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It seems we are now, more than ever, focused on our habits and routines to be the best and most productive at what we do. Morning routines, sleep and exercise habits, and healthy habits are scattered across the web, offering a plethora of guidance and fruitful promises. But what about organisations who are thriving? Even though there isn’t a one-size-fits-all approach because every organisational culture is different, there’s a lot to gain from following the habits of successful companies—especially those who have thrown themselves into this new, advancing technological space.
Alan Turing asked the question “Can machines think?” which has since powered the revolution that is Industry 4.0 and artificial intelligence. Artificial intelligence encompasses many parts of computer science, intending to build smart machines that can perform tasks that usually require human intelligence. This can be incredibly beneficial for companies, where human error occurs frequently. There are many approaches to this branch of technology, and it’s impacting every vertical which uses the technology of some form.
In our previous blog post on AI, we delved into the future of AI, and it’s various use cases. IoT is significantly impacted by AI, because while IoT provides the data from the machines and connected devices, AI has the capabilities to unlock this data and make it into something meaningful. Because of AI’s analysis and algorithms, the organisations can then make decisive decisions and garner insights they may not have been able to develop before.
Over 90% of companies that have adopted IoT also use AI in some form, meaning more and more are developing analysis techniques to make predictions. How do these companies succeed? In today’s blog post, we will be going through the changes companies need to make to develop a successful AI and IoT strategy.
When you decide that your company is going to adopt AI, it is not only a big deal in terms of the economic investment and details, but as an organisational one too. For AI to work correctly, each operational process needs to be mapped out, along with liaising with operators to understand their reasoning behind how they carry out their daily tasks. Legacy systems, routine tasks and culture are all factors that need to be assessed, and can be quite cumbersome. Companies that aren’t born with this technological mindset, and that may be a little more rooted in their ways and culture, will find this transition a difficult one. Collaboration, discussions and input from employees are vital to the success of the AI implementation, as well as strong leaders who can drive the vision for the project and the benefits for everyone involved. If people feel they may lose their jobs, or they’re not being listened to, they will soon lose interest.
It is an overall business shift, not a technological one, and you need everyone on board to drive the project forward.
Before you make any AI investments, of which there will be many as the project progresses, develop a clear goal for your organisation. Why do you want to implement AI? What problems are you trying to solve? What’s not currently working well in your company? Don’t work backwards and bring an AI project to try and fix something. Find what needs fixing and see how AI can benefit that. After all, the whole point of AI is to use the insights found from the analysis to drive better decision making or provide never-to-be-seen insights. Pick one of the problems that you think an AI would solve and give a big win, and then grow from there.
Many older organisations have developed siloed departments that can hinder the performance of the company and individuals. This is similar to the first point in that an AI project really can drive an organisational change if everyone is willing to accept this and move forward. Departments need to accept this change because the mix of disciplines and perspectives from each department will develop new ways of thinking around the project and how they can benefit each other through the insights provided by AI. When your organisation brings in data experts, and they reach out to departments as a whole unit, they’ll also be a lot more likely to get on board with the project too.
The final habit we’ve found companies with significant AI adoption tend to have is that they’re agile and flexible in their thinking and project implementation. These types of companies adopt AI incrementally, rather than seeing it as one massive project that needs to be adopted by a specific date. Instead, they break it down into sizeable chunks and readapt if the project isn’t going according to set timelines. AI projects involve a lot of tweaking and learning along the way because it’s impossible to predict precisely how the data is going to look when it is processed anyway. Once the foundations are in place, it is merely an issue of learning as you progress, rather than viewing any bumps as huge mistakes and having to start over.
As with any massive project undertaking, you are encouraging your team and workforce to get involved from the outset. But having a clear plan and story for why you want to bring about this change is just as important. AI is taking the world by storm, and it’s no longer impacting only the big Fortune 500s either; it’s everyone’s game for the taking. Our experts at Davra, a 2020 Gartner Magic Quadrant company, work with AI and IoT-implementation projects of various sizes every day and are well versed in mapping out each goal and business outcome to deliver top-quality results. If you would like to see how we help our clients build solid habits and foundations, please contact us today.
Brian McGlynn, Davra, COO
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