Blog 6 High-Impact Agentic AI Use Cases Executives Should Champion Today

AI agents are redefining the future of work, moving past simple automation to become autonomous forces within the enterprise.

AI agents are automating coding, business workflows, customer and internal support, HR, security, and analytics. These advanced agents are ready for high-impact deployment across critical business functions, from accelerating software development to fortifying cybersecurity and enhancing customer support.

This article dives into six strategic use cases where executives can champion AI agents to drive unprecedented efficiency, cut costs, and gain a significant competitive edge.

1. Software development and coding agents

AI agents are redefining what coding assistants can do. These next-generation tools go beyond autocomplete. They can write, review, debug, and test substantial blocks of code. They can even be used to reverse engineer specs from existing code and forward-engineer test cases. This evolution is giving rise to coding agents — autonomous collaborators that accelerate application development, improve code quality, and streamline DevOps workflows.

This leads to an agentic Software Development Life Cycle (SDLC), where systems can act independently, make their own choices, and control their actions and outcomes.

Tool-based agents, such as GitHub Copilot, enhance this ecosystem by integrating directly with IDEs, ticketing systems, and version control platforms to create a seamless development experience. These agents working together can provide development cycles that move 10x faster.

Insight uses GitHub Copilot as a key tool in this new SDLC. It’s an AI-powered coding assistant that provides context-aware code suggestions to help teams deliver faster and more efficiently. For instance, Insight's own adoption of GitHub Copilot with around 450 developers led to a 20% reduction in development time. GoTo (formerly LogMeIn), reported a 30% reduction in development time after rolling out GitHub Copilot to approximately 1,000 developers.

Success with agentic software developing and coding agents requires more than the right tech — it takes meaningful change management.

That means today’s developers need more than coding skills — they need the ability to collaborate with, teach, and learn from AI.

2. Workflow automation agents

These agents move beyond simple Business Process Automation (BPA) or Robotic Process Automation (RPA) to handle complex problems requiring higher-level decision-making. These agents can handle complex, multi-step workflows that require adaptive decision-making. They respond to changing conditions, manage exceptions, and operate across diverse systems with minimal human oversight.

This includes Computer Using Agents (CUA), which can navigate web interfaces and applications like a human user, and tool-based agents, which execute tasks within platforms like Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), and Business Intelligence (BI) systems. Together, these agents streamline operations across departments by automating routine processes such as approvals, data syncing, employee onboarding, procurement, and marketing flows.

In real-world use, organizations are applying these agents to turn meeting notes into project tickets, trigger supplier orders based on demand forecasts, and automate workflows in customer service, supply chain logistics, and IT operations. The result is greater agility, reduced manual effort, and improved cross-system efficiency, a key advantage as businesses scale AI adoption.

3. Customer support automation

Customer support automation agents are evolving far beyond basic chatbots.

Today’s AI agents can manage complex, adaptive decision-making processes without human intervention. This includes sentiment-aware chat agents that detect emotional tone, omnichannel agents that manage queries across email, chat, and social platforms, and voice agents that handle real-time conversations using natural language processing NLP. These agents can resolve common issues, escalate cases, and deliver consistent support across touchpoints.

Designed to enhance both efficiency and customer satisfaction, AI agents have been shown to raise conversion rates and reduce bounce in e-commerce, cut average handling time 9%, and accelerate complaint resolution.

Voice agents manage live calls with conversational fluency. Tool-based agents triage tickets, respond via chat, email, or Slack, and provide fast, context-aware answers.

Together, they offer scalable, intelligent, and empathetic support — handling everything from billing inquiries and transaction disputes to nuanced tasks like identifying the account with the highest balance.

See it in action: When a leading retailer needed to scale its call center, Insight answered the call with Azure OpenAI. Discover how we built a generative AI voice assistant.

4. Enterprise & HR workflow automation

Within HR, administrative AI agents can now handle tasks like admissions and scheduling, while recruitment agents screen candidates and manage interview logistics. Onboarding agents guide new hires through training, setup, and documentation, reducing the burden on HR teams. Internal knowledge agents provide employees with instant answers about company tools, policies, and workflows, reducing the need for support tickets and improving response times.

