QKS VoC Resonance™: How Enterprises Are Putting AI to Work

Adoption, industry use cases and investment priorities shaping enterprise AI

The next phase of enterprise AI will be defined by the business processes organizations are prepared to entrust to it. From supporting employees and retrieving knowledge to resolving customer requests and coordinating operational tasks, AI is taking on a broader role in how work gets done. That expansion brings greater expectations for reliability, integration and measurable business value.

QKS VoC Resonance™ examines this evolving landscape through enterprise adoption patterns, deployment constraints and investment priorities. The report connects interest in emerging capabilities with the practical conditions required to put them into operation, offering a perspective on where AI can expand and what determines its ability to scale. 

How AI priorities differ across industries

The path to operational AI depends on the environment in which it is deployed. In banking and insurance, use cases such as servicing, claims processing and document review place particular emphasis on traceability, data access and human oversight. In healthcare, knowledge retrieval, administrative coordination and patient communications require careful attention to information accuracy and sensitive data.

Retail and consumer businesses present opportunities across shopping assistance, customer service and order management, where AI must work with changing inventory, transaction and customer information. Manufacturing and logistics bring a different set of requirements through maintenance support, operational knowledge and exception management, where the ability to connect enterprise systems with operational context becomes central to execution.

Across these settings, the depth of integration and the consequences of an incorrect action shape how much autonomy a use case can support.

From individual use cases to connected workflows

Copilots and enterprise search help employees access information and complete individual tasks. Extending AI into business execution requires it to carry context across process steps, interact with systems, respect approval boundaries and recognize when human intervention is necessary.

This makes use-case selection a strategic decision. A clearly defined task with accessible data and an accountable process owner offers a different deployment opportunity from a workflow spanning multiple teams, applications and policies. The report explores adoption through this distinction, connecting the emergence of agentic AI with the operational foundations needed to support it.

What the report explores

• Adoption and maturity: The progression from employee assistance and knowledge retrieval to AI-assisted decisions, task execution and cross-functional orchestration.
• Industry and use-case relevance: How workflow complexity, information sensitivity and operational risk shape deployment choices.
• Barriers to scale: The implications of disconnected systems, fragmented data, governance requirements and organizational silos.
• Investment priorities: The relationship between spending on data, monitoring, skills, deployment tools and agentic orchestration.
• Implications for buyers and providers: The capabilities enterprises should evaluate and the operational requirements technology providers need to address.

For enterprise leaders, the report provides a framework for connecting AI ambition with deployment priorities. For technology providers, it offers a perspective on the requirements that influence buyer confidence, use-case expansion and sustained adoption.

Understand where AI can take on greater responsibility, and what enterprises need to make it work.