Faced with a market where the promise of artificial intelligence has been overshadowed by widespread employee fear and stalled adoption rates, Salesforce has unveiled a comprehensive new strategy designed to shift the corporate conversation from human replacement to human collaboration. The company’s newly launched “AI Fluency Playbook” serves as a direct response to a significant slowdown in enterprise AI integration, aiming to provide organizations with a practical roadmap for preparing their workforce to confidently partner with AI. This pivot moves the focus away from a purely technological solution and towards a more nuanced approach centered on organizational change, employee sentiment, and a structured development of new, collaborative skills. The initiative signals a maturation in the industry’s understanding of AI, recognizing that its successful implementation hinges less on the sophistication of the algorithm and more on the readiness of the people who will use it.
The Root of the Problem From Hype to Resistance
The Initial Backlash
The corporate world’s journey with agentic AI began with a narrative that, in hindsight, proved counterproductive. Throughout 2024, technology vendors aggressively marketed advanced AI as the definitive answer to persistent labor shortages, framing it not just as a tool to augment human workers but as a direct path to eventually replacing them. This positioning, intended to highlight efficiency and cost savings, instead sowed seeds of deep-seated anxiety and distrust among employees. The prospect of being rendered obsolete by an algorithm created a culture of fear, turning potential AI advocates into staunch skeptics. This sentiment was further inflamed by high-profile corporate actions and statements that seemed to confirm the workforce’s worst fears. A pivotal moment came in September 2025 when Salesforce CEO Marc Benioff commented on cutting 4,000 support positions, attributing the reduction to new AI efficiencies. Though later clarified, the statement resonated powerfully across the industry, validating the pervasive concern that AI’s primary purpose was job elimination and solidifying a significant wave of employee resistance against its adoption.
The initial vendor-driven messaging created a clear and adversarial dynamic between employees and the very technology meant to empower them. Rather than viewing AI as a potential assistant or collaborator, many workers began to see it as a direct threat to their livelihood and professional identity. This perception was not unfounded, as the rhetoric of replacement dominated early discussions, focusing almost exclusively on reducing headcount and automating tasks previously handled by humans. The resulting atmosphere was one of skepticism and resistance, where employees were hesitant to engage with, learn, or champion AI tools. This resistance was not merely passive; it manifested in low engagement with pilot programs, a reluctance to integrate AI into established workflows, and vocal concerns raised in internal forums. The narrative of efficiency had backfired, creating a significant human barrier to technological progress. Instead of a smooth integration, companies faced an internal cultural battle, stalling projects and diminishing the potential return on their significant AI investments before they could even get off the ground.
A Market Hits the Wall
The pervasive employee ambivalence and corporate skepticism quickly translated into a tangible and measurable slowdown in the growth of AI adoption across the enterprise sector. The trend was not anecdotal; it was substantiated by compelling data from respected institutions. A landmark report from Stanford University highlighted a significant quarterly decline in the use of generative AI at work among American professionals, with usage dropping from 46% in June 2025 to just 37% by September 2025. This downturn indicated that the initial curiosity and experimentation phase was not converting into sustained, long-term integration. Compounding this finding, research from the Federal Reserve Bank of St. Louis revealed that the daily use of generative AI in the workplace saw almost negligible growth. The year-over-year increase was a mere 0.5%, bringing the total to a disappointing 12.6% by August 2025. Together, these statistics painted an undeniable picture of a market that had collided with a formidable wall of resistance, forcing vendors and enterprise leaders alike to confront the failure of their initial implementation strategies.
This “one-two punch” of a resistant workforce and hesitant corporate buyers created a feedback loop that choked the market’s momentum. Employees, fearing for their jobs, were unwilling to embrace the new tools, which in turn meant that companies failed to see the promised productivity gains from their AI investments. This lack of tangible results made executive sponsors and budget holders increasingly cautious about approving further spending on large-scale AI deployments. The hesitation was rational; without a clear path to successful adoption and a positive return on investment, the technology remained a high-cost, high-risk proposition. The industry had reached an impasse where the technology itself was advancing rapidly, but the human and organizational components necessary for its success were lagging far behind. It became evident that simply deploying powerful software was insufficient. A new approach was desperately needed—one that addressed the root causes of the resistance and provided a clear, structured path for integrating AI in a way that empowered employees rather than threatening them.
