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Customer cases

humap-consultation-1

Powering organizational transformation with artificial intelligence

Humap Consultation

Making sense of huge volumes of qualitative data is one of the biggest challenges consultants and facilitators face today. In his work as an Organizational Consultant at Humap Consultation, Olli-Pekka Juoperi has seen this first hand.

And that’s why to Juoperi and his colleagues, the new artificial intelligence capabilities of Howspace present a clever new way to objectively categorize, analyze, and summarize unstructured data.

Artificial intelligence: a paradigm shift or a passing fad

At a time when everyone’s talking about artificial intelligence, it can be difficult to separate its functional applications from the general hype. Besides, as Juoperi notes, most of the public discourse around AI still revolves on number crunching, which isn’t as useful when you’re working on something as complex as organizational change and transformation.

“Until now, it feels like we’ve been forced to quantify open dialogue, because it has been the only way to make sense of large masses of unstructured data. However, now that AI allows us to get exact, objective, and concise information out of huge quantities of qualitative data, I don’t see any reason why we should keep forcing numbers where they don’t belong,” Juoperi concludes.

He also suggests that while AI has been dubbed as a huge time-saver (which it undeniably is), the biggest benefit for him personally lies in its objectivity.

“Too often we let our own preconceived notions guide data analysis. We might focus on things or people we think are important, rather than patterns that occur more frequently in the data. And that’s where AI comes in handy: it doesn’t have the same bias that people do.”

The third big benefit Juoperi sees in the use of AI is that it finally cashes in on many modern organization’s promises of transparency and co-creation.

“Until now, the biggest problem with engaging the whole organization has been time. No matter how much we’ve wanted to hear from every single employee, we simply haven’t had the resources to take the whole organization’s insights into account. This is especially problematic in knowledge intensive fields, where the best ideas usually come from the experts.”

Juoperi also notes that bulk surveys and other superficial attempts at co-creation can actually be harmful for the morale and culture of the organization.

“Without the ability to show that we’ve used the employees inputs to build the final outcome, why would we even ask them to participate? The participants need to know that the time and effort they’ve contributed feeds into the end result.”

In Juoperi’s world, it’s safe to conclude that AI is not just some passing fad but a serious improvement on how to facilitate complex organizational change processes. 

And to further illustrate the practical use cases of Howspace’s AI capabilities, Juoperi shares two recent examples from his own work.

Case 1: Finding a shared language to communicate a new strategy

For a few years now, Juoperi has been working on a strategy process for an organization that employs 800+ people. And although Howspace has been used for co-creating the strategy and facilitating the discussion over the course of the change process, it wasn’t until recently that Juoperi decided to put the platform’s AI capabilities to test.

“We had gotten to the point in the strategy process, where we had a good idea of the three fundamental concepts that would become the foundation of the new strategy. However, we quickly realized that each concept had several different meanings in the minds of the 800 employees. And that’s when we knew that we had to do something.”

Next up, Juoperi and his colleagues asked the group to work in pairs to define each of the three concepts. The pairs then posted their answers to a shared workspace. 

To Juoperi’s surprise, this resulted in just under 400 answers from the pairs, which meant that nearly the whole organization had participated in the definition work.

“We ended up with 16 pages of text on a minuscule font size. Analyzing that manually would’ve taken me at least one full working day. With Howspace, I got a 5-sentence summary in seconds — and all I had to do was to click a button. These 5 sentences helped us arrive at definitions that matched the majority’s thinking.”

After that, the CEO of the organization recorded a video introducing the brand new definitions of the three foundational concepts to the rest of the company. Juoperi and his colleagues then created a poll, where the participants could cast their votes on how well the AI-assisted definition process had worked on a scale from 1 to 5.

“Over 50% of the participants gave it a 4, while 25% percent thought that the initiative was worthy of full 5 points. Not one person gave it a 1. What’s more, we ended up with objective definitions that represented the large majority’s responses. All responses were treated equally in the process and no one had to fight for their voice to be heard.”

Case 2: Grouping new ideas into meaningful categories

The second recent use case of AI that Juoperi offers comes from Humap Consultation’s own research towards recognizing the different elements of work life transformation. As the first step, Juoperi and his colleagues asked people what they considered as important changes in work life.

“This time, instead of trying to squeeze one definition out of huge amounts of data, we focused on grouping data into meaningful categories. We ended up with 7 baskets that covered most of the answers. Based on that insight, we’re already planning on a webinar series with —  you guessed it — 7 episodes, each representing one of the broader themes we were able to identify from the research.”

When it comes to future use cases of AI, Juoperi seems optimistic.

“I’m keeping a close eye on how AI will help us make sense of dialogue in large organizations. I personally believe that anything that involves brainstorming or problem solving can be aided with artificial intelligence,” Juoperi concludes.

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