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

mediumThis post was originally published by Kamila Hankiewicz at Medium [AI]

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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 — those born between 1946 and 1964 — is reaching retirement age while younger workers are changing jobs more frequently than ever before following the trend of the Gig Economy. 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 that utilising a combination of analytics, IoT, 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, before they leave.

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While the market likes businesses with a long tradition, it equally favours innovations and efficiency. Organisations like the association of Parmigiano Reggiano can’t afford to be complacent. The famous Italian hard cheese makers with its origins dating back to 13th century, deploys data analytics and sensors across 350 dairy farms to better track the cheese production cycle, and to monitor the diets of the cows that produce the milk. An easy access to data enables cheese crafters to preserve the centuries-old greatness of this cheese and the newcomers to quickly gain the knowledge on how to perform their job to the best standards.

When a senior member of your team decides to leave, may it be for retirement or new opportunities reasons, one of your most urgent priorities is knowledge transfer. You know the feeling: this team member possesses critical knowledge, may it be fixing the bugs within the legacy code, 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.
While conducting an initial workforce analysis to identify key employees whose knowledge you want to retain, it may also be possible to start developing an internal skills database to maximise the potential impact of staff who may not be utilising all of their expertise in their current roles.

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 untapped 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.

An impactful knowledge management strategy equips employees with unique ways that require to engage in four key knowledge management practices, i.e., sharing, capturing, discovering, and retaining 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 the problem. Since AI (more precisely speaking — Natural Language Processing) is able to understand the context of the problem, the AI powered tools can easily and timely reach and reference any information which is a solution, taking advantage of similar or same cases which have been documented by retired colleagues, in any format you hold.

Before AI became easily accessible, many tools were developed to simplify the two practices; capturing and sharing knowledge. Technological advances provided the workers with many techniques to put their knowledge into a shareable and searchable source — for most it ended up being SlideShare. However, the process of capturing and sharing knowledge gave birth to a new problem. Since the amounts of data collected became exponential, the discovery of knowledge was more problematic.

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 base 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 to the knowledge bases, 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 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.

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Another 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.

When it comes to retaining the expert knowledge, analytics, IoT, 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.

By having the above programs in place and taking advantage of AI, it can help your staff to increase their sense of value, it can also increase your organisation’s capacity to make the most of its employees’ skills. With failure to innovate or meet customer needs one of the biggest concern for businesses, knowing what your organisation knows is a crucial first step in ensuring that you’re able to adapt. The best way to retain your knowledge is to be aware of what knowledge you have in the first place.

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This post was originally published by Kamila Hankiewicz at Medium [AI]

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