The knowledge revolution: How Gen AI is shaping the future of enterprise intelligence

AI is no longer an indulgence but the cornerstone of enterprise growth. As it transforms the way we work, adopting a strategic AI approach unlocks intelligent possibilities for today and redefines the future of knowledge.

Knowledge management and Gen AI

The evolution of knowledge management has seen enterprises move from static document repositories to dynamic, AI-driven ecosystems that enhance decision-making and innovation. Gen AI has significantly transformed knowledge management by enhancing how enterprises capture, organize, and utilize their knowledge assets. Traditionally, knowledge management relied on manual documentation and siloed databases, making it challenging to extract actionable insights. Gen AI eliminates these bottlenecks by automating knowledge creation, synthesizing unstructured data, and generating contextual insights. It enables real-time adaptability, turning static knowledge into a living, evolving resource.

Below are some key ways in which Gen AI has played a pivotal role.

Real-time knowledge adaptation

It enables knowledge systems to evolve dynamically by continuously analyzing and integrating new data from multiple sources—emails, reports, customer interactions, external news, or market trends.

Traditional knowledge management systems often require manual updates, leading to outdated or fragmented information. In contrast, Gen AI automates this process, ensuring that knowledge repositories are always up-to-date, accurate, and actionable.

For instance, a Gen AI-driven inventory management system updates product FAQs and recommendations based on real-time customer feedback and purchasing trends.

Contextual personalization at scale

Unlike traditional systems, Gen AI tailors knowledge delivery based on the specific context, roles, or needs of users. For instance, it can generate role-specific insights for executives, operational guidance for frontline staff, or customer-facing FAQs—all from the same dataset.

This contextual precision improves decision-making and enhances user engagement. In the manufacturing sector for instance, technicians access role-specific troubleshooting guides for equipment, while managers receive high-level operational summaries.

Accelerated innovation through insight discovery

Gen AI excels at uncovering hidden patterns and generating actionable insights from unstructured data. By synthesizing knowledge across silos, it helps organizations identify new opportunities, solve complex challenges, and foster a culture of innovation.

In marketing for instance, a Gen AI tool identifies emerging customer trends by analyzing millions of social media posts, surveys, and sales data.

Shift focus to high-value knowledge activities and elevate user

Gen AI automates routine and repetitive tasks, allowing employees to focus on strategic, high-value activities. By enhancing knowledge accessibility and tailoring information delivery, AI also elevates the overall user experience for both employees and customers. This shift fosters creativity, innovation, and engagement within the organization and with clients.

In customer support, AI-powered knowledge bases reduce response times, allowing support teams to handle more complex and valuable customer interactions.

Knowledge management AI initiatives across industries

To fully realize the potential of Gen AI in KM, it's crucial to understand its practical applications. This section will explore a range of specific use cases across various industries, demonstrating how AI can transform knowledge management within organizations.

Knowledge management AI initiatives: A case study

How a US-based multinational transformed their enterprise knowledge management with AI

A leading US-based company wanted to enhance their enterprise knowledge management tools to improve content quality, findability, reusability, and overall user experience.

They turned to AI to create an intelligent, responsive system that would empower employees with seamless access to the right knowledge at the right time. Nagarro collaborated with the client to build AI solutions for knowledge management. The solution enabled:

Content discoverability through AI-powered document tagging, improved search with NLP techniques, and voice-activated query search.

Personalized content recommendations that adapted to users’ needs based on their current tasks and recent activity.

Enhanced current SME search with Gen AI-driven SME search engine to help users quickly identify relevant internal experts on basis of various data points.

Effortless content in your preferred format and easy-to-read summaries for quicker content discovery.

Content sanitization tool to remove or redact business sensitive data from existing knowledge documents.

These transformative AI capabilities empowered the client’s workforce, fostering a culture of knowledge sharing, reducing time spent searching for information, and unlocking new levels of productivity.

How to measure success of KM AI initiatives

Measuring the success of these initiatives is essential to ensure they align with organizational goals and deliver tangible value.

