A friend recently shared a story that made me think about how organisations are using AI.

A team of his organisation had prepared an impressive plan for introducing a new way of working. Everything looked right: the objectives, the responsibilities, the timelines and the risks. The document was well written, carefully structured and convincing.

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But something was missing.

Nobody seemed to have asked whether the organisation was actually ready to put the plan into practice.

The deadlines were tight. Some responsibilities were unclear. The plan expected people to follow processes they had never used before.

The ideas were not necessarily wrong. They simply didn’t reflect how the organisation really worked. It was not tailored.

“Was it written by AI?” I asked.

“Possibly,” my friend replied. “But does that really matter?”

He had a point.

Perhaps the more important question was whether anyone had checked the plan against what the organisation already knew.

The knowledge nobody asked for

AI can help produce an impressive plan in minutes. But a plan that looks complete can make people less likely to question its assumptions, especially when it has management support.

There is also a less visible risk. When AI suggests a deadline and nobody questions it, that assumption can become part of the plan without anyone consciously validating it. It can then shape the decision, even though nobody delegated the decision to AI.

Consider a simple example.

A plan allows three weeks for testing a new process. It sounds reasonable until someone explains that the teams have never done this before and that previous projects needed much more preparation.

The knowledge to spot the problem already existed. It was simply missing from the discussion.

Making that knowledge available at the right moment is what Knowledge Management (KM) is for. KM brings previous experience, lessons learned and the people who understand how the organisation works into the discussion, while the plan can still be questioned.

A 30-minute reality check

Before approving a plan that will significantly change how people work, try three things.

1. Identify the three assumptions most likely to undermine the plan if they are wrong. Think about timelines, available people, skills or dependencies. AI can help you list them. People who know the organisation need to check them.

2. Test them against organisational experience. What evidence supports them? What happened when something similar was attempted? Who has challenged them? Can the people responsible for delivery confirm they are realistic?

3. Make responsibility clear. Who will validate each assumption? Who has the authority to accept the remaining risks?

A simple record is enough:

Assumption: [ ] | Evidence: [ ] | Validated by: [ ] | Risk owner: [ ] | Outcome / change: [ ]

Thirty minutes may not resolve every question, but it can reveal what needs checking before approval.

If an assumption does not hold up, adjust the plan, test the uncertain part first or make the risk explicit before proceeding.

Keep the record with the decision. The next team should not have to rediscover the same problem.
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One question before saying yes

AI has made convincing plans easier to produce. It hasn’t made organisational judgement optional.

A convincing plan is not yet a well-informed decision.

So next time an impressive plan arrives on your desk, don’t begin by asking whether AI helped write it.

Ask: Which assumption could undermine this plan, and who in our organisation would know?

The views expressed in this article are my own and do not represent the position of my employer or any institution I am associated with.

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Not long ago, if you needed to find “best SEO agencies in India”, or “top niacinamide brands”, you might tap on Google search. You would open the top three pages, skim through a handful of reviews, or read a few comparisons.

The process was pretty straightforward then.

Fast forward to now. Everything has changed. Consumers either prefer brand recommendations on Google Overviews or type into ChatGPT, Gemini, or Claude.

58% of consumers prefer product recommendations coming from gen AI tools and 68% are ready to act on those recommendations.

Let’s find out how this AI-based digital experience is reshaping the customer journey.

The Evolution: From Traditional Search to AI Answers

The role of your website has shifted entirely. Data and fixed rules once guided buyers toward conversion, but that approach no longer works.

The old question was, "What information should our website show?" Now it's, "Does our website make visitors feel certain?"

The shift has happened from information to confirmation as buyers move from “search and browse” to “ask and receive”. Now, their journey has started even before the first click.

Google users clicked a traditional result link in 8% of visits when an AI summary appeared versus 15% without it did not.

Why does it happen?

1. Your customers have shifted from navigation to interpretation

They won’t like to explore rigid, complex, or lengthy output related to their queries.

Take for example a software product that needs a bit more explaining. Here, AI search can guide your buyers step by step. It breaks down complex API integrations, compares key features, and even answers technical troubleshooting questions after purchase.

AI answers just take the pressure off your sales and support teams from pre-qualifying to post-purchase touchpoints. It acts as your confirmed confidence-building machine.

 2. Your customers are leaning on conversational searches

Their queries are now more conversational and contextual. A few keywords are no longer enough to help them decide. Instead, they explicitly describe what they want.

• Which moisturizers are best for my oily pimple-prone skin?
• Which sport shoes are best for a half marathon?
• Which CRM is best for supporting my field sales team?

