The Future of Idea Management

By: Jesse Nieminen

As the Co-founder at Viima, an idea management software company, I frequently get asked about the future of idea management. With the amount of innovation that we’re seeing across industries, it’s hardly surprising.

So, to begin 2019, I thought I’d share our views on what the coming year and next decade might have in store for our industry to, hopefully, spark conversation—and action.

Wherever we look, there’s plenty of talk about the next recession and it would seem that most analysts and people agree on the upcoming decade likely being quite unpredictable and chaotic.

No matter how the future eventually turns out to be, companies would do wisely to use innovation to build or reinforce their competitive advantage. The strongest businesses, after all, often actually benefit from a recession as there’s less competition and everything needed to fuel growth tends to be much cheaper.

As such, while innovation is tremendously beneficial during a boom, it is arguably even more critical if there are tough times ahead.

Towards an Idea Meritocracy

As the role of both technology and customer centricity continue to become more important and the society grows ever more connected, the ecosystem most companies operate in has become highly complex.

It’s far more difficult for any single person to understand both the big picture and all the grass-roots level details than it has ever before, while the pace of change would seem still seem to be accelerating in many industries. For example, half the S&P 500 is forecast to be replaced within the next 10 years.

These changes have naturally made executives’ jobs much more difficult. This is probably why the 2016 Deloitte Global Board Survey found that innovation/R&D strategy was, along with talent management, their top weakness. Furthermore, only slightly more than half of the boards were satisfied with their ability to handle digital disruption, and to invest in innovation.

We simply have to accept that the boss no longer has all the answers and find ways to take this into account in our decision-making.

The solution is what the founder of Bridgewater Associates, the largest and most successful hedge fund in history, Ray Dalio calls an “idea meritocracy” in his book Principles: Life and Work.

The point of an idea meritocracy is simple: the best idea should always win, regardless of the tenure or job title of the person who came up with it.

This is, of course, much harder in practice than on paper. How do you really know how good an idea is, or whose judgment to trust in evaluating the idea?

Dalio suggests weighting the evaluations by “believability”, which he determines based on the person being repeatedly demonstrating their capability in the area in practice, as well as by being able to explain the cause-effect relationship behind their conclusions.

The change, however, won’t be easy. Even at Bridgewater where this practice is deeply ingrained in the culture, it typically takes 18 months for new recruits to get used to working this way.

Thus, while most organizations are unlikely to make the transition to a pure idea meritocracy anytime soon, what’s clear is that organizations have to move decision-making down in their organization to be able to make better decisions faster.

The same trend holds true in idea management. The leading organizations, including some of our most successful customers, have already, or are in the process of moving away from a fully centralized model to a more empowering decentralized approach to ideation.

What’s more, empowerment shouldn’t stop at having a say in the decision. It should include the execution of the ideas.

For example, Facebook has built a stable infrastructure and ways of working that allow anyone to rapidly implement, test, and measure the impact of their ideas.

This means that they don’t just have to guess if the ideas are good, they can see if that is really the case, effectively reducing uncertainty. What’s more, since there’s no need to wait for approval, prioritization or resources, even though Facebook is a large organization, it is able to move at an incredible pace.

Building an Ambidextrous and Agile Organization

Building on my last point, the most successful and innovative organizations are typically ambidextrous and agile.

An ambidextrous organization is one where existing and emerging businesses are organized as separate and independent units, but within the same management structure.

According to a classic HBR article, more than 90% of ambidextrous organizations were able to achieve their innovation goals, whereas, at best, a quarter of the other kinds of organizations were able to do the same.

An agile organization, as defined by McKinsey & Company, is an organization that is able to simultaneously be stable and operate with speed. In practice, this typically means that the organization has certain core capabilities that remain quite stable coupled with dynamic capabilities that are able to adapt and respond quickly to emerging challenges or opportunities.

According to their research, agile organizations are more than three times as likely as other types of organizations to be in the top quartile for long-term performance.

Two different sides of the same coin, these two approaches to building an organization are catching on. According to the aforementioned McKinsey study, three-quarters of business leaders have agile transformation as one of their top three priorities.

For idea management, these approaches have a few practical implications:

• Incremental improvement needs to happen on every level across the organization
• Disruptive ideas should be collected and analyzed to identify future threats and opportunities
• The organization should have a reserve of independent “strike teams” to rapidly test, implement and create business from these disruptive ideas

Based on our discussions and experiences, most organizations still seem to have focus heavily on doing just incremental improvement, or just pursuing those big ideas. Even if they do both, they typically manage the processes in virtually the same way.

In reality, that rarely works. You need both approaches to succeed in both the short and the long term, and each of them needs to be managed quite differently.

In our experience, by far the single most common bottleneck in idea management processes is the organization’s ability to implement the ideas.