Here’s a list of several key metrics and approaches that can help measure the success of KM Gen AI initiatives:

1. Contextual relevance and personalization with metrics like relevancy scores, recommendation acceptance scores, search time savings, etc.
   For instance, in a multinational consulting firm, consultants often struggle to find case studies relevant to their client’s industry. A consultant working on a retail client’s strategy can use Gen AI to receive curated case studies, frameworks, and insights tailored to the retail sector. This would reduce content search time and reduce prep time for client meetings.
2. Knowledge utilization analytics with metrics like adoption rate, product stickiness rates, usage frequency, user satisfaction, etc.
   Let’s consider customer service agents trying to address customer queries. They can use AI-powered knowledge base to promptly resolve customer queries where an AI tool can suggest solutions based on the customer’s past interactions and query context. This would improve CSAT, ESAT, FCRs (first contact resolution) and reduce AHTs.
3. Knowledge creation efficiency with metrics like human effort reduction, time to publish, quality content generation volume, etc.
   For example, a legal team leveraging Gen AI to draft contracts could measure success by the reduction in drafting time and the number of contracts approved without significant revisions.
4. Knowledge gap identification & bridging with metrics like missing or outdated knowledge identified, feedback incorporation, knowledge gap time to resolution rates, etc.
   A financial institution, for instance, can use AI to update its knowledge base with the latest regulations on fraud detection which can reduce errors and improve decision-making speed for analysts.
5. Innovation acceleration with metrics like idea to execution time, AI-generated proposal acceptance. In product development, measure the impact of AI-assisted brainstorming sessions on reducing time-to-market for new products.

Multimodality and agentic workflows revolutionizing enterprise KM

With multimodality, systems can now create more comprehensive, context aware, and engaging outcomes by leveraging media forms like text, images, audio, and videos. This is demonstrated by the rise of agentic AI that autonomously performs tasks and makes decisions in a dynamic and adaptive manner. A few features of these agentic systems are:

• Enhanced access to information for increased accuracy and productivity.
• Efficient content creation, reducing workload by summarizing lengthy documents, creating infographics from data, producing video/audio content, etc.
• Enhanced collaboration with multimodal content such as, reports, visuals and recorded explanations. This helps in informed insights and data-driven decision-making.

Agentic AI has implications across industries and domains:

• Marketing: Imagine a marketing team working on a new campaign. The AI agent can autonomously gather and analyze market research data, generate a comprehensive report, and suggest strategies based on historical campaign performance.
  Team members receive personalized content, such as relevant article and case studies, tailored to their specific roles. As the campaign progresses, the AI agent monitors performance metrics and provides real-time insights and recommendations to optimize the campaign’s success.
• Human resource management: AI agents can automate resume screening to identify qualified candidates, schedule interviews, speedup hiring and reduce biases. During onboarding, these agents can work as interactive guides helping new employees navigate training, answering common questions. Performance management can provide personalized feedback and development plans based on real-time data.
• Project management: This area often relies on manual updates and communications, leading to misalignments and delays. Agentic workflows can automate project management tasks, such as real time progress monitoring, milestone updates, alerts for potential bottlenecks, and task reassignment to balance workload. This allows project managers to focus on strategic decisions rather than administrative tasks.
• Customer support: AI Agents can manage routine inquires, assist service agents, handle common tasks like order tracking and answering FAQs. For complex issues, one can escalate to human agents and ensure appropriate support, improving service quality and response times.
• Finance: AI agents can identify trends, access risks, and inform financial decisions by analyzing historical data, current conditions, and market changes. In supplier discount negotiations, AI agents can review agreements and trends, compare terms, and recommend actions.
  Against corporate fraud and financial misstatements, AI agents can check transactions for compliance, generate audit reports, and notify stakeholders. AI agents can optimize tax by understanding tax laws, predict liabilities, plan strategies.
• Invoice processing: AI agents can help in automating data entry, verifying invoices, creating payment requests, recommending approvals, executing payments (through payment agents), and updating the systems. This improves efficiency, accuracy, and stakeholder communication in finance operations.

Increasing use of AI workflows highlights the necessity of ethical AI practices and governance. Organizations must establish frameworks to ensure AI operates ethically, transparently, and comply with regulations, with accountability and oversight.

Enabling successful AI adoption with cross-functional specialist groups

Successful AI adoption requires a cross-functional effort where each specialist group plays a vital role in supporting and enabling the others. This collaborative approach ensures that AI initiatives are aligned with business goals, developed responsibly, and ultimately deliver maximum value for the organization and users.

Knowledge management strategy and vision group: Ensures that AI initiatives in knowledge management are strategically aligned with broader enterprise goals, enabling the development of systems that enhance knowledge accessibility, retention, and utilization.

• Set goals such as boosting employee productivity, enhancing user experience, and improving content reuse for knowledge management efforts, all contributing to broader enterprise aims like driving innovation, increasing efficiency, promoting collaboration, and supporting data-driven decisions.
• Ensure AI-driven knowledge management initiatives are aligned to the above objectives for e.g implementing AI-powered semantic search or predictive knowledge/content recommendations, assistive knowledge chat bot etc.
• Define key results (like knowledge retrieval speed, accuracy, or employee satisfaction scores), align them across teams, and regularly track their progress.