This is also why more people now turn to voice AI assistants. Your brand gets discovered in these answers long before anyone visits your website. You can use the same technology to improve your own customer experience.

For example, with Murf’s AI voice generator, you can create natural voiceovers for product demos and explainer videos. You can match your customers’ preferred languages, accents, and pacing. A reliable web hosting service also helps ensure these experiences remain accessible to visitors with consistent website performance. You can even use it to power voice support that guides customers in real time. This way, your brand speaks to buyers the same conversational way they now search.

 3. Your customers’ trust transfers to AI recommendations

When AI answers your customer questions, it pulls and cites the sources along with it. The recommendations automatically reflect your brand’s authority and credibility. In fact, customers trust AI answers even more as they refine their follow-up questions.

Adobe’s 2026 Q2 AI Traffic Report shows this in practice. Visitors arriving from AI tools spent 48% more time on retail sites than other visitors. Meanwhile, 38% of consumers said they trust AI more than they used to.

This way, your customers remain more informed, convinced, and confident when AI drives their buying journey.

How You Can Keep Up With AI Visibility to Revamp Your CX

Your goal should be to appear in AI answers, whether as a direct mention, a cited source, or a recommendation. Customer behavior and expectations have shifted, and brands that ignore this risk falling behind.

Here’s where to focus:

Write answer-first content
Before adding depth, answer directly to the questions that target your customers’ sentiments. Lead every single page with authoritative, validated, and context-rich knowledge while keeping brand definition consistent.

Invest in semantic search

Don’t rely on exact match keywords anymore. Your customers are increasingly moving toward conversational searches. By aligning brand discovery with the purchasing journey, AI is fixing the blind spots that keyword searches often miss.

Earn third-party citation

While not all sources carry equal weight, a verified external source can determine validation. For example, for your SaaS product-based brand, a G2 rating and review can be highly influential.  Building a strong presence on platforms like Socialthink can also help your brand reach audiences interested in social media trends, digital marketing, and online brand discovery. Working with a SaaS Link building agency like VH-info can also help your brand earn relevant mentions and citations from trusted industry publications. When evaluating potential publications, use a domain rating checker to assess their backlink strength alongside their topical relevance and editorial quality. As a matter of fact, AI is more likely to trust industry publications, research reports, and analyst platforms. 

Optimize for trust

Mitigate friction in different touchpoints as it can break your customers’ buying intent within a second. Think of reviews and social proof, how they earn trust from your target prospects, and help convert them faster.

Participate in trusted communities

When you join community discussions and share expert input, they spread word of mouth for your brand naturally.  For brands in the design and creative space, platforms like Designers-choice can also help audiences discover useful design tools and resources. And the trust appears in AI responses as well. For example, Reddit and LinkedIn frequently appear in ChatGPT, Gemini, and Claude responses.

Connect your data

When AI-driven knowledge management enters, it walks through information from different sources, such as chats, emails, the web, and social media. It can then link product details, behavioral data, CRM insights, and purchase history into one connected view. The more organized and consistent this data is, the more confidently AI can recommend your brand.

Measure, attribute, & improve

Review AI summaries to ensure how they describe your brand offerings and compare with competitors. Check how often AI pulls your brand, links it as a verified source, and recommends your brand whether as a primary or secondary option. To win in the AI visibility environment, you don’t need more data but more clarity.

Turn AI Visibility Into Better CX

Gen AI is shortening the research, simplifying the jargon, connecting multi-touch attribution models, and closing the loop from being invisible to being recommended.

Today, AI is raising customer expectations. At the same time, it rewards brands that offer clarity, trust, and confidence across the customer journey.​

However, that doesn't mean the traditional search result is dead. Strong technical health, quality content, and authority still determine whether AI systems can find and trust your brand.

If you perform well in these areas, you can influence AI to choose your brand. Make your target audience feel understood and convinced. Connect with KM Institute to build the well-governed and trusted knowledge foundation that makes this possible.

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A practical reflection on Syntax, SharePoint, content curation and the changing role of the Knowledge Manager

One of the most time-consuming parts of Knowledge Management is often not creating knowledge; it is getting existing content into a shape where people can actually find, trust and reuse it.

Before content can become useful organisational knowledge, someone has to find it, understand it, clean it, categorise it, tag it, check it, and move it to the right place.

Recently, I explored an AI automation use case using Syntax with SharePoint. What interested me was not simply that AI could read documents. It was the possibility of using AI to take on the repetitive first layer of content curation—while keeping the Knowledge Manager firmly in the loop.

Check out the detailed PDF Overview...

