AiThority Interview with Simon Hill, CEO at Wazoku

How have you interacted with AI and other Intelligent technologies that you work within your daily life?

AI is behind the scenes in so many elements of daily life now, it might be easier to pick out the elements that do not use AI. Whether it’s predictive searches on Google, shopping online – not just the obvious example of Amazon but lots more specialist shops and services – or matching candidates to jobs on LinkedIn, AI is everywhere. Even the much-derided chatbots are useful – a good chatbot is an efficient and effective way of interacting with a brand.

How did you start in this space?

I started my career as a consultant, and it was my experiences inside large companies such as Deloitte and PwC, and then seeing the agility and dynamism I found in a B2B start-up such as Huddle, that led me to start Wazoku. Every company in the world wants good ideas to improve efficiencies and delight their customers and I saw a huge opportunity to deliver something that would do so. There wasn’t really anything in the market at the time that could do that, certainly in Europe, so we launched in 2011 and haven’t looked back. We have offices in London, Bristol, and Copenhagen and work with firms all over the world. AI is increasingly part of our offering and our Spotlight.ai platform has been active for a few years now, using Machine Learning capabilities to build and support a corporate memory for ideas, leading to truly sustainable and on-going innovation.

How do you differentiate Wazoku from other Idea Management providers?

The Wazoku product is a highly scalable platform that supports all types of ideation at multiple levels of the organization and across all and any audience. As a business’s true, single, global home for ideas, it is vital that we develop the tools, functionality, and flexibility to support a highly diverse set of ideas. Ideas are highly complex and require the right mix of time, resources, insight, and expertise to give them a fighting chance of finding market fit. An idea is only likely to drive a change or an innovation outcome if it is aligned to a specific pain or organizational objective. Using smart algorithms underpinned by machine learning and NLP we are increasingly able to match ideas and people to organizational problems at the time they are relevant, giving every idea a greater chance of adding value and significantly improving the innovation and change outcomes for organizations. Wazoku is the only true global home for ideas.

How do you see the raging trend of including ‘AI in everything’ impacting businesses?

While Wazoku has made good use of AI in our own solution, and that there are areas of consumer life that have been improved immeasurably because of AI, I think AI for everything is problematic. The hype surrounding AI dates back to the 1960s, yet the examples of it delivering on that hype are few and far between. The current AI in everything approach can be overwhelming, and AI is far more effective and will be far more accepted when deployed for specific uses that people can truly relate to.

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What are the biggest challenges and opportunities for companies working to implement AI?

I would say that there is an element of expectation management here and a significant amount of education required also. For most organizations, the early adoption of AI technology will be very localized to a specific process or touchpoint. AI isn’t going to replace humans in every aspect of life, it’s about working out where AI can do things more effectively and efficiently than a human and how that enables people to better perform the true jobs to be done.

Additionally, the ability of AI to drive useful outcomes is often limited by the lack of clean and useful data and process to enable the AI. This will continue to inhibit scale for many organizations for many years to come.

How is Wazoku utilizing Machine Learning?

Part of our offering is Spotlight.AI, which looks to machines to better help match people and ideas to problems more seamlessly. Many organizations are good at building and developing things, but often without having a natural fit or use for that. So what happens to these good ideas that there isn’t a current need for? They mostly either drift into the ether or are stored away somewhere, undiscoverable and never used. But by running all your idea management programs on one tool or platform it allows the machine to keep building and learning and developing its own memory, which may become useful in the future.

Also, if you run an innovation challenge and get thousands of ideas submitted, the likelihood is that only a very small percentage of those are actually going to be taken forward. What happens to the other 99-odd percent of those? But often there’s still value still to be found from ideas that don’t find a home the first time around.

So we fold those ideas back into the corporate memory, as we know that at some stage in the future they could be useful. The machine learns about each idea and holds it there until that time in the future, which might be a week, a year or even a decade that someone is facing a challenge that idea addresses. Connecting people and teams with ideas, and using ideas for different purposes than which they were initially conceived. We are building the corporate memory to make it as easy as possible for the machine to quickly help match people and ideas to problems.

How should young technology professionals train themselves to work better with AI?

Great question – we need to get people beyond the hype and into a true application. Obviously, publications, such as this one, providing case studies and insight are invaluable. I would recommend that in general people look to get as hands-on with the technology and the application of the technology to learn where AI can truly add value and how.

