A Digital Market of Support

An Archestra Notebook

The Unconventional Wisdom of Popular Belief

Support comes into the picture whenever someone’s mental sense of making progress is frustrated.

From a support requester’s point of view, successful support always has the same characteristic: instant unscheduled access to the right performance of the right idea.

That combination of convenience and confidence will be the goal of using any methods provided to help.

The right idea can be a confirmation, explanation, instruction or other means of making an immediate desired difference in the user’s readiness to proceed. Performing the idea is, of course, the actual mode of support.

Consequently, any interruption in the progress towards the readiness will be experienced as a delay and, psychologically, as a deficiency in the quality of support. In expecting frustration, users are more tempted to try less, and to stay away.

Now, we all expect digitization to break through the problem.

From the support user’s perspective, digitized functions underlying performance in a support effort have to deal with four key factors, as seen above.

Superficially, the functions need to provide the user with speed, certainty, and understanding in addressing each factor. Digitization, in turn, needs to provide each function with speed, certainty and relevance.

Conventionally, this is approached by looking at a process dedicated to each function, and “digitizing” each process: that is, removing any dependencies on acquiring and converting physical resources that could supply the process with whatever operational inputs it needs for mechanical purposes.

That approach offers the idea that optimizing processes for material efficiency, then chaining the optimized processes, will maximize the ability to execute the support event.

Waiting Translating / explaining Deciding Operating

Digitization readily occurs independently in each of the areas and can generally “forward” the user from one point to another without requiring the next step to feed back to the prior one.

Over time, the improvements of the respective digitizations get introduced into the overall “flow”… and the net results are mainly positive. But there have been significant leftover effects as well.

Waiting Translating / explaining Deciding Operating

Fastest path to attention

Accuracy of interpretation

Best correlation to successes

Least complicated effectiveness

Monitoring Semantics Analytics Procedures

Steps taken to automate support are usually very welcome, but they can come with drawbacks. Theyreduce the time needed to spend on different aspects of solving a user’s support issue, but users maystill experience discouraging delays in ways that they had not anticipated.

TIME SPENT:

©2015 Malcolm Ryder / Archestra Research

< Lack of knowledge, plus trial-&-error

< Identifying correct criteria and context

The Support User’s perceived burden of accumulated “time spent” is different depending on what approach isavailable and used. The felt burden can occur due to queueing (with agents); confusion (in self-help); poorrelevance (in knowledge automation); and potentially, unfamiliarity (with automatic solutions)… As moretechnology changes the support possibilities, it increases expectations as well. Users then feel whatever thetoughest new bottleneck is, until finally enough automation removes them all...

< Unpredictable priority

< Adjusting to changes made

©2015 Malcolm Ryder / Archestra Research

So, why is that “evolution” even still interesting, anyway? The answer is that people do not insist on aiming for only the most advanced future solution in that evolution. Instead, they still use everything they know about – bottlenecks and all. And also, they are now taking all of it, not just some of it, in a different direction.

The steady march of digitized automation has led to two interesting new basic conditions.

One condition, “social collaboration”, is heavily discussed but often in a way that disguises its kernel of real importance. Here the big development is that the methodological paradigm of process automation is increasingly having to share space with the paradigm called communication. The difference between the qualification of information (by a process) and the interpretation of information (by a communication) is often ignored, even though people “naturally” respond more immediately to interpretations than they do to qualifications.

The other condition is generally referred to as “artificial intelligence”, and the artifice merits a very close look. As part of this condition, the difference between artificial and virtual is often missed, or misconstrued. What aspect of “artificial” is the reason why it is such a popular and engaging idea? Mainly, artificial pertains to some agent of information awareness and meaning, that serves as a proxy for the user’s own inherent actual capability. The aspect of intelligencerefers mainly to calculations and decisions based on the awareness.

As digitization continues to aggressively expand the real-time breadth of exposure to actionable information, social collaboration increases; consequently, consensus interpretation becomes more important and also becomes more likely to generate ad hoc and improvised activityinstead of just compliance to process. The challenge for management is to model the interpretation-by-consensus as a progressive function.

That challenge is essentially coordination – a cooperation of orchestration (by a manager) and choreography (by a contributor community) – used for achieving production control. Arguably, the most important role that digitization can play is to facilitate that coordination.

That is not contradictory to the influence of artificial intelligence (AI). Most of the time, the notion of AI is invoked to represent an ability to correctly predict-plus-act, particularly where previously unexpected issues arise. This is a good supervisory influence. And, it is entirely expected that AI will be used to improve process automation, as well as to help decide what processes should be activated under demand.

But at the same time AI represents a virtual actor that is specifically expected to do unpredicted things. And, the challenge for management is to model that unpredictability as a progressive function.

Being unpredictably correct is exactly the shared virtue of AI and Social Collaboration – which explains why both are promoted so heavily now. It is the new deliverable that exceeds conventional process automation.

Neither Social Collaboration nor Practical AI is possible these days without digitization – if for no other reason than that non-digitized versions are intolerable for most existing resource managers, and those managers are highly unlikely to be removed for ones that will “look backwards” on the company’s dime.

But more importantly, digitization allows their strategic integration.

Cooperative control of the production of support joins orchestration and choreography. It will incorporate automation improvements by having them help to integrate communications, policies and feedback as a single working environment. For the managing support provider, automation is necessary but insufficient; orchestration is required. For the social support contributor, the value of participating derives from choreography.

Digital Intelligence is what we have already become familiar with.

That is, “intelligence” has most often surfaced as the digital packaging of observations, rules and instructions that cause decisions and actions. Static packages could be published, and dynamic packages could be executed (mechanically active).

However, due to the human factor of setting the agendas of both discovery and disclosure, and of initiating the tasking involved, the intelligence was not referred to as “artificial”. The human factor, which was essentially the steering control, is what has been thought of as the “thinking”.

Having computers take the steering wheel and automatically decide where to drive is the predominant image today of what “artificial” means. Automated thinking, instead of just automated doing, is potentially going to produce new ideas, but any human party other than ourselves could also have ideas that we didn’t, given time. Digitization simply makes this increasingly likely to occur “artificially” and be noticed. Meanwhile, automated thinking can regularly steer though vast scales and scopes of complexity.

Marketing the automation is an obvious thing to do.

Overall: as we really experience it, artificial intelligence (AI) offers an opportunity to choreograph supportive communities by facilitating their collaboration (both proactively and on demand) and by orchestrating social communication with feedback from monitored policies.

The arrangement can be a marketed Quality Assurance responsibility of support organizations, offering steady guidance that simplifies the experience of fast-changing technologies and knowledge.

However, in this manner, the support “organization” actually becomes embedded throughout the company as a managed relationship between support brokers who understand, package, and communicate requests -- and support agents who understand, package and communicate offers.

With digitization, the objective now is to produce those brokers and agents, with full awareness of each other being achievable specifically for cooperation on demand.

©2015 Malcolm Ryder / Archestra Research

mryder@malcolmryder.com