Artificial intelligence is no longer a future-facing concept organizations are experimenting with on the sidelines. As the initial excitement surrounding generative AI begins to level out, organizations are entering a new phase, one focused on intentional strategy, governance, and measurable outcomes.  

During our annual Swerve event, Chris Auld, Vice President of Product at HCM Unlocked, explored how organizations are approaching AI adoption, where companies are finding measurable value, and why many are still struggling to move beyond early experimentation.  

One of the most important distinctions organizations are navigating is the difference between automation and AI. Automation is designed to follow fixed rules and workflows, producing the same output every time. AI introduces adaptability by analyzing information, identifying patterns, and determining the best course of action based on available data.  

This distinction matters because many organizations are still approaching AI as a simple productivity tool rather than rethinking how work gets done. Businesses often assume efficiency gains without clearly defining how success will be measured or how workflows should evolve alongside the technology.  

“Most companies aren’t measuring outcome” Auld noted when discussing AI adoption. Without clear measurement, organizations risk overestimating the impact of AI while failing to identify where it is actually driving meaningful results.  

The organizations seeing the greatest impact are not simply layering AI onto existing processes. They are redesigning workflows around it and rethinking how decisions are made, how work flows across teams, and where human oversight remains critical. 

AI applications are expanding rapidly across workforce management, operations, and customer-facing business functions. In human resources and talent management, organizations are using AI for candidate sourcing, interview scheduling, offer letter creation, turnover analysis, and personalized learning recommendations.  

Beyond HR, organizations are expanding AI use across operations, finance, infrastructure, and customer systems. However, not all use cases create equal value. While lower-level tasks like content generation or drafting communications can save time, the larger opportunity lies in predictive analytics, forecasting, and workflow orchestration across systems and departments.  

In many cases, the greatest value comes not from automating individual tasks, but from redesigning entire workflows to integrate AI into how work is completed end-to-end. This shift requires organizations to think more expansively about how work is structured, not just where AI can be added. 

As adoption expands, organizations are also recognizing the importance of governance. New roles focused on AI ethics, security, and oversight are emerging as businesses address risks related to bias, data privacy, and compliance.  

Successful AI implementation starts with process, not technology. Before organizations can determine where AI fits, they need to understand where data lives, how it flows their data ecosystem, what recurring business problems exist, and how current workflows operate.  

That often begins with identifying patterns within existing systems and operational challenges. For example, employee turnover may initially appear to be an HR issue, but deeper analysis utilizing AI analysis across multiple data sets could reveal patterns tied to seasonality, specific roles, or operational gaps. These insights can reshape both workforce strategy and business operations. 

The conversation around AI is also shifting toward employee enablement and AI literacy. As we surveyed our audiences, we discovered approximately 2/3^rd of companies have already deployed an enterprise AI/LLM solution such as Claude, Microsoft Co-Pilot, and ChatGPT. As tools become more embedded in daily work, organizations are encouraging employees to integrate AI into routine tasks such as summarizing information, analyzing data, critiquing ideas, and improving recurring workflows.  

These small, consistent use cases often become the foundation for broader transformation, helping employees build confidence and capability as AI becomes a more integrated part of the workplace.

As organizations mature their AI strategies, many are evaluating whether enterprise AI platforms are sufficient or whether custom AI solutions are necessary. 

Enterprise large language models offer broad accessibility, faster deployment, and built-in functionality that can support a wide range of users and departments. Custom AI solutions, meanwhile, provide greater control, auditability, and precision for organizations with highly specific operational needs or competitive differentiators. 

For many organizations, enterprise platforms offer a practical starting point, enabling quick adoption and early value. As strategies mature and use cases become more complex, some may explore custom solutions to better align AI capabilities with proprietary processes and long-term goals. 

AI adoption will continue to accelerate, but successful implementation depends less on the technology itself and more on how intentionally organizations align AI with their workflows, people and their specific roles, and business objectives.  

As a quick reference guide, here is a step-by-step approach Chris provided to drive success in AI adoption:  

Ultimately, organizations that take a thoughtful, process-driven approach grounded in clear measurement, strong governance, and a commitment to targeted employee enablement will be best positioned to move beyond experimentation and unlock sustained business value.