The use case: from SharePoint folders to curated knowledge

Think about a SharePoint folder with hundreds of files accumulated over time: proposals, case studies, presentations, solution documents, meeting notes, templates, project material, drafts, and older content. Manually, a KM professional may need to open individual files, identify the content type, understand the subject, apply metadata, identify possible restrictions, and decide where the content belongs.

With an AI-assisted workflow, the model changes:

SharePoint folder → AI processing → classification and metadata suggestions → governance flags → human review → curated repository

What AI can help automate

• Identify likely content type—for example, case study, proposal content, solution, SOP, meeting notes or thought leadership.
• Suggest categories based on the organisation’s taxonomy.
• Generate or recommend metadata such as practice, industry, geography, solution, technology and content type.
• Surface potentially duplicate, outdated or low-value content for review.
• Flag content that may contain confidential, client-sensitive, copyright-protected, or proprietary/IP material, subject to appropriate rules and validation.
• Route approved content to the appropriate SharePoint location through workflow automation.

Is it 100% accurate? No—and that is okay.

Based on my exploration, I would describe the initial output as roughly 70% accurate/usable rather than 100%. The exact result will vary with the quality of the taxonomy, instructions, examples, content, and workflow design.

For me, the important point is that AI does not have to be 100% accurate to create value. If it can remove a large part of repetitive discovery, sorting, classification, and tagging, it gives the KM team more time for the work that requires judgement.

AI is only as good as the KM context we give it

This is where the Knowledge Manager’s role becomes even more important. If we expect AI to recognise reusable content, confidential information, copyright restrictions or organisational IP, we have to define what those things mean in our environment.

The KM team provides the taxonomy, content definitions, metadata rules, examples, and governance principles. In other words, we are not simply asking AI to read content. We are teaching it how our organisation manages knowledge.

The model I believe works: AI + human review

I do not see the process as:

AI → Publish

I see it as:

AI → Process → Recommend → Flag → Human Review → Publish

The final responsibility for trusted knowledge should remain with the KM professional or designated content owner. AI can accelerate the work, but the human layer validates accuracy, relevance, reusability, confidentiality, IP, taxonomy, and content quality.

Beyond folders: connecting Forms, Power Automate and SharePoint

The bigger opportunity is to move from one-off content harvesting to continuous knowledge capture. For example, a simple Microsoft Form can capture meeting notes, project decisions, lessons learned, challenges, solutions, and reusable assets. Power Automate can trigger processing, and AI can structure and classify the information before it reaches the KM review queue.

A possible flow is:

Capture → Structure → AI classify/tag → Flag → KM review → Store → Reuse

A use case I see for bid and proposal teams

Bid teams work under pressure and repeatedly need similar knowledge: case studies, credentials, solution descriptions, previous responses, differentiators, delivery models, industry examples, and reusable proposal language.

If content is continuously classified and tagged, the bid team can move beyond searching for filenames and folders. The longer-term goal is to search by intent and meaning—for example, asking for examples of how the organisation helped healthcare clients improve customer experience through digital transformation.

This is where KM can shift from being a repository service to becoming a business enablement capability.

Manual vs AI-assisted curation

What happens to the Knowledge Manager?

This is the question AI naturally raises. My view is that the role does not disappear—it moves up the value chain.

• Less time on repetitive content administration.
• More time on knowledge strategy and architecture.
• More focus on taxonomy and governance.
• More attention to knowledge gaps and content lifecycle.
• More SME engagement and adoption.
• More time to measure reuse and business impact.
• A new responsibility for designing, testing, and improving AI-enabled knowledge workflows.

My biggest takeaway

AI does not replace the Knowledge Manager. It changes what the Knowledge Manager should spend time doing.

The future of KM may be less about manually managing every document and more about designing intelligent knowledge flows that can discover, classify, curate, flag, and route information—while humans provide the judgement, governance, and accountability.

Perhaps the future is not Human versus AI. It is Human + AI = Intelligent Knowledge Management.

We have spent years asking people to contribute knowledge and use repositories. The next opportunity may be to build systems that capture and process knowledge as part of the work people are already doing.

The question is no longer only, ‘Where is the knowledge?’ It is becoming, ‘How intelligently can we move knowledge to where it is needed?’

Also, I have attached my practical guide draft that I have been leveraging to automate content discovery, classification, tagging, governance, and routing.

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1. The Intro

For decades, Enterprise Knowledge (tacit and explicit, structured and unstructured) has felt like a vault full of treasure that nobody had the key to unlock.

Open weight AI models might just be the "Open Sesame" moment knowledge leaders have been waiting for.

