Is my prescription ready?
Has my insurance claim been approved?
Has my gate changed?
These are questions industry analysts at Forrester use to illustrate what they call the “mobile mind shift,” which they define as an expectation that “I can get what I want, in my immediate context and moments of need” on my smartphone.
The reflexive way people reach for their smartphones nearly anywhere, anytime, may even call to mind Pavlov’s dogs.
Ivan Pavlov won a Nobel Prize for describing how animals (or humans) can be trained to respond in a certain way to a particular stimulus. In the dogs’ case, he observed dogs in the lab drooling when they saw someone in a white coat — because, as he figured out, they were always fed by someone wearing one. He famously went on to train the dogs to salivate when he rang a bell.
As executives, strategists or implementers within an enterprise, we may be tempted to imagine that we are in the role of scientists like him, conditioning consumers to instinctively turn to our products and services during their mobile moments. But it is more likely we are the ones who need new to learn new reflexes and build new muscle memories for how we handle investing in technology.
In short, when it comes to digital transformation, we’re not Pavlov. We’re the dogs.
Our instinct to “plan, discuss and control” needs to be replaced with “doing, testing and discovering.” This may feel uncomfortable because our organizations have trained us to associate safety with decision funnels, net present value (NPV) spreadsheets and final-approved plans in PowerPoint (complete with voluminous appendices of supporting data).
What I would describe as the “traditional model” for investing in technology reflects the limitations of its time:
We must winnow down our options into a static plan for releasing large amounts of code, months apart, by internally debating the meaning of extrinsically collected data.
But mobile, cloud and machine learning have changed the balance of risk and reward.
Amazon’s CEO pioneered a no-holds barred embrace of these implications with an “APIs everywhere” mandate in 2002. (This presentation by an AWS devops manager on how they operate today is a must-read.) Today, the technological tools needed to take up continuous integration and relentless, hypothesis-driven (and often automatable) testing — not to mention the ability to immediately roll back deployments when necessary — are now mainstream and widely available to any company. (Disclosure: my employer, Apigee, provides a platform for using APIs at scale and as strategic assets.)
In this context, the old way of doing things must give way to a new model that takes full advantage of this:
We can release small amounts of code, weeks apart, with ways to test our hypotheses for “what’s right” and “what’s next” defined in advance — and often built-in.
However, individual behavior and organizational incentives must adapt in order to make the most of it:
If a desired behavior is fast delivery of a minimum viable product (MVP) to catalyze mobile interactions, the organization must switch from reflexively treating “deviating from the plan” as failure to celebrating “fast failing or scaling” as success.
If the desired behavior is “learning by doing,” then the organization must embrace the fact that harvesting data showing that “we were wrong and need to course correct” isn’t a proof of having made more mistakes: it’s a sign of missing fewer.
If the desired behavior is minimizing risk, then the organization must embrace small changes that can be immediately rolled back as a stronger hand to play than approvals heaped upon gates piled upon process for large changes that can’t.
These are a matter of both perspective and practice. To assess this idea, we ran a survey of IT decision makers across 800 large companies “the ability to meet or beat expectations” with regards to app development cost, time to deliver, quality, quantity, and business impact of apps as a benchmark. More than a third (36 percent) failed to exceed expectations on any of the five measures; 8 percent exceeded expectations on all five.
How did they do it? For one, these “app masters” were far more likely than the rest to describe their IT department’s approach as strongly “outside in” and “cloud first.” App masters also described their approach as “agile” and “embracing experimentation.”
In keeping with these attitudes, they were making greater use of cloud-based resources such as platform-as-a-service (PaaS) and infrastructure-as-a-service (IaaS) than weaker performers. And companies reporting the strongest capabilities using APIs and analytics were more than three times as likely to be app masters.
While consumers have made the “mobile mind shift,” enterprises still have some learning to do. Among the hundreds of large enterprises we surveyed, only 1-in-12 are exceeding their goals for apps that appear to deliver on “what I want, in my immediate context and moments of need.”
To join them, the stimulus-response defaults for how your organization conceives, approves, funds and executes on digital projects and programs needs to change. As executives, strategists and implementers that ought to be an opportunity we’re all salivating over.
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