Original post can be viewed here on LinkedIn.
It didn’t take attending too many sessions at the recent B2B Ignite event in June in London to realize that AI is the next big thing to get marketers salivating. I’m pretty sure you could have predicted this happening a few years ago too. As marketers have sought to deliver measurable commercial results to the business, so investment in marketing technology platforms has grown from baseline-tactical levels to strategic-business-platform levels of significance. The reality, though, is that the practical results of new martech investments have not always lived up to initial promise – at least not until a level of product maturity and experience has developed. Now, I’m not necessarily saying that marketers believe their own hype. But sometimes, in the quest to transform marketing operations, the realities of new software have not always been challenged before purchase. And Gartner is now throwing up the same warning flags for the next generation of technology. According to Gartner analyst Jim Hare, many software vendors are ‘focused on the goal of simply building and marketing an AI-based product, rather than identifying use cases and business value to customers.’ Put another way, we’re approaching peak-hype and every software developer and enterprise technology company is jumping onto the AI bandwagon. To be clear, I think that AI will at some point have a transformative effect upon the way marketing activity operates, but I don’t believe that the start of the adoption curve is the place to accept that we’re in a good place to see a significant impact without hefty, committed investment and a long-term roadmap. We have, of course, seen exactly this level of hype many times before. It could be argued that the first wave of martech was centered on CRM. ‘CRM will fix all of your data woes, and then you’ll be able to run perfect campaigns.’ That was the message, but it didn’t quite work out. In theory, it’s correct, and if you have your data sorted then you will run great campaigns, but it misses the fact that data is never ‘sorted’ – it’s an ongoing, evolutionary thing that needs to adapt to updates from across channels, campaigns and contact points. Data management is a process, not an endpoint. So, then the second wave came along, and marketing automation promised that you can execute all of your campaigns across channels and set trigger points to identify leads and capture information in one place, and everything will be great. The problem with this is that it never quite works out that way, and managing marketing automation well to create that perfect demand center is still a manual process requiring significant investment. And so now we arrive at the dawn of the third wave – AI. This time, AI will take care of those complexities, it will learn and respond to trends dynamically and fix all of those challenges that marketers have. This may be an oversimplification that doesn’t do justice to the giant steps that have been taken in marketing technologies, but interestingly and fundamentally, the challenge that sits behind each of these three waves of technology development is the same: ‘How do we execute marketing better?’ And so, when we arrive near the start of a new technology trend that will help us do better marketing, we start to see that every vendor is now touting AI baked into their products. What does that even mean? According to Jim Hare, ‘Most vendors are overselling the AI capabilities of their products with shiny, bold marketing when their technology provides strictly rule-based machine learning and analytics (rather than anything remotely self-learning). We are heading toward a point of dilution where AI is likely to become just another marketing term used for any software app displaying even the most rudimentary intelligence.’ If you are planning to adopt AI into your marketing strategy, it’s worth taking a quick pause to ask some questions:
For those who approach AI in the right way – as a learning exercise that is going to evolve and act as a figurehead programme to drive marketing improvement over several years – there is a significant opportunity. On the leading edge of the technology, an AI-powered customer engagement strategy could drive the business into new territory. But slip onto the bleeding edge, and it could be an expensive investment into something that isn’t really understood, has poor, zero or negative returns and could stall the business from driving forwards in other areas.
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