs to offload repetitive tasks and let people focus on complex, human-centric work. 3) Build trust and curiosity: Involve employees early. Show that AI is an enhancer, not a threat. When people feel ownership, adoption follows. The message can sound simple, but obviously AI integration and implementation is not easy. The organizations that truly unlock the value of AI, however, are likely the ones that use it to augment human potential and create new sources of value creation, rather than just efficiency improvements. So, AI will not determine the future of work - leaders will, based on whether they build cultures where innovation elevates human potential. What do you see as the barriers to effective AI integration in organizations? And where do you think the specific areas for greatest value creation reside with AI in the workplace? #AIProductivity #FutureOfWork #HumanAICollaboration #Leadership #OrganizationalDesign https://lnkd.in/ek74dAFs

Research with front-line workers, team leaders, and executives shows how they are using #AI and perceive it's usage across their organization. The excerpt in this #ChangeSuccessInsights newsletter provides an analysis across nearly 100 pages of research to distill out the 10 critical insights about how AI is being adopted in organizations. It identifies the key patterns in the data you can leverage, or need to address, to drive greater #AIAdoption and outcomes. 10 Critical Insights About Individual AI Adoption from Prosci Research: 1. Mind the AI Perception Gap - Leaders trust AI, frontline workers hesitate—bridging this gap builds confidence. 2. Prioritize Human Factors in AI Adoption - Technical issues are the minority; trust and training drive true AI success. 3. Balance AI Access Control with Innovation - Excessive restrictions stifle progress; smart access fuels responsible creativity. 4. Distribute AI Expertise Throughout the Organization - AI thrives when knowledge is shared, not concentrated in a few hands. 5. Maintain Transparency in AI Decision-Making - Clear communication about AI choices transforms suspicion into trust. 6. Focus on Role-Specific AI Value Creation - Executives seek efficiency, frontline teams find creativity—tailored AI delivers impact. 7. Build Trust Through Ethical AI Framework - Strong ethical foundations stabilize AI adoption amid evolving expectations. 8. Enable Transformative AI Change - Incremental steps won’t unlock AI’s potential—think boldly, act intentionally. 9. Balance Leadership Vision with Bottom-Up AI Innovation - A strong strategy from above thrives with creativity from below. 10. Prepare for Continuous AI Evolution - AI won’t stand still—adaptive plans outperform rigid strategies. These are the goalposts for better AI implementation and adoption, derived from the experiences and perspectives of over 1000 respondents. The full report will be added to Research Hub soon, and Prosci advisors always here to help you activate the research in your context. Reach out or tune in. And huge thank you to Scott Anderson, PhD for the amazing work on the research.

AI field note: Reducing the 'mean time to ah-ha' (MTtAh) is critical for driving AI adoption—and unlocking the value. When it comes to AI adoption, there's a crucial milestone: the "ah-ha moment." It's that instant of realization when someone stops seeing AI as just a smarter search tool and starts recognizing it as a reasoning and integration engine—a fundamentally new way of solving problems, driving innovation, and collaborating with technology. For me, that moment came when I saw an AI system not just write code but also deploy it, identify errors, and fix them automatically. In that instant, I realized AI wasn’t just about automation or insights—it was about partnership. A dynamic, reasoning collaborator capable of understanding, iterating, and executing alongside us. But these "ah-ha moments" don’t happen by accident. Systems like ChatGPT or Claude excel at enabling breakthroughs, but it really requires us to ask the right questions. That creates a chicken-and-egg problem: until users see what’s possible, they struggle to imagine what else is possible. So how do we help people get hands-on with AI, especially in enterprise organizations, without relying on traditional training? Here are some approaches we have tried at PwC: 🤖 AI "Hackathons" or Challenges: Host short, low-stakes events where employees can experiment with AI on real problems. For example, marketing teams could test AI for campaign ideas, while operations teams explore process automation. ⚙️ Sandbox Environments: Provide low-friction, risk-aware access to AI tools within a dedicated environment. Let users explore capabilities like text generation, workflow automation, or analytics without worrying about “messing something up.” 🚀 Pre-built Use Cases: Offer ready-to-use templates for specific challenges, such as drafting a client email, summarizing documents, or automating routine reports. Seeing results in action builds confidence and sparks creativity. At PwC we have a community prompt library available to everyone, making it easier to get started. 🧩 Embedded AI Mentors: Assign "AI champions" who can guide teams on applying AI in their work. This informal mentorship encourages experimentation without formal, structured training. We do this at PwC and it's been huge. ⚡️ Integrate AI into Existing Tools: Embed AI into everyday platforms (like email, collaboration tools, or CRM systems) so users can naturally interact with it during routine workflows. Familiarity leads to discovery. Reducing the mean time to ah-ha—the time it takes someone to have that transformative realization—is critical. While starting with familiar use cases lowers the barrier to entry, the real shift happens when users experience AI’s deeper capabilities firsthand.