Role-based design is key to scaling AI in the enterprise

<p><b>While a process-based approach may produce early wins, role-based AI implementation is the key to scalability. We offer 5 reasons why.</b></p>

<p>Businesses across industries are investing heavily in artificial intelligence, yet most struggle to scale these investments beyond isolated pilots. While headlines often spotlight major wins, the reality is that the vast majority of AI projects stall or fail to deliver meaningful ROI. <a href="https://32290571.isolation.zscaler.com/profile/aa0fc3ba-fc2b-4872-9833-83f26367d34b/zia-session/?controls\_id=c1798967-88a7-4a71-8f25-2529909e7876\&amp;region=cle\&amp;tenant=8be389a82b37\&amp;user=28e99b7a820d24d16553c4ba5c8a3e98442db757ae766a6ab34a751d5142a81f\&amp;original\_url=https%3A%2F%2Fwww.stack-ai.com%2Fblog%2Fenterprise-ai-strategy\&amp;key=sh-1\&amp;hmac=78ba1748ee92d61ac4129e7e66a890d01e8fab77145f0596f6963d041fa132b6" target="_blank">Recent studies</a> show that over 30% of generative AI projects are abandoned at the proof-of-concept stage, and even among completed projects, <a href="https://mlq.ai/media/quarterly\_decks/v0.1\_State\_of\_AI\_in\_Business\_2025\_Report.pdf" target="_blank">95% fail to deliver tangible returns</a>. Only a <a href="https://32290571.isolation.zscaler.com/profile/b688ce7a-c084-4fef-927c-8ed9028178d0/zia-session/?controls\_id=a6413ccb-c790-44a0-9d78-03b61fe2267a\&amp;region=cle\&amp;tenant=8be389a82b37\&amp;user=28e99b7a820d24d16553c4ba5c8a3e98442db757ae766a6ab34a751d5142a81f\&amp;original\_url=https%3A%2F%2Fcdn.openai.com%2Fbusiness-guides-and-resources%2Fidentifying-and-scaling-ai-use-cases.pdf\&amp;key=sh-1\&amp;hmac=4e2eda2587d847dd914a001d3ae4bd8d9c35914a4c80ca621be49bbfecea66c3" target="_blank">tiny fraction of organizations</a> believe their AI initiatives have reached maturity.</p> <p>Why the lack of success? Most organizations approach AI through a use case lens, identifying specific processes to automate or optimize. While logical, this strategy creates significant barriers to scaling. Each use case demands its own business justification, technical architecture and governance, resulting in a fragmented ecosystem of isolated solutions. This fragmentation prevents organizations from achieving the network effects and enterprise-wide impact that AI promises.</p> <h4>The role-based advantage: A human-centric framework</h4> <p>Role-based agentic design offers a change in thinking. Instead of asking, “What processes can AI improve?” organizations should ask, “How can AI enhance the effectiveness of specific roles across our business?” This shift from task-centric to human-centric design unlocks five critical advantages:</p> <h5><b><i><span class="text-bold-italic">1</span></i>.&nbsp; &nbsp; Tackling the human cost of complexity</b></h5> <p>In every industry, employees spend considerable time bridging gaps between fragmented systems, re-keying data, handling exceptions and translating between processes. These manual interventions drain productivity and increase operational risk. Traditional automation tools like robotic process automation work well for highly structured tasks but falter when processes become ambiguous or exception-prone.</p> <p>Role-based AI is fundamentally better suited to address this complexity. By embedding AI agents that understand the responsibilities, workflows and decision-making context of each role, organizations can alleviate friction precisely where it is most acute—at the intersection of technology, process and human judgment. This enables AI to bridge systems, guide exception handling and proactively surface insights, empowering employees to focus on higher-value, strategic activities.</p> <h6><b>Case study: Banking and financial services</b><br> </h6> <p><a href="https://www.businessinsider.com/wall-street-goldman-jpmorgan-bridgewater-using-ai-2023-12" target="_blank">When Goldman Sachs deployed</a> its AI-powered assistant to support investment bankers, it found the greatest value came from the agent’s ability to bridge gaps between legacy systems and streamline exception handling. Instead of automating only simple, repetitive tasks, the AI agent provided real-time insights and guidance tailored to the banker’s role, reducing manual work and operational risk.</p> <h5><b><span class="text-bold-italic">2</span>.&nbsp; &nbsp; Accelerated deployment through role consistency</b></h5> <p>Most organizations employ large numbers of people in similar roles across departments, regions or business units. By designing AI solutions around role archetypes rather than specific use cases, businesses can deploy proven capabilities horizontally with minimal customization. For example, a customer service agent in one division shares core responsibilities with agents elsewhere, even if the customer base or product focus differs. Role-based AI platforms can thus be rapidly scaled across the enterprise, driving adoption and efficiency gains.