Q 801. Organizations often struggle with knowledge management — documents are scattered, updates are inconsistent, and employees spend more time searching than applying what they find. AI-infused solutions promise to make knowledge more accessible, contextual, and actionable.

Think of your domain:
What’s one area where poor knowledge management creates waste or risk? How could a prompt + flow-based AI solution transform that knowledge into a real competitive advantage?

The best answer will be selected on the basis of: 

• Relevance of the knowledge challenge

• Practicality of the AI-enabled solution

• Creativity in linking knowledge to competitive value

 

Note for website visitors -

• This platform hosts two weekly questions, one on Monday and the other on Thursday. 
• All previous questions can be found here: https://www.benchmarksixsigma.com/forum/lean-six-sigma-business-excellence-questions/. 
• To participate in the current question, please visit the forum homepage at https://www.benchmarksixsigma.com/forum/.
• The question will be open until Monday or Thursday at 5 PM Indian Standard Time, depending on the launch day. 
• Responses will not be visible until they are reviewed, and only non-plagiarised answers with less than 5-10% plagiarism will be considered for winner selection. 
• If you are unsure about plagiarism, please check your answer using a plagiarism checker tool such as https://smallseotools.com/plagiarism-checker/ before submitting. 
• All correct answers shall be published, and the top-rated answer will be displayed first. The author will receive an honourable mention in our Business Excellence dictionary at https://www.benchmarksixsigma.com/forum/business-excellence-dictionary-glossary/ along with the related term.

• Some people seem to be using AI platforms to find forum answers. This is a risky approach as AI responses are error-prone because our questions are application-oriented (they are never straightforward). Have a look at this funny example - https://www.benchmarksixsigma.com/forum/topic/39458-using-ai-to-respond-to-forum-questions/

• We also use an AI content detector at https://quillbot.com/ai-content-detector. Only answers with less than 45-50% AI-generated content will be considered for winner selection.

With advent of time, business has become more knowledge based. Detailed documentation of the process, flow charts, , drawings, standards, decision trees, QAPs, legal compliances, periodic updates of SOPs have become integral part of efficient operations. Processors/operators need to refer these document every now & then to main required CSAT, targeted revenue, escalations etc. Conventional ways of referring may not be effective when you are quoting a benefit of medical policy to an online caller because it would be time consuming as multiple clicks through contract details would take the agents to desired clauses. If a LLM based model coupled with Semantic technology is enabled here, the agent can use conversational AI to arrive quickly. Chatbots can be used to refer previous similar questions or advanced FAQ models. Similarly decoding of a complex claim history would become easier if SOPs, remark codes, diagnostic codes are connected using Ontario technology. This will not only make the  process efficient & simpler but dependency on a SME will become negligible. Learning curve of average agents will get reduced significantly.

Companies that have legacy software's or legacy systems usually tend to have a huge knowledge bases. The KB might be accumulate based on the releases and the issues/features that have been worked upon.

 

The hardships that we commonly face are due to unstructured KB's

  1. Excessive time searching for fragmented/relevant documentation

  2. Inconsistent, Incomplete or Outdated knowledge 

  3. Excessive time taken to go through inefficient content

  4. Knowledge Ownership conflicts : mis-leading to incorrect POC's

  5. SME's/KME's Resistance to Change 

 

Common areas where waste/risk can be identified/introduced :

  1.  Redundant Work or Re-work : Introducing new bugs while trying to solve existing  bugs.

  2. Employee Productivity

  3. Operational Efficiency

  4. Compliance issues

  5. High TAT

  6. Damaged Reputation

  7. Customer dissatisfaction due to inconsistent service delivery

 

 We can use the prompt  + flow AI solution to create the structured KB's by tagging, recognizing patterns,  building ontologies. Advantages may be 

          1. Accurate Knowledge Creation 

          2. Knowledge Storing and Retrieving, 

          3.  Knowledge Application 

          4.  Classification based on patterns

          5.  Automated Knowledge Base Maintenance

          6. Content Gap Analysis

          7. Semantic Analysis

          8. Automatic Content Curation

          9. Intelligent Search - mechanism to find relevant content

         10. Insights Generation - like finding previous issues, their resolutions 

         11.  Automated Question Answering

         12.  leverage waste/risk

         13. Drive continuous learning

         14. Improve adaptation by creating smart knowledge repositories

         15. Handle complex information

         16. implement predictive analysis and do forecasting

         17. Improve operational efficiency by reducing cost and time wastage

         18. Improve TAT

         19. Improve customer satisfaction through consistent service delivery without any re-work. 

A good example is Human Resources. In my organization, it was gathered, we have over 20 years of documentation that is not organized, but if harmonized and standardized, we can build an AI solution that reduces resource burden from the data management team. 

