It’s well known that AI and ML are changing the way businesses operate, but it’s not all smooth sailing. Many companies are struggling with the notion that the technologies are too complicated to connect to their business visions. If they could only bridge their AI/ML knowledge and skills gaps, the sky would be the limit.
The reality is quite different. All the leading cloud platforms have tools that make leveraging AI and ML possible, even for novice companies in terms of their experience levels with these technologies. Developers can use these tools to dramatically change the products and services delivered by their companies, going from analog to digital. The main hurdle is not a lack of technical expertise but rather a fully fleshed-out business strategy that would support the leap to deploying AI and ML technologies. For many businesses, a shift in mindset is what’s needed.
With that in mind, it’s not easy but here are four steps a company could take to seed AI and ML strategies into their businesses:
1. Ask the Right Questions
You don’t know what you don’t know until you ask. Companies need a springboard for discussion into how their businesses might incorporate AI and ML. For example, what challenges is your organization facing right now? What are your long-term business goals? If you had unlimited resources to improve your products and services, what would you do? If you could improve your products/services without worrying about disrupting infrastructure or operations, what would you change? These conversations should happen at the executive level, with the support and participation of a company’s senior IT/technology leaders.
Once a company has batted around these questions, it’s time to take a look at the data. Begin with a thorough inventory. Remember that anywhere that data is used in the company, ML can be leveraged. This doesn’t just mean traditional databases. Your internal data can be aggregated holistically, gleaning information with anything from social media chatter to IoT connected devices. This can then be stored in a data lake, which enables the information to be kept in an unstructured way, keeping the door open for any kind of use you might think of in the future.
2. Remember That Machine Learning is Here to Solve Problems
Machine learning is already changing the way businesses do everything, from research to software development to operations and more. Many companies get caught in the trap of being too narrow about how ML can impact their business. This is why companies should start with their challenges, rather than the existing capabilities of any technology. Deep learning’s current capabilities are just the beginning. What’s coming tomorrow will hinge upon the next great idea. Companies need to learn to think big when it comes to how they might implement AI/ML. We’ve seen machine learning’s ability to solve big problems across industries and segments in the area of quality assurance, business forecasting, and online predictions:
- Quality Assurance: Machine learning is replacing manual labor, freeing up low-level staff to focus on more skilled work and design, and innovatively automating manufacturing. According to McKinsey, AI may “augment employment by around 5% by 2030, as well as improve productivity by about 10%.”
- Business Forecasting: Business success is heavily reliant on knowing what’s coming next. Forecasting sales, revenue, change or churn is the difference between profit and loss. Predictive analytics has become a game-changer. By 2021, prebuilt reports will be 75% replaced by automated insights with intuitive reporting capabilities.
- Online Predictions: Churn is the enemy of any online business, and ML is increasingly finding ways to improve user experience and keep visitors on websites for longer. Root cause analysis of why users leave and predictive models on how to ensure they keep clicking are making waves everywhere from retail to gaming. This is more than just reading the words on a screen. McKinsey commented, for example, that “Deep learning analysis of audio allows systems to assess a customers’ emotional tone; in the event that a customer is responding badly to the system, the call can be rerouted automatically to human operators and managers.”
3. Start Bridging the Gap
With the answers to these questions outlined clearly, you can begin to bridge the gap between where you currently are and where you would like to be. Using tools such as AWS Sagemaker, developers can access machine learning algorithms out of the box, testing technology in a safe Sandbox environment. However, before you begin you need to know what you want to learn.
4. Learn from Experience and Turn Machine Learning into a Valuable Enabler
Looking at some of the ways that other companies have thought outside of the box can be a helpful way to jump-start your ML journey. For example, in the case of software QA, ML is replacing manual labor, freeing up staff to focus on more skilled work and design. Business forecasting of sales, revenue, change or churn is another example of how predictive analytics has become revolutionary for organizational success. And finally, ML is increasingly finding ways to improve the online user experience and keep visitors on websites longer.
The use cases don’t stop here. The benefits of ML span industries from healthcare and manufacturing to finance and security. Everyone is starting to use machine learning in their business strategy to develop new products, better understand existing ones, open up new revenue streams, and maybe even create something revolutionary. If your business has already made the move to the cloud, nothing is standing in the way of your business taking a competitive leap with AI and ML technology.