Yesler’s Anthony Izzo, Group Manager of Analytics, and Shelley Morrison, Director of Digital Media and Analytics, discuss three common pitfalls B2B marketers run into when using artificial intelligence (AI) for analytics, and tell you what to look out for so you can avoid making those mistakes and set up for success when AI comes your way.
AI and machine learning are hot topics these days and it’s hard to avoid getting excited about their potential. But AI is not a magic wand. Before starting with AI, it’s important to be aware of a few issues that can affect new adopters of this technology.
Pitfall 1: Beware of snake oil
Many companies claim to offer “out of the box” AI solutions with instant and guaranteed results, and their demos are sure to be impressive. But if you look closely, most of these demos rely on highly refined data sets and are for very specific use cases with predictable outcomes.
Keep your expectations realistic. Don’t assume that you can quickly plug in your data sources and get immediate and actionable results. If you do, you’ll be disappointed.
Your organization will need someone who knows how to map a product’s individual AI capabilities to an individual organization’s operating requirements, data sets, and goals.
Only after accounting for this will you be ready to evaluate an AI platform.
Pitfall 2: Expecting dull data to deliver instant insights
Garbage in, garbage out—it’s a cliché, but one that is especially relevant in the world of AI, whose output is only as good as the input. To make the best use of any algorithm, ensure your data is of the highest quality, with robust governance and controls in place. AI produces the best results when strong statistical techniques are incorporated early, before any data is used to train any models.
To build a robust data set that can be used for machine learning, you must accomplish these prerequisites:
- Ensure accurate normalization
- Account for missing data and outliers
- Avoid sampling bias
Without these steps, AI can too easily lead you down a path of faulty decisions, negating its purpose entirely.
Pitfall 3: Putting all your eggs in one basket
AI and machine learning are powerful tools—but we’re not yet to the point where they can be relied on for every situation. For your first AI project, pick something of immediate impact that can be verified in a short-term period.
Don’t forget that the rest of your martech stack can augment your insights as well.
Whether you want to procure an existing AI product or build your own, being aware of these common pitfalls can help you choose the right ways to implement AI and machine learning into your organization in a scalable and sustainable way. Interested in talking with one of our experts about analytics best practices? Get in touch! We’d love to learn how we can help.