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    Cutting through the hype: Generative AI adoption in B2B

    Cutting through the hype: Generative AI adoption in B2B

    Ah, there it is again — the genAI buzz. It’s going to transform B2B sales and marketing, right?

    You’ve been on LinkedIn. Like me, you’ve come across a flood of posts waving hands in the air — AI is a magic pill promising to skyrocket productivity, slash costs and practically do your laundry. But here’s the wet blanket: If AI adoption was as straightforward as these posts suggest, why does nearly everyone find themselves with a case of the hives when they think about it?

    United in hesitancy and confusion

    • Boston Consulting Group gives us some numbers that’ll probably make you feel comfortable — 62% of companies are stumbling over a talent and skills gap, 47% can’t figure out where to invest and 42% are scratching their heads over how to deploy AI responsibly. Only a brave 5% claim they’ve got their genAI game on point, scaling and thriving.
    • According to Gartner, while 90%+ of enterprise organizations are mulling over their AI strategy, only 11% have deployed an AI solution.
    • For professionals who recognize AI’s significant positive impact on sales performance, 45% still report feeling overwhelmed by the non-stop nonsense of AI tools and applications around them.
    • Rain Group also found a substantial training gap: 85% of sales professionals do not receive formal training on using AI in their roles.

    The hype machine has us believing AI can appear and drive success, like pulling the ROI rabbit out of a hat. But here’s where reality rudely interrupts our dream: It’s not easy, and much of the advice you read is ungrounded. The crypto and EFT experts from 2022 have become AI experts in 2023.

    Dig deeper: 3 ways B2B marketers can use generative AI

    Today’s options don’t seem reasonable

    GPTs are like the DOS prompt from the 1980s: It feels like the DOS prompt time-traveled to the 21st century and decided to throw a party. It should never have been this complicated. 

    Embedded AI solutions are so generic that they remind boomers to go grocery shopping. All AI solutions have a notably “vanilla” sense of the world. Sure, they can tell you about farming best practices in the Sub-Saharan Desert. Still, they aren’t very effective at understanding how your solutions are better, different or more valuable than your competitors’.

    So, what’s the next step?

    Have a plan

    You don’t need to fix something into granite, but you can’t go without an opinion. AI is evolving fast, so you should prioritize flexibility over rigidity and an operating framework over verbose guidelines. 

    Think of your strategy as a sketch, not a blueprint; detailed enough to guide, yet malleable to change. Start with clear business goals and initial solution areas to achieve those goals. I typically call out “use cases” in this part of the dialog. At this point, you need to pull together your current thought leaders without pulling them down in politics. 

    Starting small but strategic

    AI technologies will change more and faster — stay agile, adaptable and always inquisitive. This mindset empowers you to pilot AI solutions that address specific pain points without overhauling your entire system. 

    Identify areas where you can deliver quick wins, be it enhancing customer service, streamlining operations or personalizing marketing efforts. Use these initial projects as learning labs, gathering insights and data that inform broader strategies. 

    Embrace a culture of experimentation, where successes are scaled and failures are “earned learning.” Stay progressive and grounded, ready to adapt as new advancements emerge.

    Dig deeper: AI in marketing: Examples to help your team today

    Context is king

    Shift from prompt engineering to training data. Today, you might be limited to 4,096 characters in a prompt. But even if that threshold tripled, it wouldn’t solve the obstacle that genAI knows nothing about how you differentiate your business. Train it. 

    GenAI doesn’t understand your market positioning or value propositions; it needs your input to become an asset. Based on the use cases in the prior step, feed it rich, context-specific data that will educate it about what sets your business apart. 

    Call it strategic nurturing or training data. This step transforms genAI from a generic tool into a custom-tuned partner that can help you become more effective — and then become more efficient.

    Productivity or performance: What’s your choice?

    This is a critical consideration in AI adoption. Prioritizing performance (efficacy) is the foundational step — establishing the effectiveness of AI solutions in achieving desired outcomes. Without proving AI’s capability to enhance decision-making, customer engagement or any other targeted performance metric, merely boosting productivity (efficiency) is like driving faster because you’re lost. 

    First, determine that AI-driven strategies effectively address your business challenges and goals. Once this performance, or efficacy, is locked in, leveraging AI to streamline processes amplifies these successes, making efficiency gains not just faster but fundamentally smarter and more impactful.

    Dig deeper: From efficiency to efficacy: 2024’s B2B marketing revolution

    People, process and technology

    AI is real; it’s here to stay and we all need to get over the resistance and understand how this new technology will affect the people and processes around us. Find technologies that help you achieve context and are easy to use, and provide your team with specific areas of demonstrable value. 

    In all cases, plan to train and onboard people to these tools. Perhaps it’s like learning to ride a bike, but falling hurts less when you’re a young kid. You will fall, but get back up and do it again. Be the kid you used to be!

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    The post Cutting through the hype: Generative AI adoption in B2B appeared first on MarTech.

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