A recent survey from McKinsey showed that 56% of respondents reported AI adoption in at least one function, up from 50% the year prior, with the three most common use cases focused on service-operations optimization, AI-based enhancement of products, and contact-center automation. Businesses are committing huge amounts of money to AI initiatives. According to Appen’s 2021 State of AI report, AI budgets increased 55% year-over-year, reflecting a shift from an experimental project mindset to an expectation of business benefits and ROI.
One reason this shift is happening now is that many businesses have built expert data science teams and matured their understanding of the discipline. However, this has not proven to be enough to maximize the business potential of AI initiatives and deliver the desired ROI. What these businesses still lack is a best-practices approach to preparing data for the AI lifecycle. AI teams also need the right tools and techniques to help them gain greater insight into and better manage the lifecycle.
Moving forward, the success of AI and machine-learning initiatives will depend largely on a business’s ability to tie the right business use case to the right model, which has been trained using high-quality, properly-sourced data. Getting this rhythm down is at the heart of AI deployment and will help to reduce complexity within the lifecycle and ensure scalability and success sooner and longer.
Data lifecycle steps and considerations
AI teams tend to say that their main challenge isn’t building the model itself but understanding exactly how to source and label the data at scale, managing the models long-term, and checking for real-world model performance. The AI data lifecycle is dynamic and ever-changing, and the approaches we take to manage its different components need to be dynamic as well.
Here are some key considerations to keep top of mind as you move through the lifecycle:
It takes patience and dedication to realize the benefits of AI. You’ll know you’re doing it right not when you wrap up a project but when you can take your learnings and apply them to other scenarios and functions within your organization. Success in AI means iterating quickly and building in a repeatable, scalable way. If you keep these data lifecycle considerations in mind when building AI, and if you don’t skip any steps or take any shortcuts, you’ll be on your way.
Sujatha Sagiraju is Chief Product Officer at Appen.
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