A standardized database can help create an Agentic AI solution that can help address inquiry management quickly and only escalate if the resource does not exist in the database.

Shared service case management process looks a potential example to me.
The process operates with customers case setup, review, validation and closure of the case in multiple business areas with fragmented knowledge across the value chain. The bigger problem within businesses is that over the period business processes and areas are more focused on the in-scope work and lack the up and downstream linkages. This creates a fragmented knowledge base, and they never talk to each other, resulting in the key objective. Further if people want to know the overall case summary the dependency is another concern with higher lead time and inefficiencies in the value chain. The dependency on people to update the Knowledge base based on the changes in product and service is another challenge as the information chain is so long from product to services, in many instances it observed we are falling behind the market updates on our own product. Another aspect on the risk side, where incomplete or inaccurate information has been shared with customers and stakeholders and leading to business losses and impacting brands market presence. A small example a miss on communication templates are not being updated on time, which leads to serious regularity and compliance risk on the contractual requirements.
 
An AI based solution could be a effectively handle the knowledge management in the organization, considering KM is broadly based on the input and content through a right channel, stored and accessible to the concern party on time with lates revisions and flags to help determine the business process. 
 

We can break the AI enabled solution into segments which will provide a complete view of KM across organizations,
 

• KM Assistant Bot – We would need an AI assistance UI through which all KM can be accessed through search and expand level with summarization, bullet and key highlights on product and its features along with recent updates and any flags.
• Central Knowledge Repository – Consolidate information from various sources such as policies, procedures, FAQs, etc. Also use natural language processing to summaries the content as per user prompt.
• Dynamic Content Generation – Based on large and complex documents can be simplified with underline sense and inputs for user to understand and take decisions.
• Tailor knowledge delivery to individual employees based on their roles, experience levels, and learning preferences.
• Interactive workflows – AI solution can provide a end to end mapping of the process flows and created an interactive workflow to help on completing their tasks.
• Validation from multiple sources – Since AI can read all information, it can provide with best suitable input for user and work as validation agent.
• Self- Learning module – Skill based self-learning module for the complex topic and underline knowledge assessments to keep the employees up to date on critical organizational aspects such as data security, ethical practices and workplace security etc. Customized training courses to individual employees based on their skills, roles experience levels, and learning preferences.
• Proactive Insights and Flags – Some time there are irrelevant, and incorrect information gets updated due to typo or missing user validation, AI agent can flag such information can flag for the user review.
• Feedback Loop – Knowledge base are very sensitive to updates and sometime updates are very frequent that we can to keep a reference from the KB, here versions can be tagged, and older version can be updated with user feedback so, appropriate version are updated and may be tagged with feedback score.  
  
  Given these aspects will keep the organization ahead on the accurate and precise information flow across organization. It will enable the functions to collaborate and work closely with each other reducing cycle time  and create efficiency and keep the organization ahead on serval business aspects. 

Let’s ground this in my domain of inpatient medical coding, since knowledge management challenges are especially costly there.

Knowledge Challenge (Relevance)

One of the biggest wastes in inpatient coding comes from scattered reference materials: coders juggle ICD-10-CM/PCS codebooks, AHA Coding Clinics, MS-DRG logic, hospital-specific guidelines, and payer policies.

Cross-check against the latest guidelines & facility rules.

Highlight coding risks (e.g., “Possible MCC missed: Acute Kidney Injury per KDIGO criteria found in lab values”).

Provide inline citations to the knowledge source → so coders trust the recommendation.

This flow converts searching for knowledge into applying knowledge in real time.