Idea management programs, unfortunately, all too often lead to just a set of promising ideas and a statement like “nobody has the time to progress the ideas”, or “projects for this year have already been planned and scheduled”.

Most agile organizations have addressed this problem by having the ability to assign small, often temporary, teams to test and pursue the most promising ideas. This is typically a much faster, more flexible and cost-effective solution than the traditional alternatives of building a new business unit right away, or hiring such a team from a consultancy.

Thus, an organizational structure and management processes that support agile testing, prototyping and implementation of ideas are key factors in helping an organization achieve sustainable long term success – even during recessions.

Continuity Breeds Sustainable Results

Idea challenges and other limited-time campaigns are a great way to get started with idea management. They allow the organization to start with a theme that is highly relevant and important for them, and to get results quickly.

As a result, a large portion of the idea management software market is also focused primarily on running these kinds of challenges.

Generally idea challenges are ran under the presumption that the organization has a limited budget and limited resources for implementing ideas, a typical scenario for organizations that subject idea management to an annual planning cycle. Thus, they want to find the very best ideas to spend these limited resources on, which certainly makes sense.

In our experience, this approach, however, leads to a number of practical challenges that often limit the organization’s ability to drive sustained results.

• It’s very hard to know which ideas are the best in advance and by only implementing a select few ideas, the risk of choosing the wrong ideas gets bigger, both in terms of probability and cost
• Focus moves to minimizing risk and using resources efficiently instead of moving fast and creating value
• Running too many campaigns can lead to “campaign fatigue”, which lowers participation rates and leads to diminishing returns over time, especially if nothing is done for the vast majority of the ideas

So, while there definitely is a time and place for idea challenges in idea management work (as a matter of fact, we typically recommend most of our customers to start with one), they shouldn’t have as central of a role in the field as they currently do.

In every field, including idea management, persistent and focused effort always leads to superior results over time. We’ve witnessed this over and over again among our customers as well.

Thus, most organizations would do wisely to make their idea management work more focused on the things that are strategically most important for them, to build management systems that allow them to move quickly and maximize value creation, and then to continuously and systematically drive results out of these efforts.

The Rise of Automation and Artificial Intelligence

In the last decade, idea management software has helped organizations to collect and manage ideas much more easily, and to effortlessly involve tons of people in the work.

However, the field is still quite young and there’s plenty that we can do to help make the work more efficient and effective. That’s where automation, artificial intelligence (AI) and other related technologies come in.

The potential benefits of these technologies can generally be categorized into two different areas:

• Collecting and processing data
• Analyzing data

So far, idea management tools rely quite heavily on users inputting their ideas in a predetermined text format. Next, said ideas are then often evaluated and the content of the idea analyzed. Then they might be labeled and assigned to relevant people for future processing. Most of these steps still involve quite a bit of manual work, even with the best of technology available today.

However, with the maturation of certain technologies, such as speech recognition and natural language processing, we are headed for an easier and more effective future.

For example, you might log ideas with your voice, or simply record meetings and let AI analyze the data, then log and label the ideas that were discussed during the meeting and assign them for the right people to progress.

Also, when people submit ideas, they can forget to communicate certain pieces of relevant information, or they might not have thought the idea through in enough detail. An AI could analyze the structure and reasoning of the ideas and proactively suggest the ideator to address these shortcomings.

It could also enrich the ideas with data from reliable internal and external sources, making further analysis much easier and more effective.

The other, arguably much more difficult, side of the picture is the ability to analyze and make sense of all the data related to the ideas, and even more importantly, the process for managing them.

Idea management and innovation are very complex fields where there’s plenty of data available, but most of it isn’t all that useful without further critical analysis.

The challenge with innovation is that, by definition, it is about coming up with new things.

Thus, while many innovators and experts, like Steve Jobs, consider innovation and creativity to simply be about combining different insights and ideas together into a new context, there still is that element of thinking of something in a completely new way, which has proven to be very difficult for not only computers, but also for most people. Paradoxically, successful innovators are both creative and evidence-based at the time.

Most people seem to understand that computers are able to analyze more data and to look at it more objectively than we humans are. However, what most people don’t understand is that computers can be surprisingly good at creating art, too.

For example, most experts can’t distinguish between Haiku poems and classical music created by renowned artists, and those created by a well-designed computer program.

As such, I’m, confident that we’ll find more and more novel, yet practical, ways to help innovators better make sense of all the data and to use that data to come up with better ideas, to improve their idea management process, and to drive their business forward.

The next decade will be a very interesting one for our field and I certainly can’t wait to see these ideas become a reality.

Do you agree or disagree? We’re working hard to shape that future and I’d love to hear your thoughts and arguments on the topic to help make them a reality, so please be in touch!

By Jesse Nieminen