In the realm of professional service, the gatekeeper's days are numbered.
Until recently, professional service consisted of human providers having real-time, face to face interactions with customers that were rewarded according to the amount of time spent. In this traditional model, advice or products are highly customized, focusing on the needs of individuals.
But in the near future, disruptive new models for the production and distribution of expertise are emerging thanks to advances in digital technology. According to the book The Future of the Professions by Richard and Daniel Susskind, these models include the networked experts model, the paraprofessional model, the knowledge engineering model, the communities of experience model, the embedded knowledge model, and the machine-generated model. Let’s examine each in a bit of detail.
Like the traditional model, this model involves human service providers, but unlike the traditional model, where experts work alone or in relatively stable organizations and groups, when experts are networked they convene as virtual teams. Groups of specialists, often online freelancers, use online platforms to interact and communicate with each other, forming transitory affiliations to solve specific problems. Professionals might not know one another, and service is more likely to be ad hoc than in the traditional model. Online project management systems are an example.
In this model, service is provided via consultation from one human being to another. However, the provider is a person with more rudimentary training in a discipline. The paraprofessional cannot provide the full professional service unaided, but rather is equipped with procedures, systems, and support tools that have been previously created by experts. Ownership of the intellectual property that results from collaboration between experts who provide the guidance and paraprofessionals who deliver it is shared by both. A junior teacher who supports her curriculum with world-class online lectures is an example of this model in action.
Here, practical expertise is represented in a system made available to users as an online service. The production process involves identifying the formal, well-documented knowledge of a given area of expertise, “mining the jewels” from expert heads, and structuring informal expert input into systems that can generally be applied to a wide range of problems. Users can tap these solutions without consulting directly with human beings. Online contract drafting tools are examples of the knowledge engineering model.
In this model, evolving bodies of practical expertise are collaboratively sourced, built up through the contributions of past recipients of professional service or non-experts who sorted out problems for themselves. People in these communities are prepared to share the techniques, methods, insights, and knowledge that worked for them, and the bodies of experience are edited, supplemented, and kept current by a group of committed participants in the spirit of Linux and Wikipedia. Alternatively, when a specific problem or challenge arises, members of communities, often in large numbers, may be called upon to participate in issue resolution. This is sometimes referred to as crowdsourcing, and OpenIDEO is an example.
Here, practical expertise is distilled into a form that can be built into machines, systems, processes, working practices, physical objects, and even human beings. Knowledge is applied automatically, as in the case of the intelligent building equipped with sensors to regulate temperature according to environmental law with no input from human compliance specialists (although knowledge may well originate from them). The same body of knowledge may be reused on many occasions, though it can also be fine-tuned to a unique situation.
Practical expertise in this model is generated by machines instead of human beings. This is achieved via Big Data, artificial intelligence, intelligent search, and innovations that haven’t been conceived yet! Such systems may operate on a one-to-one or one-to-many basis, and will most likely do so online. Human beings will be needed to design the systems through which machines can create and distribute knowledge, and ownership of content will be a concern. Currently, bots that write and publish news stories are an example of this model.
Have you seen any of these models in play already? Are they rough around the edges, or do they work fairly seamlessly?