In the procurement-to-payment domain, poor knowledge management creates waste or risk of vendor dissatisfaction and delays in payment. PTP is a global team that deals with invoice processing from invoice receipt to payment release. The vendors are spread across 20+ countries globally and different time zones with diverse languages and tax codes. The PTP team observed enormous challenges while determining the tax code in vendor invoices during payment. Because, based on the tax code, the payment amount will be adjusted and released. Let me elaborate the issue below
• Tax codes vary across the globe and are not integrated into the ERP. Different geographies require different tax codes (GST, VAT, withholding tax, exemptions).
• Policies and rate updates are often scattered across emails, PDFs, or local folders which stores in isolated locations like individuals Sharepoint
• Employees spend hours checking SAP notes, internal guidelines, and past cases.
Associated waste & risk
• Wrong postings will have ripple effect on rework and delay in vendor payment and in turn vendor dissatisfaction
• In case of government tax payment, this will lead to compliance issue
• In case of early payment vendors, risk of incurring penalties are high
An AI Solution was created to address this KB issue.
Below Prompt+ flow-based AI solution transform that knowledge into a real competitive advantage
• Step 1 – Prompt: Once the invoice reaches the user for payment processing. Then user will write the below prompt“What is the correct GST code for a consulting invoice from Vendor X, billed in Kolkata, service date Jan 2025?”
• Step 2 – Flow:
o AI fetches policy documents, recent tax circulars, and historic postings which are in AI optimized format
o Because the tax code and policy change over time, we need to change the KB accordingly. We preferred a solution that would have features such as content organization, version control, draft management, and search and tracability. We opted for Document 360.
o Contextualizes the query (vendor type, geography, service category) using Content organization feature of Doc 360
o It recommends the correct tax code and confidence score.
o Generate a short explanation (“This vendor is registered under Kolkata GST, services are consulting, applicable rate 18% as per 2025 notification”).
o Links to source documents for the audit trail where proper tagging has already been done.
• Step 3 – Action: AI pushes the selected tax code directly into SAP entry after validation with user with the help of API integration features of Doc360
In this way, the AI-infused solution helped consolidate fragmented tax codes and notes and employee assumptions, turning diverse knowledge into a real-time, contextual decision engine.
 

 

Let us consider a software engineering program intended to migrate applications from legacy technologies to cloud technology. Usually, legacy programs running for decades have fragmented knowledge base resulting from poor knowledge management, inconsistent updates to the knowledge base by different stakeholders , multiple documents maintaining the same details etc. This results in efforts to search the details from the available documents, duplicate analysis effort that causes re-work.

A prompt + flow-based AI knowledge solution can transform this challenge into a competitive advantage by the following means-

·       Maintaining a central repository of documents, following a standard template and automating the process of adding the technical documents, important meeting notes in a structured format.

·       Enable contextual discovery so that data engineers can query in natural language and get response in precise manner with links to the reference documents

·       Integrate the AI workflow with project management and version control tools to regularly update the knowledge base documents like – runbooks, SOPs, Technical design documents etc.

·       Before production deployment add  a step in deployment runbook to trigger workflows like opening a Jira ticket to update documents and notify leads

 

So, the prompt+flow based AI knowledge solution help the program gain by providing faster, reliable and consistent knowledge base, thus reducing the risk to the current program and a dependable knowledge base for future programs.

In my opinion the question is not Can AI Turn Knowledge Into a Competitive Edge? I want to argue that NOT having AI knowledge will give you Competitive Disadvantage. NOT having AI knowledge is no longer a neutral stance it is a strategic liability in today’s rapidly evolving AI landscape. NOT understanding or engaging with AI isn’t just a missed opportunity, it poses an active risk of being outpaced by those already learning, adapting, and building with it.
 

We’ve seen this story before, when electricity was first harnessed, it was a scientific curiosity exciting but abstract. But soon, its practical applications began to reshape the world heralding Industrial revolution. Those who adopted had automated production lines, powered transport, lit cities. The speed, efficiency, and scale became their advantage.
 

AI is following the same trajectory. It’s more than tool for automation or a back-office efficiency play. It’s a foundational capability—one that turns data into decisions, complexity into clarity, and speed into a strategy. Organizations that understand this are pulling ahead, not because they have AI, but because they know how to work with it. The edge lies in application. Companies are using AI to streamline their operations, personalize the customer experiences, and make better decisions faster. For instance, in our product development company AI helps teams to rapidly sift through user questions, support tickets, and qualitative feedback to identify what really matters. That insight translates to faster responsiveness, quick iteration and tighter product-market fit.
 

Meanwhile, those who are still sitting on the sidelines—waiting for “the right moment” or “clearer ROI”—are already at a disadvantage. Delay doesn’t just mean slower progress it means falling further behind as others scale their AI capabilities and create better offerings in shorter timeframes. We no longer ask whether electricity gives a business an edge, it is just assumed and soon the same will be true of AI. It will be invisible but indispensable woven into decisions, processes, and products in ways we no longer even think about. This is the start of AI Revolution.
 

So yes, AI can absolutely turn knowledge into a competitive edge. But more importantly, lacking that knowledge is already putting you at a disadvantage and without positive action that gap is only going to grow.

 

There is a Ticketing process at the bank to handle requests coming to Service Delivery team. They do the filtration of these tickets and allocate  all issues or new request raised to the respective clusters for resolution. This is a domain where the use of an AL solutions will definitely helps out to transform knowledge into a competitive advantage.