</p> <h6><b>Case study: Healthcare</b></h6> <p><a href="https://www.healthcareitnews.com/news/oracle-cleveland-clinic-g42-collaborate-ai-platform" target="_blank">At Cleveland Clinic</a>, a role-based AI platform was introduced for nursing staff across multiple departments. By focusing on core responsibilities shared by nurses regardless of specialty or location, the AI assistant could be rapidly deployed enterprise-wide. This led to faster adoption and improved patient care, as nurses received consistent, context-aware support for documentation, medication management and patient monitoring.</p> <h5><b><span class="text-bold-italic">3</span>. Systematic opportunity identification</b></h5> <p>Role-based implementation naturally surfaces new AI opportunities through “role adjacency mapping.” Once AI capabilities prove valuable for one role, organizations can systematically identify other roles that could benefit from similar enhancements. This creates a repeatable, scalable methodology for expansion, moving beyond ad hoc discovery and enabling continuous innovation.</p> <h6><b>Case study: Life sciences</b></h6> <p><a href="https://www.fiercebiotech.com/sponsored/precision-matters-elevating-ai-excellence-medtech-and-pharma-wcgs-avoca-quality" target="_blank">A leading pharmaceutical company</a> initially deployed AI agents to assist research scientists with literature review and data analysis. After seeing rapid productivity gains, the company mapped adjacent roles—such as regulatory affairs and clinical trial managers—and extended the AI platform to support these functions. This systematic approach enabled continuous innovation and cross-functional value.</p> <h5><b><span class="text-bold-italic">4</span>. Simplified business case development</b></h5> <p>Perhaps most significantly, role-based implementation dramatically simplifies business case development. Instead of calculating ROI for dozens of disparate use cases, organizations can model value at the role level and multiply by the number of employees in that role. This approach enables more straightforward cost-benefit analysis and clearer scaling economics. When AI saves each employee in a given role several hours per week, the aggregate impact across the organization becomes immediately apparent.</p> <h6><b>Case study: Manufacturing</b></h6> <p><a href="https://storydesign.industryweek.com/Global/FileLib/Siemens/Maintenance\_4.0\_whitepaper.pdf" target="_blank">Siemens implemented</a> a role-based AI solution for its plant maintenance engineers. By quantifying the time saved per engineer and multiplying across global operations, the company was able to present a clear, compelling business case for scaling the AI platform. The result was a significant reduction in downtime and maintenance costs, with ROI calculations that were straightforward and persuasive to leadership.</p> <h5><b><span class="text-bold-italic">5</span>. Targeted and sustainable change adoption</b></h5> <p>Role-based AI fundamentally transforms change management. By aligning AI deployment to clearly defined roles, organizations can directly target change efforts to affected stakeholder communities. Employees see immediate, practical relevance in new tools tailored to their daily responsibilities, resulting in faster onboarding and reduced resistance. Moreover, the impact of AI can be measured and reported at the role level, providing transparency and fueling ongoing engagement as success stories motivate broader adoption.</p> <h6><b>Case study: Communications and media</b></h6> <p><a href="https://trewknowledge.com/2025/05/12/how-ai-is-reshaping-editorial-workflows-in-high-volume-content-production/" target="_blank">The Washington Post developed</a> a role-based AI assistant for its content editors. Because the AI was tailored to the editors’ daily workflows—suggesting headlines, checking compliance and automating routine publishing tasks—adoption was rapid and sustained. Editors reported higher job satisfaction and productivity, and the company saw a measurable increase in content output and quality.</p> <h4>The strategic imperative: From efficiency to transformation</h4> <p>While use case and process-based approaches may generate initial AI wins, role-based implementation provides the most scalable path to enterprise-wide transformation. Organizations that embrace this approach will find themselves better positioned to achieve the three critical scaling objectives that define AI success: accelerated deployment, systematic opportunity identification and simplified business case development.</p> <p>The question for industry leaders is no longer whether to invest in AI, but how to structure that investment for maximum scalability and impact. The answer increasingly points to putting roles, not cases, at the center of AI strategy.&nbsp;</p>