Why Traditional Knowledge Management Doesn’t Work in the Digital Age

Knowledge management—the efficient handling of information and resources within a commercial organization—is not a new concept in business. In fact, it has been around since the ’90s. But even as businesses are still trying to get a handle on knowledge management practices, the concepts and processes developed back then are aging to the point of becoming obsolete.

In other words, knowledge management as we know it is not destined to survive the digital age. The issue is that digital technology has, far from making management easier, contributed to the explosion of available information.

To survive the digital age, organizations will need to start thinking, instead, in terms of knowledge automation.

Proof that Knowledge Management Falters in the Digital Age

Knowledge management grew out of a realization that large organizations needed to organize their information in a more holistic manner. This meant capturing and retrieving needed information from a variety of sources: databases, documents, policies and procedures, and even the expertise and experience implicit in the practices of individual employees.

This sounds like just the sort of thing that should be easy with cloud technology and better integration tools. But consider:

Information is everywhere in organizations. But employees are spending an inordinate amount of time trying to find just the information they need, when they need it. As the information in an organization grows, this situation gets worse, and management of that information itself because an ever greater task.

In other words, knowledge management has led to better collection of, and access to, information…but it has failed to make the retrieval of pertinent knowledge faster or easier.

What Knowledge Management Promised

Still, the original needs that knowledge management was supposed to address will not go away. In an ideal world, proper knowledge management would enable things like:

  • Rapid data-driven decision-making
  • Fast dissemination of relevant information across siloes
  • Minimization of duplicate efforts
  • Broadcasting best practices and solutions in a timely manner
  • Better utilization of existing knowledge assets, both formal and informal
  • Better leveraging of SMEs’ knowledge
  • Better use of SMEs’ time
  • Standard and repeatable processes, procedures, techniques, and templates
  • More accurate and timely information for sales teams
  • More rapid response by customer service and support teams

Notice that these benefits are about speed and relevance as much as anything. But these are exactly the areas where traditional knowledge management has been slow to develop.

Even more disheartening is the cost burden that knowledge management has brought, without realizing return on those investments. Improper planning, design, support, and evaluation can easily lead to a lack of widespread contribution, which further erodes usefulness, relevance, and quality. And, even when moderately successful, using older knowledge management system is costly to maintain and difficult to scale because of their dependence on “top down” knowledge management rather than “self-building” knowledge.

From Knowledge Management to Automated Knowledge Curation

“Knowledge automation” is becoming a popular way to describe how machine learning and artificial intelligence (AI) can be used to automate more of the knowledge management process. (“Automated knowledge curation” is another, although less popular. It means much the same thing.)

Much of the knowledge within organizations is generated by the activities of your people. They email questions and answers back and forth. They use informal communication tools like Slack. They produce wikis and sales sheets and blog content. All of this knowledge is there in the organization—it just needs to be curated and made automatically available with the touch of a button.

This is the idea behind “self-building” knowledge: knowledge that is already present in an organization and that is continuously curated instead of “managed.” Knowledge automation creates access to self-building knowledge, rather than relying on “top-down” management.

After all, information itself is cheap; as the list above shows, it exists in many ways, and in many different forms. That information only becomes knowledge when the right slice of information can be applied in the right situation.

For example, suppose a prospect has a question about a particular feature on one of your newer products. Your sales team should be able to answer that question without wading through a pile of sales sheets and development wikis—or worse, waiting for an answer from a SME halfway around the world.

Another example: A customer has an issue and reaches out to your organization via social media. Your customer service team now has to query several separate systems in order to handle this: a case management system, an internal incident management system, a knowledge base, and several off-band communication channels. This would usually take a full day; imagine, though, if the relevant knowledge in these systems could be made available instantly, so that a reply could be made within the hour. (In fact. call center costs and volumes can decrease by as much as 30% when better search and automation tools are implemented.)

These are just a couple of simple examples—you can follow the links for more detailed use cases. Still, they are good examples of why traditional knowledge management is not surviving the digital age. They also show why we developed our app, Nimeyo, as a way to automate the gathering of information across channels. It is a knowledge automation solution that brings both speed and relevance to those who need the right information at just the right time.

Like Beanie Babies and dial-up internet, some things should be left in the ’90s. It’s time to update the way we access information in this digital age.

Two AI Use Cases for Customer Support and Services

In our last post, we highlighted the fact that many companies assume that the more “human” parts of business —sales and customer service—have little to gain from Artificial Intelligence. Of course, this assumption is incorrect, and liable to mislead companies who could otherwise stand to benefit.

