Leveraging Agentic AI for Success in Large Deals: Enhancing Efficiency and Innovation
Integrating artificial intelligence (AI) into automation has revolutionized various sectors, significantly boosting productivity and reducing costs. Automation has thus been a cornerstone of technological advancement for many years, streamlining processes and increasing efficiency. The advent of Generative AI (GenAI) represents a significant leap forward, empowering machines to execute pre-programmed tasks and create novel content, further enhancing automation capabilities. However, this is just the beginning. The emergence of Agentic AI promises to redefine automation, unlocking even greater potential for improvements in user experience, cost reduction, and overall productivity across diverse industries, including IT development and service desk operations.
The Challenge of Human Dependency
A significant challenge in AI-based automation is the often-necessary human-in-the-loop design for complex processes. While AI can generate recommendations and content and even execute actions, human intervention remains crucial for review, refinement, and feedback. This human dependency cycle times and hinder productivity gains, especially in processes involving multiple handoffs and requiring diverse skill sets. The need for cost reduction and improved user experience highlights the demand for more autonomous systems.
Enter Agentic AI Systems
Agentic AI systems, powered by Large Language Models (LLMs), are designed to plan and take actions to achieve goals iteratively. They can perform complex tasks requiring human-like understanding, reasoning, and decision-making. Agentic AI agents possess defined personas, assigned roles, access to tools, and the knowledge to utilize them effectively—much like humans. Unlike traditional AI, which follows predefined instructions, Agentic AI can interact with its environment, collaborate with other AI agents, adapt to new situations, and make autonomous decisions. Imagine a team of individuals with specialized skills working together towards a common goal; that's the power of Agentic AI. This leads to significant improvements in productivity and efficiency.
Common Use Cases for Agentic AI
In IT development and support, AI agents can function as developers, reviewing and refactoring code, creating test cases, and performing testing—use cases supported by existing solutions. Similarly, AI agents can augment service desk operations, handling initial interactions, triaging, assigning, and tracking tickets, monitoring SLAs, summarizing incidents, and generating RCA reports. This offers significant potential for cost reduction in IT development and service desk operations. Beyond IT, Agentic AI has broad applications across various domains, including banking (invoice processing, account receivables), insurance (risk assessment, policy issuance, claims processing), healthcare, retail, and logistics.
Evolution of the Solution Ecosystem
Major technology providers are rapidly integrating Agentic AI into their offerings. Microsoft (with AI Agents working alongside Copilot), Google (through its Vertex AI Agent builder), and Amazon (with Bedrock) are leading this charge. Platforms such as ServiceNow (with its AI Agents), SAP (incorporating AI Agents into its solutions), and Salesforce (with Agentforce) are also actively incorporating these capabilities. This widespread adoption underscores the growing importance of Agentic AI in improving user experience and driving automation across various sectors.
Is this the silver bullet?
Well, not quite. All the checks and balances required for AI based solutions are applicable to Agentic AI systems as well. Agentic AI systems rely on LLM’s, which can hallucinate and are prone to adversarial attack. Inter-dependent nature of Agentic AI systems can lead to faster, and at times deliberate propagation of failures in the automation chain, resulting in un-intended or harmful outcomes. Agentic AI systems need ethical and legal safeguards to be built-in, such as ability to monitor, audit, explain, control and when required, shutdown autonomous operations. As with the emergence and adoption of any new and disruptive technology, governance frameworks are evolving to ensure Agentic AI systems work as intended and have relevant guardrails in place to ensure ethical, legal compliance and mitigate associated risks.
Importance in Large Deals
We're witnessing a significant shift in customer priorities, with AI, GenAI, and automation increasingly central to RFPs. For the past year, numerous RFPs have explicitly prioritized using automation and GenAI to enhance user experience and drastically minimize human intervention. This reflects a broader trend; clients are actively seeking solutions leveraging AI and GenAI to achieve core business objectives or address the inevitable need to upgrade legacy IT systems. This focus on productivity and cost reduction through advanced automation will only intensify.
What Can We Do?
At a minimum, familiarity with the basic concepts of Agentic AI is crucial for identifying opportunities to integrate these capabilities into deals. Depending on your area of expertise, a deeper understanding of Agentic AI within specific ecosystems—such as hyperscalers (Microsoft, Google, AWS), ITSM/ITOM/workflow platforms (ServiceNow), and CRM/ERP solutions (Salesforce, SAP)—will prove invaluable. This knowledge allows for more objective discussions with Service Line experts, moving beyond generic AI/automation/GenAI statements to articulate specific interventions and measurable outcomes. This approach will result in a more cohesive, compelling, and forward-thinking overall proposition, enhancing both productivity and cost reduction for clients.
