Cutting out the hype from reality with AI for B2B
Whether it’s streamlining content production or the reimagining of marketing automation entirely, you’ve likely seen a lot of hype around AI in B2B. But with all the noise surrounding this wave of new-age tech, how do you separate the next big client solution from those looking for their next big sale?
It feels like we’re on the cusp of one of those historic, evolutionary hyperleaps into a new era of knowledge, productivity, and innovation. Artificial intelligence (AI) has the potential to change almost everything in our lives – a prospect that’s exciting and terrifying in equal measure.
AI tools and large language models (LLMs) like ChatGPT have caused some to predict the end of high school English as we know it. AI-generated images have pundits in an uproar after winning a photography competition. And AI song covers are taking social media by storm. But the question around any new technology isn’t how much hype it can generate. It’s how much impact it can deliver – a point that’s especially true in B2B industries.
Expertise or the lack thereof
AI isn’t an entirely new concept in B2B. It’s a term that’s been thrown around by many for years without any form of challenge as to what it really means in practice. It’s understandable when considering the potential gains for both productivity and efficiency – up to $2.6T worth in marketing and sales alone.
But now that Pandora’s box is well and truly open, everyone and their cat seems to be jumping on the AI bandwagon. What’s worse, they all suddenly seem to be specialists – a fact that was brought to my attention at a recent B2B marketing industry event about the emergence of AI in B2B. The host invited a panel of ‘experts’ representing a cross-section of B2B marketing leaders to explain how they’re using AI to innovate.
The session was consistent with much of what we already see across the media landscape: companies and agencies throwing the term ‘AI’ around without truly understanding what they’re referring to. And after wincing through an hour of discussion, it was painfully clear how easy it is for B2B Marketers to get lost in the smoke and mirrors.
Quantum leap or quivering stumble?
As is so often the case in B2B (and in marketing in general), we have one hundred and one ways we could leverage AI. Swathes of generative AI tools are being released week after week, with many getting smarter every day. But I’d wager a large majority would have a negligible impact on your business – especially when in the hands of those who don’t understand their potential.
Most of the use cases for AI tools today now have a certain novelty to them. We’ve all read AI-generated write-ups that sound remarkably accurate, if a little factually loose. And who doesn’t love asking ChatGPT to write an OOO message in the style of Blackadder? The trouble is, the sheen of generative AI tools whose sole purpose is regurgitating information gathered from the internet can fade very quickly.
Lightening admin burdens, improving communication, and even helping create new content are all scenarios in which a business could look to adopt AI tools into their workflows. However, they’re hardly the quantum leap forward some are making it out to be. So, what is? And what should you look out for when discussing supposed ‘AI solutions’ with providers?
AI tools are only a piece of the puzzle
Understanding your business case and asking the right questions is part of realising AI’s transformative potential:
- Where can AI tools save time on tasks that deliver no differentiated value?
- How can they streamline or automate workflows to drive new efficiencies?
- Can they boost bottom-line revenues when companies are feeling the pinch of a downturn?
Let’s take the multibillion-dollar B2B sales industry as an example. As economic conditions remain challenging, extra pressure falls on revenue teams to produce and preserve as much value as possible. AI tools and generative AI platforms might help them eke out as much value as possible from their workday to help maximise every single revenue opportunity.
However, notice how I wrote that understanding those baseline questions was only part of the bigger AI picture. There’s one element of the equation that’s often overlooked: generating tangible business value for a specific part of the organisation (like revenue teams) requires a deep domain understanding integrated into the solution.
AI tools can’t understand customer needs and behaviours, nor can they predict how to personalise the buying experience consistently enough to drive revenues without being provided the contextual domain-specific info they need to operate. What’s missing is the layer that feeds the AI tools with what they need to drive value across the organisations: human expertise.
The human challenge
The challenge in B2B is that buying journeys are complex, and humans are nothing if not irrational. Make no mistake, AI solutions can be transformative tools for B2B businesses. But they aren’t good at accounting for the inconsistencies in human behaviour. It’s something that needs to be trained and supervised.
B2B has never had an issue with data and insight gathering. The challenge has been (and always will be) how to use what we find and the tangible steps that stem from those learnings. Tools are only as good as the user, and AI in B2B will be no different. After all, it’s not that we necessarily need more data and solutions in B2B. It’s knowing where they can help streamline processes and drive efficiencies.
To continue my example in the revenue world, any AI-infused customer relationship management (CRM) system worth its salt will add value to a seller by incorporating past discussions with a customer – data contributed by the sales rep using it.
For an AI tool to streamline processes and drive new efficiencies without human input, it needs to incorporate relevant extracts from previous discussions or engagement behaviours to understand your customers. Only then would it have the context necessary to feed a generative AI and inform personalised content to tailor the buyer experience.
It can be a complicated endeavour that takes time. Time for training the machine-learning (ML) model and your teams that many businesses don’t have – ignoring the need to iron out any inevitable systematic wrinkles in the process.
AI tools and platforms are still some way off from delivering the game-changing solutions we see floated around today. That’s why a blend of human creativity, expertise, and innovation and AI is still necessary for incrementally improving customer, brand, and buying experiences. And it’s why our Applied Intelligence hub exists.
Learn more about how we use cutting-edge technology, apply data, and leverage Transmission-exclusive insights to bring future-ready solutions to age-old problems. Get in touch!