How to succeed with AI and machine learning at scale
You’ve heard all the great benefits that AI can provide for enterprises today. From detecting fraud to predicting machine failure to understanding customer behavior — AI has the potential to deliver game-changing business value in a variety of different areas.
You may have even dabbled with AI and machine learning (ML) models in a few pilot projects. But has your organization actually delivered on the promise of AI with tangible business benefits? If not, you aren’t alone; most of your peers are facing similar issues.
Gartner predicts that over the next year “80% of AI projects will remain alchemy, run by wizards whose talents will not scale in the organization.” We have seen this happen time and again in enterprise organizations embarking on AI projects: they set up an innovation lab to ‘Do AI’, only to realize later that they haven’t been able to operationalize their ML models into real-world business processes.
Only operational ML models — models that have been integrated with business functions in production — deliver business value. So, what does it take to succeed with AI / ML at scale and operationalize your ML models? Here are a few key considerations:
Many AI / ML projects fail to deliver due to inflated expectations of what AI can do. Before starting an AI initiative, identify the goals of the project. Start with the business goals — what metrics are you trying to improve? For instance, are you trying to reduce your customer churn rate? Reduce cases of fraud? Reduce the time spent on processing customer applications? From the very outset, it’s important to clearly identify the use case, define measurable goals, benchmark current performance, and then realistically define success criteria.
AI projects can also fail to succeed due to a lack of consensus between various stakeholders. Once you’ve identified the use case, map out the different stakeholders who need to be involved. To figure this out, you’ll need to have a plan for how the output of the machine learning model — detection, classification, segmentation, prediction, or recommendation — will be used and who will use it. There’s no point in having an ML system crunch numbers and predict the insurability of a prospective client when the output is either unusable, inaccessible, or simply not planned to be a part of the decision-making process. It’s essential to plan out how the predictions will be made accessible to the downstream tools/processes/people.
The shortage of available data science talent has been well documented — and hiring for that role remains a fundamental challenge. But success with AI / ML requires more than data science skills: from data prep and model building to training and inference; it’s a team sport requiring multiple different roles, including data engineers, ML architects, and operations. Organizing and scaling the team effectively is another challenge. Do you have the right people and skills in-house to take the project from idea to implementation? You’ll need to determine whether you build up the skills — through hiring and retraining — or hire someone to help complete the project in a given amount of time. Building up the skillset helps with scale in the long run, whereas third-party advisory services may help get the project up and running quickly.
Time and again, we have seen data science projects stumble due to a lack of planning on the technology front.
