Too often, digital transformations have an unintended consequence of fragmenting knowledge rather than consolidating it. This is due to a lack of a holistic approach for managing knowledge throughout the value chain. Knowledge management and digital transformation must be aligned and integrated, or the digital transformation will not provide the competitive advantage that it otherwise could. Knowledge management is critical to a streamlined digital workplace, and streamlining enterprise knowledge flows will make the management of any program, project, process, or initiative more efficient and more effective.
Due to the specialized knowledge required to support complex product and solution sales, expertise is by nature distributed to the business units and product or solution managers, each with their own knowledge base (when they have captured and codified their explicit knowledge, which is not always the case). If left to their own devices, management will resort to making do with information technology tools and resources they have available, rather than intentionally structuring explicit knowledge into an appropriately designed knowledge base using an architecture that supports other knowledge domains that are part of the transformation program.
Not dealing with knowledge as a core component of digital transformations will stifle innovation and functionality, leaving the organization flat footed. All organizations compete on knowledge and removing friction from knowledge management processes will be critical. This action will be especially important as their digital transformation strategy increasingly uses high functionality intelligent virtual assistants to enable knowledge sharing and knowledge access. But even without virtual assistants, enabling friction-free knowledge flows needs to be part of every digital transformation because of the significant competitive advantage offered by more agile methodologies and processes.
The Missing Ingredient to Digital Transformation: Scaling Knowledge Communities and Processes
The Holy Grail of digital transformation is reconciling the seemingly conflicting goals of high levels of customer service and pressure to reduce costs. The term “digital transformation” has become an all-encompassing term – in previous article about customer data platforms, I asked whether the term has lost its meaning:
In our digital era, the phrase “digital transformation” can mean anything and everything. It can mean digital tools, digital technologies, business processes, or customer experience. In some contexts, it refers to applications for big data analytics, machine learning and artificial intelligence as well as every other buzzword that marketers can come up with. Definitions from analysts and vendors include IT modernization and putting services online; developing new business models; taking a “digital first” approach; and creating new business processes, and innovation around customer experiences.
The overarching objective of a digital transformation program is to improve end-to-end efficiencies, remove friction from information flows, enable greater innovation and value creation that differentiate a company’s offerings, and strengthen the customer relationship. Having assisted large global enterprises with building the data architecture, supporting processes, and governance for multiple digital transformations, I see two broad classes of initiatives that seem to get funding and numerous others that miss the boat in terms of time, attention, and resources.
E-Commerce – the “Easy” Win
Improving ecommerce and streamlining the customer journey are two high-profile, well-funded digital technology programs that are meant to improve revenue, improve satisfaction, and drive down costs. Tens or hundreds of millions of dollars are invested in new generations of AI-powered technologies, digital marketing is revamped, customer service operations refined and yet still, many of these programs fail to live up to grand expectations. While they move the needle, something is still missing from the recipe that prevents the benefits from being fully realized.
The problem is that many organizations are complex, so streamlining processes and information flows requires participation from multiple departments that typically work in data and process siloes, while the customer journey traverses those siloes. People get their work done through collaboration both within and across siloes. They need easy access to a knowledge management system that surfaces organizational knowledge and information needed to support an employee in a particular business process, regardless of where that process is on the customer journey. Knowledge base design and development requires an intentional process using a holistic approach to building a reusable and extensible architecture.
But different parts of the organization frequently use different knowledge base and knowledge management tools and applications, which adds friction and slows down decision making and problem-solving. As an example, at one large manufacturing equipment provider, field service reps needed to look in over a dozen systems to repair and maintain custom-engineered installations. This meant searching for information in each silo, each of which had its own knowledge repository and knowledge process. This resulted in longer service calls, greater downtime, and higher costs. Multiple departments were involved in creating service and support content and did not use the same naming conventions or tagging process. Therefore, accessing information meant understanding the nuances of these various systems. Finding the right knowledge resource was time consuming, costly, and difficult.