 

Below points highlight how we can make use of the Ai solution to integrates the process

 

🔍 1. Intelligent Ticket Classification & Routing

Using AI , the categorisation of the issues and request can be done without manual intervention. Since at times the triage is not done in a consistent and regular basis. This can improve the routing time and improve time resolution rates.Based on the information provided , the tickets gets routed to the right support team based on historical resolution patterns

 

📊 2. Predictive Analytics for Incident Management

The advantage of using AI, helps predicts recurring issues before they get escalated and it flag them so that appropriate action can be taken in time . Some critical example  like adding on space on database or addressing recurring network issues helps to maintain stability of the systems and applications.

 

🧠 3. Knowledge Base Optimization

When technicians resolved Tickets , they provide a resolution to the issue or incident raised. With the AI agent, a knowledge database is created with all the steps provided at the resolution stage and this helps when similar past incidents are raised, users can make reference to the recommended solutions, this will in turn reduced the volume of tickets getting loaded to the service delivery team, it acts like a self service portal.

 

🤖 4. Conversational AI & Virtual Agents

In an environment where there are lots of applications spread across different countries, it is cumbersome to handle common IT requests like (password resets, access issues). Embedding the required steps in the AI agent help to assist users in handling these L1 support with the introduction of a ChatBot. Given that we worked in different time zone across 5 countries, The Chatbot provide support 24/7 just reducing the dependency on human agents. On top of it, with the interaction with users, it improves the quality of the response over time.

 

 📈 5. Performance & Compliance Monitoring

Most of the time, when handling tickets , we do noticed long overdue items which have crossed SLA since there is a delay in assigning the ticket and get it to closure, the AI solution helps to monitor the adherence to the SLA set and regularly provide a feedback to the requester which when manually done, the technicians does not revert back on time and thus creates frustration among users. Feeding the AI solution with banking regulations and internal policies, ensure that when resolutions of the tickets are captured, it is in line with the internal and external policies.

 

🏦 6. Competitive Edge for Banks

Making use of the AL solution with the ticketing system, helps to enhance operational efficiency and deliver faster and smarter support to the internal and external customers. It also reduce the cost of adding additional resources to handle tickets and improves the service quality .

 

In a nutshell ,

Making use of AI to manage the ticketing process helps in terms of using the data coming out from these tickets getting raised into useful information for the clusters and the bank . It ensure that anomalies are flag faster and helps the bank stays compliant. It also enhance customer Trust which is a competitive edge, since faster internal issue resolution leads to better customer service. Most importantly with its predictive capabilities it reduce the occurrence of downtime of critical systems which ensure operational resilience.

Yes. AI can turn knowledge into competitive Edge.

When automation was first introduced through technologies like Robotic Process Automation (RPA), the primary goal was to achieve efficiency by handling repetitive and rule-based tasks with speed and accuracy. This reduced manual effort and allowed employees to focus on more value-added activities. Over time, organizations realized that automation could go beyond repetitive work. By adding cognitive capabilities such as learning, reasoning, and decision-making, Artificial Intelligence (AI) was introduced into business processes.

While RPA can follow the defined rules, AI has the ability to analyze data, understand patterns, and adapt to new situations. As more processes and tasks are handed over to AI, it not only executes them efficiently but also builds knowledge from every interaction. This accumulated knowledge helps organizations make smarter decisions, predict outcomes, and innovate faster.

In Alternative Investments Transfer Agency, Poor knowledge management creates waste or risk in complex product processing rules. Each client has different structures, fee rules, Redemption timelines. Team members often has to manually search multiple documents, emails, spreadsheets to find correct instructions.

This creates

1.       Operational delays – slower processing of Purchases, redemptions and transfers

2.       Errors – Following different rules for a particular client creates errors including compliance and financial risk.

3.       Training inefficiencies  - New team members spend more time learning all procedures. (Long curve of learning)

A Prompt and Flow-based AI solutions can transform this

1)      Centralizing Knowledge – AI ingests clients documents, emails and other documents properly

2)      Guided experience  for users – Processors can follow step by step auto populate fields based on AI guided based on the client business rules.

3)      Faster turnaround on complex processing tasks

4)      Reducing operational risks and errors

5)      Higher scalability (seasonal volumes, new fund events can be handled without any issues)

6)      Achieving client satisfaction due to reliable service.