Consider:

  • According to Forrester, 72% of businesses say that improving the customer experience is their top priority.
  • Most contact with customer service now takes place via the web using a chatbot, via email, or via social media. The set of skills and tools needed here are different than, say, handling a case via phone.
  • Customers have ever-growing expectations with regards to response time. A decade ago, customers were willing to wait 24 hours for an answer to a question or a solution to their problems. Now they want an answer right away…if not instantly.
  • As business grow and expand their global reach, more and more customer support cases begin to look similar. Solving each case independently is burdensome, if not impossible.
  • The average customer triage and resolution cycle takes five or more steps having to do solely with information search among the organizations various data sources.

In other words, the need for a human being with “people skills” is diminishing just as the strengths of artificial intelligence agents—such as the ability to query multiple data sources quickly and efficiently—are coming in high demand. Indeed, one prediction holds that, by the year 2020, more than 85% of all customer interactions will be handled without the need for a human agent.

But what does customer support via artificial intelligence agent look like? Again, we can illustrate this best with two use cases around our own knowledge automation solution, Nimeyo.

Use Case 1: Resolving Customer Issues When Knowledge is Siloed

The Context:

Today, customer service reps are expected to resolve customer issues faster and faster, even as they take on huge case volume to justify their job roles. In order to do a good job of meeting customer expectations and succeed in their roles, a single pane of information and knowledge access is essential.

The Challenge:

Again, the typical customer resolution in an organization of any appreciable sizes takes researching five or more data-sources. These include:

  • Querying customer-facing case management systems (such as Salesforce Service Cloud or Zendesk) to identify duplicates and bring up relevant contextual information
  • Comparing across internal incident management systems (such as Jira) to find similar cases already being addressed, or that have recently been addressed successfully.
  • Searching KnowledgeBase articles and wikis for quick resolution of common problems, or concise answers to frequently asked questions
  • Combing through off-band but relevant conversations in emails or Slack channels

Already, this process is pretty daunting. When you consider that two or more of these steps could be taken for cases that are very similar, and for which solutions already exist, it becomes painfully obvious how much time is wasted and productivity sacrificed. Currently, organizations are struggling to find ways to integrate these various sources into a single pane.

How AI Helps:

This scenario is easily fixed with a solution like Nimeyo knowledge automation. Using Nimeyo, customer service reps can address cases more readily, thanks to instant access to knowledge of similar cases across content silos of customer issues and internal product ticket systems.

Nimeyo can also integrate with management systems like Salesforce, as well as incident management systems like Jira and chat channels like Slack. It can then access these systems instantly and use the information in them to help zero-in on the resolution for a given case, relieving the customer service rep from having to do these searches manually.

More importantly, Nimeyo helps customer service reps deflect more cases by giving them increased visibility of similar cases across customer issues and internal product ticket systems.

All of this results in more rapid response which, ideally, leads to improving their first contact and/or first time resolution times.

Use Case 2: Self-help Bots For Customers and Service Teams

The Context:

As we all know, a lot hinges on having a positive customer service experience: It can mean the difference between a loyal customer, and a disgruntled one. Speed and accuracy matter crucially, and customer demand instant responses. If they don’t find an answer immediately, they are disappointed and are quick to share their bad experience on social media or other public channels.

But increasing complexity of products and services, along with the high turnover rate of most call centers, means that it is almost impossible for service reps to keep up with the content needed to resolve issues in a timely fashion.

These dynamics are fundamentally changing how both customers and service reps seek out information. For example, the majority of Millennials actively avoid situations for which human interaction is necessary to solve an issue, much preferring self-service options instead. One study of the generational divide in customer service found that a whopping 72% of Millennials believe a phone call is not the best way to resolve their customer service issues.

So how are consumer resolving their issues, if not calling customer support? Right now, they are using a mix of chat bots on websites, social media sites for the relevant brands, chat channels, and Google searches. In other words, they are already going with digital self-help solutions.

The Challenge:

Companies face two choices: Either improve the self-help bots they make available, or better empower their service teams to compete with these bots.

Most of the current self-help systems are web centric, so customers are relegated to searching for a solution themselves—and are often confronted with more pages than they are willing to review. Even if they do find the  answer they seek, it may not be the most accurate or latest answer.

That said, many Customers are still “put at ease” knowing that there is a customer service rep in the interaction; but this “human touch” engagement is costly, often only available during business hours, and is (for the most part) unscalable.