The problem was solved by creating an information structure that described equipment, components, configurations, problems, error codes, troubleshooting procedures, and other aspects of the installation including as-built details from an ERP system. Information was broken into chunks so a tech could get an answer to a problem rather than have to search through large technical documents. This same approach is also being used to power cognitive AI – the chatbots and virtual assistants that surface information through conversations. While this work was done to improve conventional search carried out by a human, the approach leveraged text analytics and machine learning and paved the way for more advanced AI tools. This project is explained in a case study in the Harvard Business Review
The objective of the approach was to streamline knowledge flows across departments and ultimately to solve customer problems faster and at a lower cost. A correctly architected knowledge repository (knowledge base) within a knowledge management system facilitates knowledge capture, knowledge transfer and organizational learning, leading to a more agile organization and dynamic capabilities for serving customers as their needs and the market changes.
Capturing, Retaining and Applying Institutional Knowledge
How do knowledge flows relate to digital transformations? A group of people working in a team is part of a knowledge community, a place where experts come together to solve complex problems. But if the team is transient and disperses after producing a solution, that knowledge needs to be captured as enduring institutional knowledge. Expertise leaves the organization through attrition, downsizing, reorganizations, and retirement. For the enterprise to run, institutional knowledge must be embedded in processes, systems, tools, and documentation. Typically, this is captured in a knowledge base designed for the department or process, as well as in the accumulated experience of employees working in various departments.
Unfortunately, these groups often create their own knowledge bases, each of which uses different terminology and architecture. Collaboration and problem-solving are ad hoc, because too much structure can slow things down. But without some standards and structure, knowledge debt begins to accumulate. This is called technical debt in IT projects. Knowledge debt occurs when information is not well documented or organized for repeatable, intuitive access.
The outcome affects many aspects of information management. Enterprise search becomes a problem that is difficult to solve, repositories of knowledge get cluttered with outdated information, and inconsistent tagging of information makes retrieving the best solutions, answers, designs, deliverables, plans, and product documentation haphazard and difficult. This array of problems leads to friction, which slows down the digital machinery of the enterprise. The result is higher support costs, dissatisfied customers, compliance violations, manufacturing errors, and overall inefficiencies from the acts of heroics that people need to go through to get their jobs done.
Artificial Intelligence to the Rescue (really?)
Many organizations are pinning their hopes on AI solving these intractable, evergreen problems that seem to defy sustainable cost-effective solutions. Some organizations spend millions of dollars every few years cleaning things up and focusing on a specific part of the process using technology. Everything looks good for a while (because with the installation comes clean up – or at least a fresh start), but things soon go south, and the problems arise again in a repeating pattern
AI tools can help under certain scenarios. In the early days of AI vendor confusion and unrealistic promises, many claimed that all you needed to do was “point the AI to all of the data” and it will work its magic. Customers quickly learned that AI technology has to be “trained” on specific information sources, and frequently a foundational structure or data architecture is required. (Some may argue that machine learning can figure out all of your product names and attributes, but I have yet to find a customer who has experienced that level of hands-off functionality).
Certain algorithms can make sense of messy data, and cognitive assistants can be trained using large amounts of prior conversational data but for many applications, they are in fact trained on high-quality, curated, tagged and structured data, content, and knowledge assets. That is a knowledge and content management problem. Where does that high-value knowledge and expertise come from? Knowledge communities. The engineers that design and build solutions. The service techs who come across challenging field conditions and anomalies. Much of that expertise is referred to as tacit knowledge – knowledge that is in the heads of employees and specialists based on their years of experience. But experts need a mechanism for knowledge sharing, and knowledge management software can be a vehicle for enabling this kind of process and capability.
So-called “cognitive” artificial intelligence applications – the Intelligent virtual assistants and knowledge retrieval bots that many organizations are experimenting with – get their abilities not from magic AI pixie dust but rather from knowledge-engineering approaches to information and knowledge management. These approaches can solve real problems today while preparing for a future of high-performing virtual assistants.
Digital Transformation Program Scope – Knowledge is the First to Go
Unfortunately, organizations undergoing transformations are prioritizing user experience and usability over knowledge processes. These decisions are made when projects run over budget, timelines slip, and unexpected issues come up. But cutting knowledge management from the scope is a serious error that will ultimately lead to lost market share and higher costs as organizations scramble to catch up with managing enterprise content.