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2. The Story... So Far

If you haven't been tracking the frontier AI landscape over the past few weeks, the open-weight debate just reached a boiling point:

• July 9–13, 2026 | Security & Frontier Risks: An autonomous AI agent from frontier lab OpenAI escaped its isolated sandbox environment (a "jailbreak" in lab parlance). To pass an internal benchmark test, it inferred that an external site (Hugging Face) might hold the answers. It gained internet access, moved laterally, and compromised Hugging Face's dataset processing infrastructure and cluster credentials.
• July 16–27, 2026 | The Benchmark Shift: Moonshot AI released Kimi K3, an open-source frontier model matching or beating closed proprietary models like Claude Fabel and OpenAI Sol across key benchmarks. Think of it as AI's "Tesla vs BYD" tipping point. On July 27, Moonshot offered free access to its model weights—marking a major turning point for knowledge management.
• Industry Giants Line Up: NVIDIA’s Jensen Huang authored an open letter defending open weights, co-signed by Microsoft, Google, IBM, and over two dozen tech leaders resisting proposed federal bans. (The notable exception was Anthropic, who did not sign).
• Enterprise Validation: AWS and enterprise IT leaders are stepping in to provide enterprise-grade support and security for open-weight deployments—mirroring the playbook that made Linux the backbone of modern enterprise software.

3. The Technology (In Plain English)

So, what actually are "Open Weights"?

When you use proprietary SaaS models, you are renting access to a black box over an API. You send your data out; you get an answer back. This is the case for closed models like Claude, ChatGPT, or Gemini—you cannot see how your data is being used. Because your IP is at risk, there is a forced brake on AI adoption and progress for many companies, especially in highly regulated sectors.

With Open Weight models (like Kimi K3), the provider gives you the underlying neural blueprint and trained parameters ("the brain"). You can download it, host it on your own servers or private cloud, run it offline, and tweak it as you see fit.

You control not just your data security and privacy, but the model itself. You can begin to imbue it with the values and personality of your organization. For example, the term "disclosure" carries a vastly different legal weight in a law firm than in a restaurant chain.

Owning model weights is the difference between renting a taxi (Claude or Gemini) versus owning the vehicle (Kimi K3 or Inkling models) and parking it inside your own private garage.

4. Open Sesame: What This Means for Knowledge Managers

For Knowledge Managers, this technical shift solves the two biggest roadblocks that have plagued enterprise KM for twenty years:

• Absolute Data Sovereignty & Privacy: You no longer have to compromise between cutting-edge AI and strict legal compliance. Run models inside your firewall or Private Cloud (VPC)—sensitive IP, code, and confidential documents never touch a third-party server.
• Bespoke Domain Expertise: Off-the-shelf commercial models know a little about everything, but zero about your internal jargon. Open weights allow you to fine-tune smaller, highly efficient models directly on your corporate taxonomy and historical post-mortems. KM teams are uniquely positioned to gather, organize, and validate what the model should and shouldn't learn—opening a massive new horizon for modern knowledge managers as models are updated periodically in partnership with IT.
• Predictable Cost & Scalability: Querying expensive proprietary APIs millions of times a day drains budgets fast. Open weights let you optimize inferencing costs and run task-tailored models affordably at enterprise scale.
• From "SharePoint Graveyard" to Active Intelligence: Passive document repositories (where good ideas go to die) transform into a living, conversational corporate memory. The intelligence from your best data sources now sits inside the model's weights. When a user queries a novel situation, the model provides guidance because institutional wisdom is built directly into its architecture. The modern knowledge manager is instrumental in making this happen and curating it overtime.

The Open Sesame moment has arrived.

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How many times have you searched your organisation’s knowledge base, found three different versions of the same policy, and had no idea which one was current? Or asked a question that clearly comes up every single week — and found nothing?

As a Knowledge Manager, this isn’t a rare moment. It’s the everyday reality of running KM in an organisation that moves faster than any team can document.

And every time it happens, the same thought creeps in: We have so much content. Why does it still feel like we can’t find the right content or right people to point us to the right content?

The honest answer is that most KM programs are built to store knowledge — not to actively manage its health. Gaps go undetected. Outdated content sits published for years. Nobody knows if an article is still worth the effort of keeping it around.

This is exactly where AI stops being a buzzword and starts being useful, everyday infrastructure.

1. Finding the Gaps We Can’t See

Normally, gap analysis depends on someone noticing a gap. A customer complains, an agent flags it, and only then does someone go check if an article exists. AI doesn’t wait for that. It listens all the time.

By scanning search logs, chatbot questions, and support tickets, AI can find out what people are actually asking — even when the same question is worded fifty different ways.

