Enterprise Playbook for Generative AI Adoption

Era of Generative AI and Agentic systems

The use of Generative AI (GenAI) started with the basic traditional use case which is generation of content. Simple applications and wrappers were built over Large Language Models (LLMs). These facilitated users interacting with LLMs and consuming the generated content. But this was just the beginning and the domain of GenAI evolved significantly from that preliminary use case. The concept of Tools enabled LLMs to fetch more context and interact with external environment. The ReAct (Reason and Acting) capabilities of LLMs combined with Tools led to the next generation of systems called Agentic AI systems. Agentic systems have unlocked significant capabilities that were previously unimaginable. These systems and capabilities are driving rapid transformation across all industries.

Enterprise organizations are looking at how to make best use of these Agentic capabilities. They are also preparing to make their services and offerings agentic ready to strengthen their leadership position in their respective domains. In this article, we will discuss an Enterprise playbook to help organizations in this journey. We will look at mainly two tracks. One track where organizations consume the existing AI services to get better. The other track is where organizations build AI applications or prepare their services to be consumed by agentic systems.

Generative AI for organizational productivity

Generative AI and agentic systems started playing a major role in improving user productivity tremendously. There are lots of use cases. Tons of tools and products are being built by companies, both startups and established software vendors, that help with productivity use cases. Let us look at some of the major use cases that are related to organizational productivity – related to employees and internal workflows.

content generation

Text content generation was the first use of LLMs, made popular with consumer apps like ChatGPT. Later, the sequence generation capabilities of LLMs were enhanced beyond language and made to support multi-modality. Multi-modal LLLMs can generate various forms of content – text, music, code, images, video etc. Models like Nano Banana from Google Gemini can create excellent images from user prompt. Models like Sora from OpenAI can create good detail videos based on prompts. This unlocks significant productivity for employees in media domain. And in areas like marketing where creating media-rich marketing content becomes instant. The capabilities are not just limited to new content. Models can edit or generate new content based on existing images. And the media tools have incorporated AI based creation and edit capabilities into their existing tools.

Another significant area of productivity improvement is the office suite of tools. Employees can now generate various forms of content using generative AI. Creating a detailed document from a few lines of input, generating a power point deck from an existing document etc. are some sample use cases. Additional scenarios include generating live transcription in meetings, creating summary notes and extracting action items for meetings etc. Application suites like Microsoft 365 and Google Workspace have made these experiences seamless by integrating AI into their core applications that are already being consumed by millions of users across the globe.

Coding assistants

Code generation is another form of content generation using LLMs that has picked significant popularity and adoption across the software organizations. The initial use cases were creating small pieces of code from natural language user input and vice-versa – generating language description of existing pieces of code. This got extended to generating test cases, fixing defects, performing code quality checks and suggesting review comments. Tools like GitHub Copilot, Gemini Code Assist etc. were built for this. Integrations of these AI assistants with existing IDEs have made the adoption easy for the developers.

With agentic AI, coding assistants have been transformed into coding agents. And have unlocked major use cases in the coding space beyond basic code manipulation. Coding agents can now create whole code repos, generate code for whole applications, and perform total testing. With help of tools, they can connect to GitHub to commit the application to repos, connect to cloud tools to perform direct deployments as well. With a simple and clear input of requirements, agents can handle the end-to-end flow to fulfill the whole software development life cycle. Coding agents have also found tremendous value in modernizing legacy code bases to latest tech stack for ease of maintenance, reducing a significant cost of ownership associated with legacy software. Codex from OpenAI, Cursor and Windsurf are some popular coding agents that have made the concept of vibe coding popular and productive.

Enterprise knowledge search

In any organization, information is spread across various data sources – could be stored in wikis, GitHub docs, dedicated sharepoint sites, files shared in MS-Teams chats, sharepoints linked to Teams locations etc. There will also be employee’s own content stored in either local laptop storage or cloud storage connected to employee’s account. The forms of data could also be numerous like .doc files, .pdf files, meeting notes stored in some text files. With this sprawl of data, finding the right relevant information with minimal effort becomes a challenge.

With generative AI and integrated ecosystems like Microsoft 365, intra knowledge search becomes instant. Employees can conversationally query for information from across their personal internal storage and enterprise data sources. And get summarized information along with the source links provided for further analysis. The best part is that these integrations also enforce the enterprise level authentication and authorization by linking with existing access control databases. This prevents unauthorized data being made available to users.

Content processing

In an organizational ecosystem, lot of content gets generated, transferred and consumed. Generative AI can be very helpful in making this content handling efficient. Imagine an executive trying to read an email thread with twenty back and forth conversations between his team. He can use AI assistant to summarize the twenty mails and give a crisp five-point summary and any action items that need his decision. Similarly, imagine an employee created an elaborate word document on a topic. But he needs to present this content for review in a meeting. Converting text from the document to a slide deck, along with pictures and charts to visually present the narrative, will usually be a time-consuming activity. But this becomes an instant activity using AI assistants supported by multi-modality capabilities of LLMs that can generate images as well.

Workflow agents

The concept of Tools combined with the power of LLMs has led to the innovation of AI agents and agentic systems. Tools enable LLMs to fetch additional information (latest information, domain specific data, enterprise private data etc.) as well as execute tasks or code so actions can be triggered in external environment. Protocols like Model Context Protocol (MCP) have standardized the approach of building tools enabling faster, seamless integration. Organizations across domains have embraced this and started making their product capabilities available as MCP tools. Agents empower systems to autonomously achieve goals by performing tasks. And the availability of powerful tools has unlocked the possibility of creating agentic systems that can handle complex tasks and workflows without needing human intervention. This results in reduced human efforts to create and manage workflows, improved productivity and significant cost savings for organizations.