Salesforce’s Solution The AI Fluency Framework
Shifting the Focus to Human Collaboration
In response to the stalled adoption, the AI Fluency Playbook introduces a deliberate, human-centric framework designed to guide organizations through the complex process of cultural and operational change. The model is structured around a three-stage progression that prioritizes employee readiness before technical implementation. The foundational stage, AI Engagement, directly confronts the primary barrier to adoption: negative employee sentiment. This initial phase is focused on understanding and addressing the workforce’s attitudes, fears, and willingness to interact with AI technologies. It involves transparent communication, education about the benefits of AI as a collaborative partner, and creating a psychologically safe environment where employees can experiment without fear of replacement. By starting with the human element, the framework aims to transform skepticism into curiosity and build a solid foundation of trust upon which further integration can be built. This stage acknowledges that without buy-in from the people who will use the tools every day, any AI initiative is destined to struggle.
Once a baseline of employee engagement is established, the framework proceeds to the subsequent stages of AI Activation and AI Expertise. AI Activation marks the shift from theoretical acceptance to practical application, focusing on the seamless integration of AI into core, daily workflows. This stage is about identifying high-impact, low-friction use cases where AI can deliver immediate value, helping employees see the technology as a helpful assistant rather than a complex burden. The final and most aspirational stage, AI Expertise, represents the pinnacle of human-AI synergy. Here, the goal is to cultivate an environment where the unique strengths of humans—such as adaptability, critical thinking, emotional intelligence, and complex problem-solving—are powerfully combined with the computational and data-processing capabilities of AI. This fusion is intended to unlock a level of productivity, creativity, and innovation that neither humans nor AI could achieve alone. The playbook positions this as the ultimate objective: not just using AI, but collaborating with it to redefine how work gets done and achieve unprecedented business outcomes.
Defining the Evolving Roles of AI
To further demystify the integration process, the playbook complements its three-stage fluency model by identifying four distinct and evolving roles that AI can assume within the employee experience. This classification provides a clear and accessible vocabulary for organizations to map out their AI journey, allowing them to set realistic, incremental goals. The most fundamental role is AI as a Tool, where it functions at a basic level to perform specific, commanded tasks, such as generating text from a prompt or summarizing a document. The next level of sophistication is AI as an Assistant. In this more proactive role, AI helps manage and organize tasks, schedule meetings, or surface relevant information, effectively acting as a personal productivity aide. These first two roles are designed to be intuitive entry points, helping employees build confidence and familiarity with AI technology in a low-stakes environment. By starting with these more straightforward applications, organizations can demonstrate immediate value and gradually prepare their workforce for more advanced forms of collaboration.
As an organization and its workforce mature in their AI fluency, the technology can transition into more strategic roles. The third role, AI as a Contributor, marks a significant step forward, with AI beginning to provide substantive input and content within a workflow. For example, it might draft initial versions of reports, generate code snippets, or create marketing copy, moving beyond simple task execution to active participation in the creation process. The highest level of integration is AI as a Collaborator. At this stage, AI acts as a true partner in strategic thinking and complex problem-solving. It can analyze vast datasets to identify unseen trends, simulate the outcomes of different business strategies, or co-ideate on innovative solutions with its human counterparts. This progression from a simple tool to a strategic collaborator provides a tangible roadmap for companies, illustrating a path where AI integration becomes progressively deeper and more impactful, ultimately transforming not just individual tasks but the very nature of strategic work itself.
The Ultimate Finding Strategy Trumps Technology
The insights gathered from both Salesforce and its forward-thinking customers converged on a single, powerful conclusion: the success of any AI initiative was determined not by the sophistication of the technology, but by the strength of the organizational strategy behind it. Nathalie Scardino, Salesforce’s Chief People Officer, articulated this by emphasizing that the true differentiator was how organizations shaped their governance and integrated AI into core business processes. It was fundamentally about “changing how work gets done,” not simply deploying a new software tool. This perspective was strongly echoed by enterprise clients who had navigated the complexities of implementation. They found that AI’s potential could only be unlocked after foundational strategic work had been completed. This shared understanding marked a crucial maturation in the industry, shifting the focus from a tech-centric hype cycle to a more sober, strategy-driven approach that prioritized people and processes above all else.
This consensus was vividly illustrated by customer experiences. Pierre Matuchet of Adecco revealed that his company used the push for AI as a “pretext” to compel teams to refine and standardize their workflows, explaining that applying AI to chaotic or inconsistent processes was a recipe for failure. The technology served as a catalyst for much-needed operational discipline. Similarly, Greg Shewmaker of r.Potential offered the most concise summary of this viewpoint, stating plainly, “Technology is not a strategy.” He observed a clear divide in the market: companies that treated AI adoption as just another “software project” were consistently struggling, whereas those that viewed it as a fundamental transformation in “organizational capability” were the ones achieving success. Ultimately, the central finding was that a thoughtful, human-centric strategy, built on a foundation of standardized processes and a clear vision for the future, was the essential prerequisite. Only then could AI technology be effectively layered on top to deliver on its promise for both the business and its employees.
