Using AI And Machine Learning To Break Past The Constraints Of The ‘New Normal’
As we kick off 2021, business technology leaders around the globe are kicking off their 2021 IT strategic plans. If 2020 taught us anything in the technology world, it was that if you weren’t refining your business processes using advances in technology, you should be. Rather than just thinking outside the box, technology leaders should be assuming that there is no box at all.
In 2021, artificial intelligence and machine learning technologies will continue to become more mainstream. Businesses that haven’t traditionally viewed themselves as candidates for AI applications will start to embrace these technologies as they become both more affordable and easier to implement. Think these technologies aren’t for you? Think again.
A great story of machine learning being used in an industry that is not known for its technology investments is the story of Makoto Koike. In 2014, Makoto started working at his parent’s cucumber farm. If you’ve never worked at a Japanese cucumber farm, it’s hard work, which Makoto quickly realized. Cucumbers need to be sorted, and guess what — they are prickly. Using Google’s TensorFlow, Makoto initially developed a cucumber sorting system using pictures that he took of the cucumbers. With that small step, a machine learning cucumber sorting system was born.
Getting started with AI and machine learning is becoming increasingly accessible for organizations of all sizes. Technology-as-a-service companies — including Microsoft, AWS and Google — all have offerings that will get most organizations started on their AI and machine learning journeys. These technologies can be used to automate and streamline manual business processes that have historically been resource-intensive.
As business leaders continue to refine their processes to support the “new normal” of the Covid-19 pandemic, they should be considering where these technologies might help reduce manual, resource-intensive or paper-based processes. Any manual process should be fair game for review for automation possibilities.
For example, a small commercial property management firm might look at automating its building walk-throughs. In today’s Covid-19 world, these firms have increased their building walk-throughs, especially since few tenants are inside to report potential building issues. This traditional paper-based process often involves an inspector or a security guard walking through the building, making notes on an inspection form. But what if, using the power of AI and machine learning, instead the inspector walked through the building, taking pictures with their smartphone? Using AI and machine learning technologies to develop an app deployed to the mobile phone, we can train the app to recognize locations within the building from the pictures. These pictures could be used both to complete the inspection form, as well as to provide geo-location information and time and date stamps of when the inspection was completed.
If you are interested in getting started with AI in your organization, where do you start? First, look at those processes and procedures where you might have a “run book” or documentation regarding how to handle specific data or tasks when certain conditions occur. Customer service and IT organizations are great places to start. These organizations typically have very defined processes for addressing questions or troubleshooting issues, making them great candidates for online chatbots that automate interactions with both internal and external customers.
Clearly define and understand the “why” of implementing AI — what are you looking to achieve with the project? Faster response times? More accurate problem resolutions? A better customer service experience? It’s important to both clearly define the objective and how you will measure success.
It’s also crucial to note that as your organization considers implementing AI technologies, it is not considered primarily an IT initiative. While there is a strong technical component to an AI initiative, the initiative should be owned and embraced by business stakeholders. The business should define the problem to be solved, while the IT organization typically will deliver the technical solution.
Conversely, AI initiatives that are implemented in silos by business leads with limited IT involvement can have their challenges. Successful AI implementations are dependent on access to large quantities of data and can require substantial storage and processing power. These needs are best met by involving the IT organization early in the process.
The possibilities for using AI and machine learning are endless and available in every industry. Does it help if you have a data scientist as part of your technology staff? Of course, but that’s not the real requirement. The real requirement is that your leaders are willing to suspend preconceptions about the “way it has always been done” and to be open to newer, more innovative ways to refine and accomplish the required task.
Use cases for these technologies will continue to emerge in 2021, particularly as business leaders continue to grapple with balancing the challenges created in the “new normal” of the COVID-19 pandemic.
This article originally appeared on forbes.com To read the full article and see the images, click here.
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