When Netflix set out to enhance its recommendation engine using AI, the AI strategy & vision group played a pivotal role in aligning AI initiatives with the company’s core business goals of improving user engagement and content discovery.

Personalized recommendations now account for over 80% of content watched on Netflix, directly contributing to its customer satisfaction and growth. The strategic vision of the AI group ensured that the technology aligned with Netflix’s long-term goals of improving user experience and retaining subscribers.

Change management & communications group: AI adoption in KM often involves transforming the way employees interact and contribute to organizational knowledge. This group drives cultural change with right communication & adoption strategies to reshape workflows helping in various activities like:

• Building launch strategy for various user groups, and regions.
• Developing launch email communications, nudges and teaser videos to create awareness & generate interest or curiosity.
• Supporting users with smooth tool onboarding experience with Product guides, and FAQs.
• Building knowledge base articles to empower service support functions for query handling.

When Microsoft began integrating AI across its product offerings, particularly with the AI-powered features in Microsoft 365 (like intelligent search, automated summarization, and data insights), the change communications group played a key role in ensuring smooth adoption.

They built communication strategy, training materials and addressed concerns about the impact of AI on workflows and job roles through internal town hall meetings and Q&A sessions.

Data governance and compliance group: Knowledge management systems rely on accurate, secure, and compliant data to function effectively. This group establishes protocols for data quality, governance, and ethical usage, ensuring AI-driven KM solutions remain trustworthy and employees can use them without any concerns about data integrity, privacy breaches, or regulatory violations.

Organizations can ensure data governance and compliance by building policies to tackle:

• Biased outputs by regularly auditing & implementing mechanisms to flag and correct biased outputs
• IP rights by establishing clear guidelines for managing IP rights in KM systems, ensuring AI tools only access data with appropriate permissions and there is proper usage monitoring
• Accuracy of output by implementing robust data validation processes, continuously monitoring, maintaining human oversights, and updating the training datasets
• Sensitive data exposure by adhering to strict data privacy regulations (e.g., GDPR, HIPAA) and implementing strong encryption and access control policies

This helps establish a central Responsible AI governance group with RAI assessment frameworks to guide & drive AI initiatives responsibly across the enterprise.

For example, Pfizer’s AI-based KM platform for R&D relied heavily on robust data governance. The governance group ensured compliance with industry regulations such as GDPR and HIPAA while enabling secure access to sensitive research data. By setting up automated processes to flag and manage data discrepancies, the group maintained the integrity of the knowledge base and enhanced trust in AI-generated insights.

Learning and development group: Effective use of AI in knowledge management depends on a workforce skilled in navigating new tools and processes. This group creates training programs to build employees’ confidence in leveraging AI-powered knowledge management platforms.

L&D teams can accomplish this by:

• Conducting Gen AI masterclasses for employees
• Creating educational microsites
• Assigning Gen AI champions across office, locations, zones etc. for employees to freely reach out and consult.
• Integrating AI into new joiners onboarding kit & training plans

At EY, the Learning & Development Group launched a tailored training program to familiarize employees with its AI-driven KM portal. This initiative included hands-on workshops on using natural language processing (NLP) for information retrieval and leveraging AI-driven insights for client consulting. As a result, employees quickly adapted to the new system, significantly improving internal knowledge-sharing efficiency.

User experience (UX) group: For knowledge management systems to be effective, they must be intuitive and adopted widely. The UX group ensures that AI-powered knowledge management tools are designed for seamlessly discovering knowledge, ensuring all employees can easily access and contribute or utilize the knowledge base. This is done by researching on areas like:

• User current behavior and expectations from AI to map workflows where AI can add value
• Prototype feedback and real time usage learnings
• Summarize research with insights into gains, pains, and motivations to align development efforts with user-centric goals.

At Spotify, the user experience group was instrumental in the design and refinement of the AI-powered recommendation engine that personalizes music suggestions for users. The team worked closely with data scientists and product managers to ensure that AI’s recommendations were not only accurate but also presented in a way that was seamless and enjoyable for users to interact with. By focusing on a highly intuitive interface and personalization, the UX team helped improve user engagement, resulting in higher retention rates and satisfaction.

The knowledge revolution driven by Gen AI, is redefining enterprise intelligence by transforming how organizations manage, access, and utilize their knowledge. By breaking down silos, enabling real-time adaptability, and fostering innovation through contextual insights, AI has shifted the focus from routine tasks to high-value strategic activities. From revolutionizing workflows with multimodal capabilities to empowering industries with agentic AI applications, enterprises are now equipped to innovate and collaborate at unprecedented levels.

To build an AI-led enterprise KM strategy based on your unique needs, reach out to us for a consultancy engagement.

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