How AI Helps:

With a Nimeyo AI solution, customer service organizations can create a foundation of knowledge and insights from approved content sources like FAQ databases, product documentation or issue tracking systems. Subsequently, bots or auto responses act as the first line of defense to respond to common question with known answers or fixes.

When a customer sends an email to a support email address, the email autoresponder can look at the knowledge available to instantly respond with links to most relevant answer. If the customer is happy with the answer, then the customer service team can mark the issue as resolved. If the customer indicates that more assistance is needed, a rep can reach out for additional information.

What about availability and scale? Typically, a customer service chat is available only during business hours (unless you have a globally distributed service teams.) However, if a customer initiates a chat with a rep during off hours, an auto responder bot can respond to customer query with knowledge from approved sources. Queries such as the status of a case, answers to FAQs, or product specific questions can be responded to in seconds without any human intervention. Again, the chatbot can be the first line of defense before a rep needs to be engaged.

Again, these are just two simple ways that AI agents are changing the face of customer service. Counter to many of the assumptions surrounding AI, human beings will always have a role to play in customer support, since there will always be difficult cases requires a person’s  ability to understand the nuances of the case and find creative solutions. Increased productivity comes when human beings can be freed from routine and easily-solved cases, and allowed to focus on more complex cases and tasks. Artificial Intelligence can potentially leave service reps free to tap into the critical thinking and problem-solving skills, not to mention emotional intelligence, when they are needed most.

If it still sounds like a pipe-dream to empower human interactions through AI technology, we recommend you try Nimeyo yourself to see how this can be done in your organization. Sign up for a free demo, and we would be delighted to give you a tour and show how the Nimeyo AI can be best used by your customer service teams.

Wrangling Enterprise Data

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In our previous blog, The Myth of One Golden Informational Warehouse, we described the ideal informational warehouse where all relevant corporate information resides in an easily searchable, coherent, and up-to-date form. We also discussed how individual preferences, habits and organizational culture make it hard to achieve such a warehouse.

In this article, we will try to analyze this problem from the “systems” perspective. Systems, in this case, consist of tools, processes and the data that they hold and operate.

Data and Tools

blog_enterprisedata

The above diagram describes various types of data and the tools and services a typical organization uses. For the sake of simplicity, this diagram only includes systems that are pervasive and used frequently by employees.

As you can see, the tools on the left tend to be “systems of engagement” – natural, conversational and dynamic; while tools on the right tend to be “systems of record” – highly structured, curated and managed. Moreover, communication mediums like E-mail and IM are noisier (e.g. due to language ambiguities) from the information perspective compared to structurally curated information repositories like CRM.

Unfortunately, internal knowledge organizations in enterprises have this unenviable task of wrangling all these sources of information into a cohesive, searchable, navigable solution that is non-disruptive, secure, and easy to use.

The Problem/Solution Gap

It is quite easy to see why knowledge or IT organizations prefer stricter, more structured services (right side of the spectrum). For example, extracting a customer name or an employee assigned to a task is much easier from a CRM or PM tool where specific fields capture that information. However, it is much harder to extract such information from Slack messages or E-mail conversations.

On the other hand, employees hate to curate information for the system. They have a job to do after all, be it development, sales or marketing. Documenting something is just an overhead they would rather avoid.

And therein lies the dilemma. Employees are communicating through IM or E-mail and generating tons of useful information in a dynamic work environment – and almost none of it makes it to the system of record. Meanwhile, the more structured sources that contain no IM or E-mail are the places from where business intelligence is derived.

Needless to say, this deep rooted and pervasive disconnect has created a gap in the way employees access corporate information relevant to their jobs.

Enterprise Wiki: Shifting to the Right

A good example of the tension described above is Enterprise Wiki. A few years ago when wikis were all the rage, there was a massive effort to shift collaboration from chat and E-mail to wiki.

blog_wiki

As demonstrated in the diagram, many forward looking organizations thought that this new initiative would make their data easier to manage by shifting to a more structured, organized, and system ready service.

Unfortunately, this shift required a change in behavior from employees as they had to learn a new user interface and process which was less natural than before. While some appreciated the change, others were not quite so enthusiastic. For example, Product Marketing may love wiki pages as they are well organized and easy to manage. However, for the Sales team (supposedly the benefactor of this content), such a solution would be a burden as they may not have access to corporate network all the time and may want a mobile friendly solution. They just need answers, not documents. They would naturally stick with E-mail or chat.