Consider an example of an agent built in context of software development life cycle (SDLC). A successful pull review for a feature into a branch can be input to the agent. The agent with help of tools can execute relevant test suites. On successful testing, it can create a build, deploy to certification environment. Agent itself can change the status of the feature to “ready for release” in Agile tool, send email to product owner and to the quality assurance team for final check before moving the build to production. This is the potential of the powerful capabilities and possibilities using AI agents. Most recent applications like Claude Cowork, OpenClaw and Perplexity Computer are great examples of the potential of AI agents.

Enterprise readiness for AI-native world

There are lot of companies building in the agentic AI space across the spectrum of the stack. Companies like Nvidia are building the hardware. There are companies (OpenAI, Anthropic, Meta etc.) building foundational models and LLMs – both open source and closed source. Cloud providers are building platforms to provide compute, host LLMs for their customers. Companies are building pure GenAI applications (ChatGPT, Perplexity etc.). And some are providing solutions across the ecosystem. For example, Google builds TPUs in hardware space, has Gemini LLMs, provides Vertex AI cloud platform and also builds applications like Gemini Assistant in the GenAI space.

Beyond these companies building purely for the Gen AI stack, we will look at how traditional organizations can build solutions for the AI-native world.

Building generative AI solutions

One preliminary use case where generative AI has found tremendous value across domains and organizations is in knowledge search and customer assistance. AI assistants, Question and answer systems are connected to internal enterprise knowledge repositories. Depending on the user input these assistants can fetch relevant information, summarize and provide a crisp summary. Advanced capabilities like conversational agents with memory, seamless support for multilingual capabilities and omni-channel support (text, voice, messaging etc.) significantly enhance the end user experience. AI platforms provide Platform-as-a-Service (PaaS) model for creating agents with these capabilities reducing the work needed for organizations to add these assistants to their products. A typical use case could be a voice assistant embedded in a food delivery application helping users with any of their queries.

Agentic AI added to these assistants turn them into even more productive agents. These support agents can not only provide information but can also complete tasks to fulfill user requirements. With the help of tools available they can invoke complex workflows to meet the goal. For example, the AI support agent embedded in a food delivery application can complete task when user requests for cancellation of existing order. It can coordinate and complete workflows like cancelling the order, initiating money refund, sending email confirmation with details etc.

Apart from generic use cases, organizations should look at their domain specific problems and use cases that can be solved by leveraging generative AI. The capabilities of LLMs can be used to solve problems or requirements that were traditionally either time-consuming or resource intensive or cost intensive. Agentic AI systems enable workflow automation with minimal efforts. A lot of complex, time-consuming, multi-step workflows can be easily transformed into agentic systems.

Building AI enablers

Apart from the aspect of building generative AI solutions, the other area where traditional organizations can add significant value is by making their domain specific services ready for consumption in the AI-native world. The existing services and capabilities can be modernized for integration with the new age generative AI applications and agentic AI systems. Agentic systems are powered by tools. Organizations are publishing their core functional capabilities as tools and making them available via MCP servers. And third-party integrators or AI service providers can build their agentic systems by connecting to these MCP servers. This has the dual advantage of the traditional organizations focusing on their core capabilities and at the same time make them available for all the new AI-native applications being built.

Adopting AI-first culture

To succeed in this AI-native world, organizations need to adopt AI-first culture. Adopting agentic AI cannot be a check list initiative, rather it must be an organizational mindset embraced from top to bottom in the organizational hierarchy. The strategy needs to be both ways – top to bottom and bottom to top.

Leaders need to make decisions on business strategies to win in the agentic AI world. Decisions on where to make strategic investments, whether to build solutions or enter into partnership with new age startups or make acquisitions that provide instant advantage. Executive leadership also needs to plan to make all the employees, across all functions, AI ready. Investments should happen in employee up-skilling, regular training and knowledge sharing sessions, evaluation and procurement of tools that improve employee productivity etc.

Employees and engineering teams working closer to products should have the AI-first thought process. Teams need to embrace all the agentic AI tools, applications and workflows that are resulting in tremendous productivity gains across the software development life cycle (SDLC). Teams can identify what product areas can be improved with the agentic AI solutions. As part of customer discussions or requirement brainstorming, using AI-first thought process, teams can identify novel solutions that can be built that address customer pain points and bring in additional revenue.

Finally, there should be channels of continuous communication and collaboration – leadership sharing their vision and plans, employees sharing the feedback on any internal initiatives rolled-out, customer feedback on external AI solutions deployed etc. Considering the novelty and the pace at which agentic AI space is evolving, a constant feedback loop and an iterative approach of build, measure and learn will help organizations succeed by quickly pivoting to new strategies as needed.

Final thoughts

Generative AI and Agentic AI have unlocked significant capabilities which are driving tremendous transformations across all industries. Organizations need to adapt to these and keep up with this rapid pace not just to thrive but even to survive these waves of changes. Having an enterprise AI strategy and a play book for adoption and pivoting quickly to any changes becomes critical for business. And making careful decisions in selecting the AI tools, partners and platforms, careful analysis of costs against productivity benefits, also become key part of the strategy. Finally, organizations need to be conscious that adoption of generative AI and navigation of the AI-native world is, not a one-time activity but, a continuous journey that organizations need to be prepared for.