Briefing Knowledge Leaders Conference 2025. Key Takeaways: The art of making a phone call, cathexis and the evolving role of the KM team in today’s tech-enabled world

Jenni Tellyn and Anthony Olisa
51 minutes ago
11 min read

This year’s Briefing Magazine “Knowledge Leaders” conference was, once again, a feast of ideas, debates, and practical wisdom for anyone in the business of law firm knowledge management. We wanted to share a handful of the things we found most interesting from the day.

What do future KM teams look like?

We are often asked to gaze into the future to reimagine what KM teams might look like when AI tools have the potential to take some of the admin drudgery out of getting documents into KM systems and drafting punchy current awareness alerts for us. Looking 5 years into the future may feel impossible as AI tools are developing so fast but we are reminded that we work in the legal sector and therefore how resistant to change firms are! KM teams understand risk, technology, processes and the legal knowledge required to deliver excellent client service. Being embedded in the practices allows the KM team to be well-placed to add more value than other Business Services functions. KM teams which do not exploit this will miss a trick and new Knowledge Lawyer hires must be prepared for this to be their role.

KM should be a no brainer in its strategic importance at firms, especially in an age where firms have simply got to be able to leverage their collective expertise effectively in order to attract and retain clients who can do the initial work themselves with AI tools now. KM teams must seize opportunities to demonstrate how they contribute to the firm’s quality content stores, to its subject matter expertise and therefore to their firms’ long-term resilience in order to remain strategically vital.

The generation gap in law firms was commented on several times as firms are grappling with how to enable all their staff to thrive. It was commented that there is often a culture clash as “Juniors often expect leaders to teach whereas seniors expect juniors to be curious.” KM teams have always been the bridge between the capabilities of systems and the practice of law and they must continue to evolve to bridge this gap between the different generations in their firms.

KM teams (especially where they also have “Innovation” within their remit) are at the heart of “transformation” (the current buzzword) at their firms. Not all members of KM teams are excited to be on this journey! Those whose USP is their legal expertise may increasingly find that this is not enough in isolation as technology democratises access to legal knowledge and problem-solving. Knowledge Lawyers might see their role getting more and more client-facing as they are deployed to the front line to help bring stickiness to client relationships by providing access to the complex knowledge in context that democratised platforms can’t give clients so effectively.

Given the role of technology in enabling the delivery of legal services of today and tomorrow, it is vital that KM teams have the skills to understand how the tools their firms have invested in work under the bonnet so they can ask sensible questions of vendors and understand if the tools will meet the expectations of the partners, clients and insurers. There was a recognised need for greater transparency from vendors about how their tools actually work so that firms can evaluate when testing the tools what impact changes in the underlying models will have on outputs. Vendors are currently unwilling to reveal details of the system prompts their tools use but they may be pushed on this further in future as understanding and sophistication of their customer base grows.

Data literacy of KM teams is also increasingly important as the proprietary data of the firm is its biggest asset in the fight to remain competitive in a market where clients also have access to AI assisted tools and can increasingly tackle lower-level tasks themselves that in the past they may have asked their outside counsel for help on. Getting the firm’s data into the right systems and in a clean and structured format is a key responsibility for KM teams and one which is often overlooked by IT, Innovation and management teams in firms. It may not be the sexiest part of the job but getting a robust firmwide taxonomy in place is one of the areas a KM team can really add value at their firm. Nailing this will inform good legal processes and create tighter datasets for search and retrieval of the right knowledge at the point of need.

The market for AI-assisted tools is very crowded, with functionality overlapping across products. KM teams can play an important role in helping their firm get the balance right between horizontal and vertical/practice-specific products and advising on where investments should be made.

As AI-assisted tools are used more and more, risks go up so we see KM teams leading on governance frameworks and advising on guardrails and designing workflows for use of the technology as they are close to how they are being used in practice in the legal teams. As AI democratizes access to knowledge, KM teams become even more vital to curate, validate, and ensure the quality of information being consumed by lawyers and the tools they rely on. Some KM teams are being asked to do more pre-curation so client-facing lawyers can turn off alerters from external research resources to free up their inboxes a little. They are being asked to be guardians of prompt libraries and to produce Quick Reference Guides on hot topics (think: “talking points for clients”). Process mapping might come back into vogue as KM teams help their firms to pin down how they deliver their legal services so they can unpick how agents might be deployed to help them do so more efficiently or in a different way. The ultimate aim is to ensure that lawyers know what good looks like before they start using AI-assisted tools and to ensure that they have the necessary context for the “why” of their work rather than just focusing in on the task at hand.