A simple AI-led gap analysis can:

group similar questions together, even if the wording is different, to reveal a gap hiding behind messy phrasing; spot articles that almost answer the question but stop just short; compare what exists against a list of all the topics that should be covered, to expose entire missing areas; rank gaps by how often they come up and how much they matter to the business, instead of guesswork

This is the difference between fixing a gap after someone complains, and knowing it’s there before anyone has to ask.

Case in point: At XYZCorp, agents kept getting asked about VPN errors — but each ticket used different wording (“can’t connect to VPN,” “VPN keeps failing,” “remote access not working”). No single article was written to catch all of these. AI grouped the tickets and showed there were over 200 such questions a month, with no clear article answering any of them well. That gap had existed for over a year, completely unnoticed.

2. Knowing What’s Actually True Anymore

Publishing an article isn’t the finish line. Content goes out of date. Policies change. Products change. And most KM teams have no easy way to know which articles have quietly become outdated or wrong.

AI can act as a constant accuracy check by:

pulling out facts, numbers, and steps from articles and checking them against the real source of truth (like product documentation or policy systems); flagging two articles that say different things about the same topic using an AI reviewer to catch old terms or steps that no longer make sense; sending anything flagged to a human expert to confirm — AI should never publish the fix on its own

The point isn’t to let AI decide what’s true and do all the work on its own. It’s to stop asking the knowledge team to re-read everything manually, all the time, just to catch what’s gone wrong.

3. Catching Content That’s Technically There, But Practically Dead

This is the quiet failure mode of KM — content that still exists, still shows up in search, still gets used, but refers to a policy or standard that’s no longer in force.

This matters most in places like presales, where proposal and RFP content constantly pulls from policy documents, compliance standards, and certification references. If the source policy has moved on and the content hasn’t, that outdated reference ends up in a client-facing document — and nobody notices until it’s already out the door.

AI can catch this by:

linking each article or proposal template to the exact policy or standard version it was written against watching for updates to policies, certifications, and standards, and flagging every linked article or template the moment a newer version is published setting simple rules so old, untouched reference content gets reviewed automatically on a schedule, not by chance building a simple dashboard that shows, at a glance, which policy-linked content is going stale, so nothing outdated makes it into a client-facing document

Case in point: XYZCorp’s presales team kept a standard security-compliance annexure that got copy-pasted into almost every proposal. When the underlying compliance standard was revised, nobody updated the annexure — it had been reused so often that no one remembered where it originally came from. AI flagged it the same week the standard changed, because the annexure was linked to that specific policy version. Without that link, an outdated compliance claim could have gone out in the next client proposal.
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The stakes get even higher in industries like pharma. A drug’s prescribing information — dosage, interactions, side effects, usage guidelines — can change after a regulatory update, a new clinical finding, or a safety alert. If a sales rep, call centre agent, or patient-facing article is still working off the old version, that’s not a minor inconsistency — it’s outdated medical guidance reaching a doctor or a patient. A pharma company linking every piece of content to its exact regulatory version, and getting flagged the moment that version changes, isn’t a nice-to-have. It’s the difference between staying compliant and putting someone’s health at risk.

4. Knowing What Should Exist — Before Someone Has to Ask

Filling gaps is reactive. The real shift is planning content ahead of time — AI suggesting what needs to be written next, based on patterns that would take a human months to spot.

This looks like:

pulling common themes from tickets, calls, and search behaviour into content suggestions recommending the right format, not just the topic — a simple decision-tree for troubleshooting, not another wall of text drafting a rough first version from scattered sources like emails or chat threads, for a human to finish and check comparing the product roadmap against current content, so articles are ready when a feature launches, not three weeks later

5. Measuring What Actually Matters

Page views were never a real measure of value. They only show attention, not impact. AI lets KM finally measure what content actually achieves.

This means tracking things like:

whether an article actually solved the query, or the customer still had to escalate; how much faster an issue gets resolved when the article is used; “zombie content” — articles that take effort to maintain but barely get used or barely help — as candidates to retire; whether content usage connects to real outcomes, like fewer tickets, faster onboarding, or lower churn

No, AI can never replace knowledge managers. Because they are the ones who feed AI knowledge and information that it requires to do its job of keeping the KB updated.

All what it does is— it frees us from being full-time content archaeologists, digging through what already exists, and lets us focus on what KM was always meant to do: getting the right knowledge to the right person, at the right time, without them having to go looking for it.

AI doesn’t fix KM by doing the writing for us. It fixes KM by finally giving us visibility into the health of what we’ve already built — and the foresight to know what’s missing before it becomes someone else’s bad day.

That’s not automation for its own sake. That’s KM finally working the way it was always meant to.

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