Today machine learning is relevant for every business. It is being used across departments to increase customer engagement, improve product fit and recruit top talent. Fifty-six percent of the respondents of a McKinsey survey reported AI adoption in at least one function, up from fifty percent in 2020. Most companies have hired data scientists, are doing POC’s and are beginning to declare publicly that they have an AI-first strategy.
Despite the progress, there are still many challenges in the way of adopting AI. Based on our MLOps survey of 290 AI professionals, 80 percent of machine learning applications never make it to the end-user. AI models often stop working as planned when they are put into production, and projects are delayed or eventually canceled due to unplanned complexities. In order for AI to stay on top of the agenda, there needs to be a company-wide commitment and a fundamental shift in business thinking based on the awareness of the power of AI to drive revenues.
Five steps a company can take to shift to an AI-first strategy
1. Make AI the center of the business – To make AI-first work, it has to be the center of the organization. Every department needs to investigate how profitability can be improved, whether if it’s by more accurately forecasting sales, increasing customer engagement, or improving product quality. This type of initiative is relevant for all types of industries including education, healthcare, e-commerce, manufacturing, etc.
2. Make a strong foundation – In order for machine learning models to run smoothly, there needs to be a strong data foundation where it is collected and prepared efficiently. Many of these tasks can be automated to streamline data pipelines. There also needs to be sufficient flexibility to add and change data sources on the fly as model requirements change and flexible infrastructure that includes hybrid clouds, on-premises, and on the edge. Ideally, software teams should have access to a wide variety of processors including AI accelerators with the ability to pick the one that can best handle the AI/ML workload with the best price/performance ratio.
3. Make AI sustainable – For AI to continue to provide meaningful insights in the long run, machine learning models need to plug easily into other systems to fit into the organization. Models need to be continuously monitored to measure drift and when necessary, there must be the ability to inject data easily for models to be retrained. In addition, after data is initially cleaned it needs to be monitored and rechecked continuously to maintain data health.
4. Make sure you can scale AI – In order to ensure the adoption of AI, models need to be moved as easily as possible into a production environment. Models should be able to scale up to support a growing number of users without manual intervention. Machine learning capabilities need to be easily added to any application without requiring long training processes or hiring new data science professionals.
5. Make software developers AI creators – Making AI applications should become part of everyday work by encouraging developers to use off-the-shelf software. There are data connectors available engaging developers to use any machine learning task, including machine vision, recommendations, and sentiment analysis with the ability to build working pipelines with just a few clicks. Many models are pre-trained and developers just need to bring their own data.
With the new online economy in 2022, companies will race to use AI insights to capture customer sentiment, provide targeted personalized recommendations, streamline operations, optimize supply chains and more.
By making AI the center of the business, building a strong and flexible foundation and making tools and pre-trained models available to developers, enterprisers can make the shift to creating a competitive advantage based on powerful insights.