Where firms have separate Innovation teams, the role of the KM team (and the Knowledge Lawyers in particular) in helping to test and pilot AI-assisted technology has been in issue. Busy lawyers might not always be the best testers of products but deploying a combination of practicing lawyers and Knowledge Lawyers to test the tools has found success in many firms, especially in longer trial periods of up to 12 months. Knowledge Lawyers clearly have a lot of demands on their time but carving out time to take on this role in preparing the lawyers in their groups to effectively use these important technologies feels essential. This should not extend to checking the outputs of AI tools which have been generated by lawyers for their matters. It should remain the responsibility of the lawyers to ensure their work product is accurate.

Training in the age of AI

The key takeaway from this excellent panel discussion was to use AI to augment old school training methods and use it as another learning tool in your arsenal. It is no longer necessary to train lawyers how to draft from a blank piece of paper as the technology is there to enable them to start with a better idea of where they will end up. Just as document automation tools were criticised when they were first introduced (“How will juniors know what the tool has stripped out? They will become robots!”), AI tools can be used to help juniors to explore how clause wording has evolved or to see whether counterparties have accepted proposed wording in the past. They can also be a safe space in which to ask foolish questions (so long as the quality of the answers is up to par!) and can lead lawyers to new opportunities for collaboration and the creation of new networks.

The panel suggested that the time lawyers might save through using AI-assisted tools as a knowledge retrieval tool they could use to apply to training on the human skills that AI-assisted tools are a long way from developing, such as those espoused by the O Shaped Lawyer movement. The art of “cathexis” (a new word we learned from Professor Robert Rowland-Smith) highlighted how humans are good at picking up details of nuance and mood that robots (currently!) cannot. Juniors are being schooled in developing skills like applying judgement, critical thinking, connecting with clients in distressing situations to be the calming voice of a trusted advisor, the art of effective supervision of juniors, as well as making phone calls IRL (which feels alien to the latest generation coming into the profession who communicate in a different way with peers!). The analogy was given of juniors being people who have learnt to drive in an automatic car and have no idea what an engine sounds like when they need to change gear.

The age old problem with AI tools that juniors “don't know what they don't know” was raised and caused some firms to think about who should use some AI tools (with more senior lawyers being better equipped to use legal research tools safely as they already have an idea what the answer they are expecting to see is and are more likely to spot hallucinations). Firms report changing the structure of their training frameworks in the light of advances in AI. This means not just preparing juniors to use the technology safely but taking the training up the chain to seniors and how they are deploying it, so the right level of supervision and validation is in place. The idea of ensuring that supervisors are clear when they give instructions to juniors is not new but it feels sensible to ask them to clearly outline whether they want the junior to use the technology to just get a general gist of an issue/area of law (level 1), whether they want them to orient themselves in the subject area then delve into the client’s problem using either traditional sources or using the tech as a “thought partner” (level 2) or whether to go back to the primary sources and research the issue without using AI (level 3). Normalising this clarity and asking juniors also to be clear on the level of their review and the sources behind their work feels like a sensible guardrail to put in place. Digital fluency strategies deployed at firms are about ensuring that lawyers can confidently use the right tools for the right tasks and use them optimally (to enable the firm to achieve some ROI).

Ultimately, this panel reminded us that the golden age we look through our rose-tinted spectacles at when we were trained in the 90s/noughties wasn’t wall-to-wall great training so training in the age of AI isn’t necessarily worse! We had hope that training could be made better as firms have been given a wake-up call to relook at how they roll out training to make it as effective as it can be and to examine how they recruit lawyers to make sure they have the right people in the building with a predisposition for thinking critically.