How do you consume information on AI/ML and related topics to build your opinion?

I read a lot, both about idea management and how people are deploying AI, but also in the wider business and technology communities. This includes specialist titles such as AiThority.com, but also publications such as the Financial Times and Harvard Business Review that look at AI in the context of business improvement.

What makes understanding AI so hard when it comes to actually deploying it? How do you manage these challenges at Wazoku?

We have gradually incorporated AI into Idea Spotlight, our core solution, so haven’t had the need for a major educational push with customers, prospects or employees. It has felt like a natural evolution of the technology we use. I think some of the languages around AI can make it seem more daunting than it actually is. If vendors kept things simpler and more focused on how AI can make people’s roles easier, then understanding it would be much more straightforward. For AI to really take off in the business mainstream, then it needs to be in the hands of business users, not techies or data scientists. This is how we pitch it – a hugely useful technology that delivers better results than before.

How potent is Machine Intelligence for businesses and society? Who owns Machine Learning results?

The potential is huge for both business and society. We have a limited ability to process the significant amount of data we generate (as businesses or individuals). Machine intelligence, if applied well, should bring that processing power to our fingertips, allowing us to make better-informed decisions and focus on the right things. However, we have a very long way to go!

Where do you see AI/Machine Learning and other smart technologies heading beyond 2020?

I think we will see it becoming more a part of our everyday lives in both an office and home setting. Most of the technology we use, and it feels like everything is becoming digitized, will likely have Machine Learning algorithms operating across a range of different processes. In general, this should be pretty seamless for the end-user/consumer and should make life easier…but let’s see!

There are some interesting challenges ahead, for example we are looking at how we ensure that our ML doesn’t contain any inherent bias (gender, ethnicity, etc), it’s an interesting topic to consider, as these algorithms are programmed by people and so we need to ensure we do not build in any potential bias into the coding or outcomes from these programs. In general, there will be pros and cons to all of this new way of working, but I see more pros than cons in the current approaches.

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What is the Good, Bad and Ugly about AI that you have heard or predict?

The idea that machines will rise up and one day take over from us is a sci-fi trope rather than a genuine concern based on evidence or reality. But a lack of control over AI is something that we should certainly be mindful of. There is a lot of discussion about driverless cars right now, but how autonomous should they really be? That’s a question not just for car manufacturers but also for the wider society at large. Another concern is the loss of jobs. With machines able to do certain tasks far quicker and more effectively than a human ever could, it stands to reason that they would be used instead of human beings in these instances. But I prefer to think of it as roles changing rather than being replaced. There will always be a place for humans, working alongside AI rather than not at all, and provided the right training is given there is no reason to think there will be substantial job losses as a consequence of AI.

What is your opinion on the “Weaponization of AI”? How do you deal with the challenge here?

Any new technology will at some stage, almost certainly be deployed for both good and bad. But perhaps the potential for AI to be weaponized is greater than with other technologies. One can read many stories about the use of AI in cybercrime and also about instances of AI-based facial recognition – this is an area that almost instantly feels uncomfortable. It’s therefore important for Governments to stay on top of security around AI. As criminals get more sophisticated and professional in their use of AI, so must the defenses against them.

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What technologies within AI and Computing are you interested in?

I think that AI for AI’s sake is damaging. Rightly or wrongly there is a nervousness around certain elements of AI and what its impact might be, so I am always drawn to AI applications that really make a difference and genuinely improve what humans were capable of by themselves. In my industry, that means using AI for sustainable and scalable innovation but could really be anything that changes things for the better. It’s always about the application rather than the technology itself for me.

As a Tech Leader, what industries do you think would be fastest to adopting AI/ML with smooth efficiency? What are the new emerging markets for AI technology?

Most industries have been relatively slow to adopt AI so far, and outside of the early adopter community, there aren’t many that up and running with AI in any meaningful way. But I don’t think that certain industries are inherently more suited to AI than others. FS is known as a sector that is slow to utilize new technologies, but within the industry, there are pockets or bubbles in which people have made interesting advances with AI.

Tag the one person in the industry whose answers to these questions you would love to read:

Satya Nadella – Microsoft

Marc Benioff – Salesforce

Robert M. Lightfoot Jr. – NASA

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Thank you, Simon! That was fun and hope to see you back on AiThority soon.