Indeed, research from MITRE has suggested that people have resisted putting information onto wikis because:

First, we uncovered a reluctance to share specific information due to a perceived extra cost, the nature of the information, the desire to share only “finished” content, and sensitivities to the openness of the sharing environment. Second, we discovered a heavy reliance on other, non-wiki tools based on a variety of factors including work practice, lack of guidelines, and cultural sensitivities.

In other words, the failed adoption of wikis was part of the systematic difference in expectations between the consumer and the producer of the information.

The Ideal Solution

With organizations wanting more clean and structured data that is easier to slice and dice for business intelligence and employees wishing for more conversational style mediums, an ideal solution would have to fill the gap of expectations.

In an ideal world, employees would be able to converse in a free-flowing manner using whatever means their group feels is best, while innovative technologies and products would analyze that content and extract valuable business information to form a structured database. Thankfully, traditional search technology along with linguistics and machine learning applied on very specific areas like sales and support can make this problem tractable.

We at Nimeyo are working on technology where employees can continue use E-mail, chat, SharePoint, Salesforce or anything else they desire, while our algorithms build the knowledge meta-layer on top of that.

With our “pods and bots” approach, our pods aggregate unstructured information and add a layer of intelligence, while our bots deliver answers right into your workflow. Essentially, we require no change in user behavior while still tapping into one of the richest sets of corporate information.
Drop us a note if you would like to learn more.

Can Enterprise Search effectively serve employees’ needs?

My toddler has an impressive collection of toys. He tries to keep his favorite ones somewhere “safe” but then he cannot find them when he wants to actually play with them. While trying to find it, we both know that the one that we are looking for is somewhere in the house and yet it remains alluringly out-of-reach since the exact places we determine to look for never have it. It’s frustrating for him to have this happen on regular basis.

Unfortunately, millions of enterprise employees feel similar frustration when they can’t find the information that they know is around them in various forms. They could be in mailing lists or in SharePoint or Box repositories, or in internal chat rooms, or on some internal wiki pages.

Realizing the potential value of unearthing information that employees need to do their jobs, companies – particularly large ones – employ enterprise search. Unfortunately, most of those engines remain poorly deployed and minimally used by the end users.

imageSo the question is why do enterprise search engines do such a poor job at engaging users whereas search outside of the corporate firewalls are part of our daily lives?
Although there are number of technical reasons, we believe the key problem is a “lack of user-orientation”. In other words, these solutions are neither attuned to the actual needs of the end users nor they understand the data itself in a meaningful way to be able to serve it in a meaningful way.

Let’s take few examples –

  1. Content and Users: Search engines’ key strength is in indexing wide ranging data types – web pages, documents, CRM systems etc. So when user searches with few keywords, search engines define success by uncovering wide-ranging data in a sequential manner based on some ranking criteria.However, not all data types are created equal. For instance, email communications are a lot more meta-data rich and time-relevant compared to documents. If used intelligently, such data specific analysis can be immensely useful in understanding how row communication relates to the end user needs.
  1. User interface:   Needless to say, we all are used to typing a simple search query (or question where there is a unique answer – e.g. “Father’s day 2015”) and expecting search engine to return “satisfactory” results. However, in a corporate context, this model is highly limiting as any one article or document is unlikely to provide a comprehensive answer in most real world scenarios.For example, when a sales rep gets into a competitive situation, a query like “MyCompany vs MyCompetitor” should surface variety of information including product differentiation, pricing, and nuggets from other similar situations. All these pieces are equally important to put together a competitive tactic to win the opportunity. A linear listing of results based on uniform ranking criteria does not do justice to needs of that sales rep.A UI that allows users to navigate through these various pieces in a consistent manner and “assist” in creating a cohesive picture would be much more effective in creating engaging user experience.And finally,
  1. User preferences and behaviors: In most of our enterprise search experience, presentation of results is “black magic”. Let’s say you searched for some information today, worked through the hits and were fortunate to find what you were looking for at the 20th You had to put the effort but you found what you were looking for!Unfortunately, say a month from now, if you are looking for the similar information through a similar query, you would still find the information deep down in the ranking.If solutions allowed ways to capture user preferences – expressed implicitly and explicitly – it would be able to return results that are a lot more aligned with what the end user needs are.

We at Nimeyo believe that to achieve the true potential of enterprise search, we need to stop viewing it through the prism of consumer search technologies.

To fulfill the promise, enterprise search products must understand not just who the user is and what role she performs in the organization but also identify optimal mechanism through which to deliver knowledge to the employee.