Part 2 of our three-part series, highlighting main themes of Forrester’s 2024 B2B Summit.
In the digital age, trends come fast, and B2B brands are not immune to the effects. A prime example is AI. Though AI has been a part of business applications for at least two decades, the advent of Gen AI has taken the world by storm, creating a frenzy around its functions and potential.
But as companies restructure their roadmaps to incorporate AI, many are left scratching their heads when it comes to determining the best AI strategy. A further problem is assessing whether their organization’s current processes allow them to be AI-ready. Most of all, how do they determine if AI will create business value? Let’s explore what constitutes AI.
Of course, that was a tricky question. The truth is there are several different types of AI, each with its own value and purpose. Forrester tends to group AI into five categories:
- Automation AI: This is a type of AI that automates background tasks. If the parameters are clear and data is normalized, automation AI can handle the job. This type of AI has been around for so long it’s almost taken for granted rather than recognized as AI, powering things from customer journey automation to dynamic content and copy within emails.
- Perceptive AI: a step further than Automation, this type of AI processes large amounts of data and information and provides related insights. This type of AI is also old hat, used mainly in reporting aggregators and analytics tools. This is because those types of tools are best suited to process vast amounts or reliable data. However, predictive AI has been making a splash, to great or not so great effect, (see recent Google search memes), proving that AI outputs are only as good as the reliability of the data being consumed.
- Predictive AI: This AI hit the scene several years ago, mostly due to the creation of Salesforce’s Einstein. Predictive AI does just what it says. It identifies trends within sets of data to make predictions across a score of use cases: from product recommendation carousels to a person’s buying propensity to inventory and sourcing level alerts. The sky has become the limit for predictive AI. Of course, this assumes that the data being fed the AI is accurate. Otherwise, you’ll see children’s Adidas shoes recommended in your shipping confirmation for packing tape like Amazon recently sent me.
- Prescriptive AI: Taking Predictive AI one step further, prescriptive AI takes insights from data to recommend the next best action to take with a user at a specific point in time. Utilizing this type of AI effectively requires data that is very timely, ideally real-time. The last thing you’d want is to have a sales or service rep prompted to make a renewal call to a customer that cancelled their contract the day before.
- Generative AI: Finally, we come to the type of AI that everyone is talking about. The one that has become synonymous with simply, “AI.” This involves taking natural language models and teaching an AI to produce novel and new content altogether: from code, to copy, to images and directives. It’s powerful stuff, which can be a curse or blessing as we’ve seen with the most infamous gaffes, like buying a car for $1. This also extends to things like the editor in Microsoft Word giving me suggestions to improve my writing and grammar as I type this article It’s also the type of AI that requires the most outlay of time, money, and precision.
Organizations can utilize each type of AI as their digital maturity grows. As more AI-driven applications are incorporated, the more confident you need to be in your systems and data. Which brings us to the running theme and prerequisite we’ve seen in each type of AI.
Normalized Data
Reliable Data
Accurate Data
Timely Data
AI-Readiness begins with data cleanliness. And if this is sending you into a spiral, don’t fret. Data cleanliness is an issue for many organizations, especially in the B2B space. A whopping 27% of organizations reported that their data is entirely unstructured, according to Forrester. Almost everyone is behind when it comes to data integrity, so it’s not too late to begin from the ground up.
And if the task seems too herculean, Smith is here to help. Our newly formed Martech and Personalization group contains data-obsessed experts who can work with you to make your data as pristine and valuable as it can be.