Five things we learned from our survey on government innovations and the hype cycle
The Gartner hype cycle tracks how technologies develop from initial conception to productive use. There is much excitement around different methodologies and technologies in the “government innovation” space, but which of these is hyped and which of these is truly productive?
Last year we made some educated guesses and placed ten government innovations along the hype cycle. This year, however, we went for something bigger and better. We created an entirely non-scientific poll and asked respondents to tell us where they thought these same ten government innovations sat on the hype cycle.
The innovations we included were artificial intelligence, blockchain, design thinking, policy labs, behavioural insights, open data, e-government, agile, lean and New Public Management.
Here is what we learned.
For the most part, we're still in the early days
On average, our respondents don't think that any of the methods have made it into truly productive use. In fact, for seven out of the ten innovations, the majority of respondents believed that these were indeed still in the “technology trigger” phase.
Assuming that these innovations will steadily make their way along the hype cycle, we should expect a lot more hype (as they enter the “peak of inflated expectations”) and a lot more disappointment (as they descend into the “trough of disillusionment)” going forward. Government innovation advocates should take heed.
Policy Labs are believed to be in “peak of inflated expectations”
This innovation attracted the highest level of disagreement from respondents. While almost two out of five people believe that policy labs are in the “technology trigger” phase, one out of five see them as having already reached the “slope of enlightenment”. On average, however, respondents believe policy labs to be in the “peak of inflated expectations”.
Both this disagreement over where they stand and the general current level of disappointment with policy labs may point to two things: the highly ambiguous meaning of the concept, and the fact that while many countries have “policy labs”, they often are similar in name only and do wildly different things. Proponents of policy labs point to longstanding success stories like Mindlab in Denmark, Policy Lab in the UK, Innovation Lab in the US and city labs such as the Barcelona Urban Lab.
Blockchain is seen as the most nascent government innovation
Our survey respondents rather unanimously believe that blockchain is at the very early stage of the “technology trigger” phase. Given that blockchain is often characterized as a solution in search of a problem, this view may not be surprising. The survey results also indicates that blockchain will have a long way to go before it will be used productively in government, but there are several ways this can be done.
Artificial intelligence inspires a lot of confidence (in some)
On average, respondents believe that artificial intelligence (AI) is in the “technology trigger” phase. Interestingly, this technology also attracted a fair level of disagreement between respondents. While just under two-thirds of respondents thought it was in the “technology trigger” phase, it is also the only other innovation (along with policy labs) where some respondents believe that it has made it beyond the “trough of disillusionment” and into the “plateau of productivity”.
This may either reflect the hype surrounding AI or, more optimistically, point to the fact that the future is already here - it's just unevenly distributed. What seems certain though is that AI will, over time, reshape how governments look, think and act.
New Public Management is - still - overhyped?
Some respondents believed that New Public Management (NPM) had moved beyond the “trough of disillusionment” and into the “slope of enlightenment”. On average, our respondents rather surprisingly rated NPM as being in the “peak of inflated expectations”. It's unclear whose expectations of NPM have not been deflated at this point.
This result may be explained by the absence of a “trash heap of failed innovations” option in the survey. Respondents who believed that NPM never made it beyond the “peak of inflated expectations” may not have had any way to indicate that.
What we've missed
We asked respondents to assess these technologies and methods in the context of the government they were most familiar with. Not every method will be at the same point on the curve in every polity. This is likely one of two reasons behind the variation in opinion that we see (the other one being genuine disagreements over where a method stands).
We received a number of suggestions for methodological refinements, such as measuring how the assessments might differ between countries or levels of government.
Respondents also suggested other government innovations which were not part of the survey. These include evidence-informed policymaking, delivery units, virtual reality and - in a developing country context - the use of mobile phones to monitor government operations.
What we believe this all means
The “hype cycle” model, this particular survey and the insights are certainly entertaining, but it would, of course, be a mistake to take the results too literally.
There is, however, an important point here. “Innovation” on its own is devoid of meaning. This is true more generally, but particularly so in the government space. Ultimately we should care about government effectiveness. Innovations are only interesting in so far as they allow us to improve people's lives.
The “hype cycle” model can help us manage the progress of a method, hopefully speeding up the time it takes to reach productive use. The Public Impact Fundamentals, our framework for understanding government effectiveness, are a useful lens to look at government innovations and understand how exactly they are contributing to governments achieving impact.
The Centre for Public Impact is investigating the way in which artificial intelligence (AI) can improve outcomes for citizens.
Are you working in government and interested in how AI applies to your practice? Or are you are an AI practitioner who thinks your tools can have an application in government? If so, please get in touch.