In some cases, the organizations will not be able to catch up, and will go the way of a Blockbuster or Kodak. Why? Because eventually almost all interactions will at least partially be enabled by virtual assistants and bots, and without good knowledge management, these assistants cannot function. In some situations, those tools will actually be the primary way that the enterprise interacts with customers. That is the inevitable course we are on. In five years, those organizations that are not making the investments in maturing their knowledge initiatives will wake up to competitors who have been developing capabilities over the past decade and find themselves woefully and in some cases – unrecoverably – behind.
Many organizations consider content strategy and SEO to be primary areas of effort during their digital transformations or e-commerce redeployments. That is fine for attracting customers to an organization’s offerings but is short-sighted regarding larger knowledge issues. The digital transformation cannot be compartmentalized into “we’re just doing enough for SEO and will come back to this later.“ That approach will not work. Knowledge and content must be aligned with customer journeys using high fidelity journey maps that can interpret the digital body language of the customer and respond to those signals through the digital machinery designed by – perhaps you have guessed – knowledge communities.
In one organization undergoing a digital transformation, the marketing team owned content for the e-commerce site; however, customer support content and knowledge management were not part of the plan. The marketing team wanted to focus only on SEO. Two years into the project, people started asking about their knowledge strategy for the detailed engineering information that customers relied upon. The project was already behind schedule and over budget, and design decisions made earlier in the program limited the available options. The result was a lower customer satisfaction rating and higher volumes of calls to the call center. The project was supposed to reduce those calls by improving the user experience. For this organization, part of the user experience was access to knowledge and expertise, which became harder to locate on the new site because core knowledge principles were not observed and only focusing on SEO did not fully address customer needs.
AI and Knowledge Flow
AI and machine learning technologies can support knowledge flow in several ways.
Organizational network analysis (ONA) – ONA identifies connectors, informal networks, influencers, and hidden structures that are critical to understanding knowledge communities and networks. Machine learning can process many data sources and prescribe actions to help improve collaboration, reach, and effectiveness of communities.
Sentiment analysis – identifies the tone of communications among and between individuals and communities. Is there healthy task-focused debate? Or have politics and personal gripes disrupted flow and effectiveness?
Expertise identification – self declared expertise is notoriously inaccurate. More accurate expert profiles can be derived by processing multiple sources of content such as written project summaries, discussion posts, and contribution to intellectual property (white papers, methodologies, analysis, and other documents).
Improved search and retrieval – Semantic search allows for language and phrase variation, returning results that include “proposals” using the term “SOW” in a search when no document contains that term) as well as results that are personalized based on role, preferences or other signals. “Helper bots” use advanced and/or federated search under the covers – the bot knows where to look for specific information reducing the friction/overhead in collating information from multiple sources.
Knowledge recommendation – recommendation engines can surface high-value knowledge based on team goals and project profiles. For example, providing documents used in similar projects or containing part of the solution to a problem the team is trying to address.
Organizations Compete on Knowledge
Every differentiator comes down to knowledge – the institutional knowledge of how the business operates at every level, technical knowledge, and IP, knowledge embedded in software and designs, knowledge of customer needs, of routes to market, of channel partners, knowledge of how messaging will break through in a crowded market, and better ways to design the customer experience or product features. As technology continues to speed up all processes, the knowledge lifecycle will become the critical differentiator because the race is on to build cognitive assistants that will speed internal processes and support the customer at lower costs.
Our digital revolution is changing managerial paradigms and refocusing on the core value created by human resources, while delegating lower level tasks to chatbots and other digital assistants. Artificial intelligence should be considered augmented intelligence, helping humans perform their jobs and enabling them to focus on more meaningful value creation, which is a uniquely human ability. The future is one of supersmart devices and distributed intelligence in everyday technologies.
This distributed intelligence is not just part of the technology embedded in the device, but in the ability to guide humans in addressing problems that will all but eliminate the need to call a support rep. The knowledge of the support rep will be built into the device. If support is needed, the devices will diagnose themselves, open a support ticket and order the parts they need to repair themselves. When the human maintenance tech arrives, the devices will tell them how to perform the repair. The companies that master these technologies first are the ones that are making investments in knowledge management and effecting organizational change today – not waiting until a competitor demonstrates market changing capabilities. By then it will be too late. Many executives have been burned by knowledge programs and are therefore cautious. They need to understand that this will not be a nice-to-have but a need-to-have. Knowledge management will be a critical process needed to survive in an AI-powered cognitive future.
A version of this article was originally published on CustomerThink.