These capabilities are made more powerful through agentic RAG systems, which pull directly from trusted internal knowledge bases to ensure accurate, contextual responses. Tool-based agents integrate seamlessly with enterprise platforms like Jira, Workfront, Confluence, and ServiceNow to populate systems, route tasks, and trigger actions without manual intervention.

The result is a boost in operational efficiency. AI agents streamline the hiring process, improve candidate experience, and enhance onboarding — factors that also contribute to employee retention. Across the enterprise landscape, companies are increasingly embedding AI agents into platforms like ServiceNow and Salesforce to support scalable, intelligent workforce operations.

5. Cybersecurity and threat detection

AI agents are becoming critical front-line defenders in cybersecurity and fraud prevention, capable of monitoring systems, detecting anomalies, and responding to potential threats in near real-time. In finance and insurance, fraud detection agents analyze transactions, claims, and customer histories to flag suspicious behavior. Tool-based agents operate within security dashboards to automate incident response, while UI interaction agents can navigate across multiple systems to isolate and contain threats quickly.

These agents offer a smarter, faster alternative to traditional monitoring systems. They improve security posture while freeing up security teams to focus on complex issues. They also drive efficiency and cost savings by automating labor-intensive processes like compliance checks, incident triage, and report generation.

Beyond threat detection, AI agents are being used to monitor financial activity for anti-money laundering (AML) and know-your-customer (KYC) compliance. They also enable adaptive security protocols that personalize defenses based on user behavior or environmental signals. As regulatory pressure increases and cyberthreats grow more sophisticated, AI agents are becoming essential to modern enterprise security strategies.

6. Business intelligence & data analysis

AI agents are automating reporting workflows. These agents go far beyond static dashboards, using natural language interfaces and real-time retrieval to make analytics accessible across the organization. Whether it’s advising marketing teams on budget allocation, forecasting sales demand, diagnosing medical conditions based on patient data, or predicting machine breakdowns for preventive maintenance, AI agents help convert data into decisive action.

Agentic RAG systems retrieve and synthesize information from internal data sources, delivering concise, relevant answers in natural language. UI interaction agents can navigate BI tools and dashboards to extract and summarize information, while voice agents provide conversational access to insights, enabling non-technical users to ask complex questions and receive intuitive responses. These capabilities help democratize analytics, empowering more employees to interact with data directly without relying on specialists.

Tools like Databricks’ Generative AI capabilities take this further, enabling AI-driven business intelligence through these development patterns. As a Databricks Elite Partner, we’ve had the opportunity to trade learnings and share real-world outcomes — adding even more value to client engagements.

By reducing the friction in accessing and interpreting data, AI agents directly impact key outcomes like profitability, operational efficiency, and patient care by enabling more timely, data-driven decisions. Their ability to disambiguate vague queries also makes data interaction more intuitive, helping organizations unlock the full value of their information assets.

Championing the AI agent workforce

AI agents are no longer experimental — they’re becoming foundational infrastructure for modern enterprises. Leading organizations aren’t waiting. They’re already piloting high-impact use cases and integrating AI agents into core business functions, from software development to cybersecurity and HR.

To unlock their full potential, organizations must address challenges head-on: ensuring data quality, breaking down silos, managing change, and fostering a culture of trust and responsible governance. It also means thinking holistically about designing systems where agents don’t just act in isolation but work in concert across functions and platforms.

These six use cases aren’t abstract theory — they’re being delivered today through a diverse ecosystem of voice agents, RAG agents, UI navigators, coding copilots, and tool-specific assistants. They’re fast, adaptive, and ready to scale. As agent orchestration evolves, we’re likely to see multi-agent systems coordinating across departments — enabling AI not just to act, but to collaborate.

This is how you gain momentum. This is how you build operational efficiency. This is how your teams develop competency and confidence in using AI.

While others are still experimenting with one-off solutions, your organization can be orchestrating a fully agentic AI workforce — turning automation into a strategic advantage.

Gain your competitive advantage. We help you turn vision into value — with speed and precision.