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How to retain your institutional knowledge when employees retire (and how can AI simplify this)

In the midst of pandemic, the world of work is facing a perfect storm when it comes to retaining talent and knowledge. While in the past, informal knowledge sharing was easily accessible over “the water cooler”, remote working and the current set up caused lots of silos for most of that expert know-how.
Many more factors add to the complexity of the situation. Baby Boomer generation is reaching retirement age while younger workers are changing jobs more frequently than ever before following the trend of the Gig Economy.
On top of that, since leaving EU, with the new regulations and more complexity with hiring employees from abroad, many UK companies may suddenly find themselves in a shortage of a top talent and the specialised experts.

Since skills, knowledge and experience are vital to a successful business and the pace in which it innovates, retaining existing institutional knowledge is an increasing priority.  How can you guarantee that your company’s know-how won’t just walk out the door and jeopardise your brand and positioning? The short answer is: You can’t. But there are ways utilising a combination of analytics and AI techniques, along with corporate training and knowledge replacement strategies, can help.

When finding people with the knowledge you need is getting harder, a good idea is to focus on keeping and developing the knowledge from the people you’ve already got.

Time doesn’t stand still.

knowledge management ai

Inaccessible information is as a good as a deleted one.

When a senior member of your team decides to leave one of your most urgent priorities is knowledge transfer. You know the feeling: this team member possesses critical knowledge and if that information leaves with them, the repercussions will be felt throughout the organisation. For many, the departure of a critical employee triggers a list of meetings to capture as much knowledge as possible before they go.

Many organisations struggle to understand how to collect and access that expert knowledge. But even beyond employee exits, there are plenty of reasons to develop and implement a knowledge management strategy. Consider how a weak knowledge sharing process might impact your onboarding and intern programs, or employees transferring to a new role.

Every year, over four million Baby Boomers leave the workforce in the U.S. alone, with 10,000 people a day hitting retirement age. In the U.K. and many other developed countries, over 30 percent of the workforce is over 50.  Most of those workers are in leadership positions, and when they leave the company, almost all are taking with them decades of accumulated skills, experience, networks and personal business relationships, as well as first-hand know-how why their projects have evolved the way they have.

The good news is, that a lot of that specialised knowledge and know-how is already stored within your organisation. With the right knowledge management practices, you can unlock its value to be able to share it with all your employees. By empowering your organisation with the AI technologies such as Machine Learning and Natural Language Processing, you’re able to discover and share the knowledge you’ve probably even didn’t know you had.

Unlocking the knowledge value with the help of AI.

An impactful knowledge management strategy equips employees with unique ways to share, capture, discover, and retain knowledge. Knowledge sharing occurs everywhere – over an email, reports written for other colleagues, in customer service ticket responses and so on. Most of that knowledge is a data locked in the silos, spread across many different systems. By using AI tools like our Untrite AI engine, you’re able to automatically link relevant information (think – Wikipedia referenced articles), see the bigger picture of the potential solution and reap the benefits of the existing knowledge. You don’t need to reinvent the wheel when analysing a problem. Since AI (more precisely speaking – Natural Language Processing) is able to understand the context of the problem, our AI powered tools can easily and timely reach previously unseen solution, for example, taking advantage of similar or same cases which have been documented by retired colleagues.

AI streamlines the discovery of knowledge.

The amounts of information produced in virtually all organizations is exponential, the problem isn’t lack of information it is the discovery of it and forming knowledge out of it. Luckily, the latest AI technologies like semantic search, Natural Language Processing and Machine Learning make it simpler for the employees to find the knowledge they are searching for in a quick and easy way, and often in a real time.

In short, Natural Language Processing and semantic search eradicate the requirement for Boolean search, intricate hierarchies, and granular tagging and classification. It enables the employees to find the knowledge by using natural, human language. After that, it makes inferences and provides results as per the search terms, synonyms, and implied context.

Machine learning, however, supervises both the terms as well as the user behaviours over time to know what workers are searching for. Machine learning analyses and predicts what employees search for based on the knowledge that helped other workers having similar queries earlier. It can also learn and adjust results based on the user’s interactions so with time, it gives more accurate results.

Most importantly, AI offers instinctive search capabilities, making it easier for employees to take advantage of all the knowledge stored in otherwise unaccessible data sources, such as CS ticket responses, tutorials, reports etc. Of course, good AI tools work with your user permission levels, so your employees will only see the data they could otherwise search and access manually.  

AI helps in keeping the knowledge base content updated.

AI also helps in dealing with the other problematic knowledge management process – knowledge maintenance. Probably two of the biggest problems organisations face when it comes to lots of data are – versioning and outdated knowledge. Keeping obsolete information in the knowledge base can prove to be detrimental and costly. The likelihood of employees making errors increases, which results in them not using the source altogether as they lose their trust in it.

Machine Learning and other AI tools solve this problem by assisting the companies in maintaining their knowledge base and keeping it updated automatically. Because AI understands context of the information, it will provide the latest, most relevant information. On noticing a specific result performing miserably, it stops sending people that particular information and sends an updated one, which can satisfy user intent.

AI links data from disparate, siloed sources.

riverflowAnother major knowledge management obstacle faced by the companies is the irregularity and inconsistency of the workers to capture and share information in a similar way. It’s a common practice for different team to use tools which often need to contain the same, overlapping information. E.g. product teams make use of project management tools;  sales reps handle their knowledge in a CRM tool and support teams capture and share knowledge in a system of ticketing.
This siloed data storing practice quickly creates a knowledge discovery issue, where workers don’t know where to find the knowledge they require.
AI-powered tools assist in connecting and blending the knowledge across various systems, thus giving all the workers with the right access permission, the knowledge they require, no matter where they live. The fusion of AI’s capability to rapidly search through massive libraries and its ability to predict what users are looking for makes it a powerful tool for solving some of the most significant knowledge discovery issues that enterprises have faced in the past.

The broader benefits of active knowledge retention.

When it comes to retaining the expert knowledge, analytics and machine learning will never replace the know-how that decades-long experts have. Even if a lot of the knowledge these experts produced and documented can be retrieved easily with the use of AI, expertise documentation should be as intentional as possible. Before your product experts leave the company, it is imperative for the company to extensively interview these experts about “secret sauces” so the knowledge can be documented and interlinked with what’s already stored.

An added advantage of encouraging older employees to share knowledge is a boost in engagement – and with that retention levels – of younger employees, as learning and developing new skills are a vital part of career development.

Another good practice of an active knowledge retention are mentoring programs. These can also act as successor training schemes, giving more junior employees a clearer sense of their career path. Strong training programs are in demand among today’s increasingly mobile workforce and thus employee-first environment. The well-constructed knowledge hubs can be a valuable pull-factor for jobseekers when supported with the right mix of internal communication and encouragement.

The Holy Grail Of Organisational Knowledge

By having the above programs in place and taking advantage of AI, it can help your staff to increase their sense of value and increase your organisation’s capacity to make the most of its employees’ skills. The best way to retain your knowledge is to be aware of what knowledge you have in the first place.

To get inspired arrange a demo of Untrite AI.

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