With AI tools, there are great opportunities to add a high level of order to messy documents all across the organisation. Typically, AI tools provide the opportunity to generate recommendations that lead to improved knowledge outcomes and enhance the way the outcome is organised. It is important to understand the burden and overhead the non-organised knowledge management would lead to, including but not limited to extra effort required, increased error rate, and eventually affecting overall productivity

Let’s imagine a law firm that has an organised knowledge base. This will create a huge loss of staff time for data search; the same task might be repeated several times by the same user or multiple users at the same time, the probability of error happening is higher, and eventually, many opportunities are lost due to a lack of organised knowledge

Using a prompt and flow-based approach makes knowledge management more efficient as it turns out to be faster to retrieve, gives users the opportunity for smarter decisions based on smartly organised knowledge, triggers updates, projects and other critical events as part of the flow-based approach, and further it detects gaps in the organisation and provides recommendations

Do you know how banks are getting smarter?  It is by turning Knowledge as a Competitive Edge : 

 The real game-changer is how they're using everything they know by decoding the customer brain through data by studying their patter 24/7/ 365 days.

 

 1. They are ditching the old for the new

• Simplify and streamline operations: They are replacing legacy systems with modular, cloud-native architectures which was un-thinkable just a few years ago.
• Use continuous integration and delivery tools are used to reduce development time and improve agility.
• In action: Bank of America using Cloud Provider: Private cloud infrastructure. Saved approximately $2 billion

---

 2. Investing big in data and AI

• They are Building a solid foundation: Banks are building unified data views from their scattered data sources. To connect all the dots which was their biggest pain.
• Use AI and Gen AI to generate insights, automate decision-making, and to give a personalize customer experiences.
• Example: Several Autonomous Decision Intelligence Platform is developed by banks to turn fragmented data into strategic assets for fraud detection, risk assessment, and marketing.

3. Shifting focus from maintenance to innovation:

• Redirecting funds: Tech budgets are being redirected from simple maintenance to things that where it is creating new value.
• Customer-first focus: The focus has shifted to improving the customer experience, personalizing everything, and getting to market faster.

4. Nurturing the right talent and culture

• Build a tech-savvy board and leadership team. Gen AI training are provided to the leaders along with it’s application.
• There is a significant Increase in the proportion of in-house engineers and reduce overhead roles.
• Foster a technology-first mindset across the organization.
• Example: Another US Bank Capital One, with it’s 12,000-strong tech team transitioned to a cloud-first model and began selling its own software products.
• 5. Use AI to Enhance Relationship Management

AI isn't just for behind-the-scenes—it's also a powerful tool for customer-facing teams.

• Smarter advisors: Relationship managers are being armed with AI-generated client summaries, risk profiles, and insights.
• More meaningful conversations:  They are spending more time on strategic, high-value conversations because gen AI solution is providing them with the necessary inputs.
• The payoff: This is leading to a stronger, more profitable customer relationships and faster decision-making.

While applications like Confluence and SharePoint are helping organizations—especially in banking—manage their knowledge repositories better, I still believe it remains a challenge at the grassroots level for employees to find the relevant information they need. This is mainly due to the large amount of content and the fact that different teams contribute information in different ways. In most cases, employees jump between outdated FAQs, long policy documents, and scattered internal notes. For example, if a customer raises a query on a call about his payment transfer being declined five times in the last two days, the call center agent would need to access account information, check transfer failure reasons, and review the guidelines on what to do in such a case. Because of these long searches, the agent may have to put the call on hold. This not only wastes the customer’s time but also increases dissatisfaction and overall call AHT.

 

To overcome these challenges, I believe a prompt + flow-based AI solution (leveraging NLP as well) can help agents reply instantly by simply asking, “What are the next steps if a transfer transaction is declined for reason X?” Within seconds, the AI can fetch the answer by searching the bank’s knowledge base, explain the reason, and provide the right next steps for the customer. It can also suggest a ready-made script the agent can use. For example: “Your transaction is being declined because your KYC is pending. Please complete the KYC process at the earliest to ensure smoother transfers.”

 

This AI-powered solution for knowledge management can truly help call center agents respond to customer queries quickly, consistently, and with full confidence. The real benefits come from higher first-call resolution, reduced handling time, and customers leaving the call feeling their bank truly understands them.

 

Any tool is as effective as the user. AI can analyse massive data and find actionable insights for the business to act on. The sooner the business takes this path, the quicker it can gain an advantage over its competitors. AI can help the leadership with speed, precision and foresight to make a strategic decision. The advantage can be in any of operational efficiency, customer experience and continuous learning and innovation, or all of these, depending on the direction the leadership takes. 

There were very high chances that there would have been no winner for this question as most of the respondents were not able to clearly establish the linkage between knowledge management and competitive edge.

 

However, there is one answer that establishes the linkage and hence has beeen selected as the winner. Well done Tushar Ghosh.

 

P.S. The other aspect that I would have loved to see in the answers is how do you keep AI up to date with the change in the knowledge as all answers agreed that in andsence of AI, it is the pain point