Dealing with information overload

It’s an old chestnut but we still hear lawyers complaining about the flood of emails, Teams chats, WhatsApps, instant messages, Viva Engage posts, intranet dashboard notifications, and LinkedIn pings taking over their lives. Copilot and other AI tools are seen by the overwhelmed as just another class of distractions lawyers are being asked to engage with. Clearly not all information is valuable, and our job as KM professionals is to help people cut through the noise. In our roundtable on this topic, we discussed balancing the push and pull of communications to lawyers (hoping that AI enabled search might enable lawyers to self-serve more robustly in future so that only key information is pushed to them by KM teams). AI-assisted summarisation tools and agents are helping lawyers find what matters at the point of need and cutting through the less valuable “stuff”. Tacit knowledge flow around firms should also not be neglected in this age of AI. So lunch & learns, group meetings, and rewards for sharing what’s in people’s heads are all still valuable ways to share knowledge memorably.

Some intranet and knowledge systems now enable audience targeting so content can feel personalised to help lawyers focus on the information pertinent to them and cut through some of the noise. It is clearly also important to use the right channels for the right messages and be very clear and disciplined about “what goes where” so staff know which channels to visit for different types of information.

The challenge here is to try to use new technologies to help cut through the myriad channels to the nuggets of gold rather than add to the noise.

Different Pricing models in the age of AI

The “death of the billable hour” debate is alive and well, but now with an AI twist. This is clearly not a new conversation as firms had the same dilemma when technologies such as document automation were introduced. As always, the answers turn on firms having grown-up conversations with their clients about the scope of the work to be undertaken and what they will actually be doing for their fee (whether using technologies to support the work or doing it “old school” style). Clients (under pressure to reshape their spend on legal services and their own AI strategies) might move away from asking their law firms to deliver time/inputs to seeking delivery of value/outputs. Firms should focus on helping them to do this in partnership.

This requires not just thinking about using AI as an efficiency play around saving time but focusing more deeply on how to reshape the service which is being offered. This is not about passing the cost of tools on to clients (which is not always easy if a task is defined by its complexity not the volume of data involved). Firms need to understand their clients, the outputs they want and their risk appetite in order to continue to price work competitively. We discussed tiering pricing differently depending on risk and having a combination of the “human in the loop” and AI-assisted work depending on the task at hand. For example, clients should have a choice of having human lawyers review whole data rooms line by line or using an AI-assisted tool to pull out the material points and have humans conduct verification of the results. Or having a “quick and dirty” AI-generated translation to get the gist of what a document says versus paying for a specialist translator to work on documents with higher value, complexity or where regulators/courts require a higher standard of certainty. Clearly the higher the value or impact on the client’s business, the less risk the client may wish to take and therefore the more they may be prepared to pay for outside counsel (who they can sue if it goes wrong!) to help. Firms need to be able to truly show where they add value to their client in order to justify their pricing. It may be that by using an AI-assisted tool, they can deliver something better than they would otherwise have been able to. For example, meeting an impossible deadline, showing the client a trend or insight they wouldn't otherwise have had without the tool’s ability to crunch vast quantities of data effectively, etc.

Today’s legal market requires firms to be able to partner with their clients to deliver the desired results together. This might mean that clients do some of the work themselves then hand some things off to their law firm (a) where the client doesn’t have specialist expertise in-house, (b) where the firm has access to AI-assisted tools which the client does not have the budget for themselves or chose not to buy and support internally or (c) the client doesn’t have the volume of data/underlying knowledge content to get good results from AI-assisted tools compared with the law firm. Having a grown-up conversation about this upfront may be more successful than waking up one day to find that a client has dropped the firm which feels too expensive now. It also allows firms to tackle the issue of clients serving up a dog’s breakfast of a first draft they have generated themselves for lawyers to “bless” rather than understanding that it would in fact be cheaper to start with the law firm’s own template.

We also discussed the rise of client demand for fixed fees as a pricing model and how firms need to be clear in each practice area, office and work type whether a fixed fee is allowing it to preserve or increase profit margin or simply to allow it to reduce write-offs. Examining the data on fee quotes, amount billed and realisation rates and understand the variables at play is an important component in this equation.

Ultimately, there’s no one-size-fits-all conclusion to how to price services in the age of AI. Flexibility, transparency, and understanding what your client values are key.

If you want to discuss the shape of your KM team of the future, the evolving role of the Knowledge Lawyer and how to bring them on the journey, wish to tackle the creation of a robust firmwide taxonomy or discuss any of the other issues raised in this summary, please get in touch.