Editorial
The Gist
- AI strategy. Evaluate AI solutions for real problem-solving, not just buzz, in your AI strategy.
- AI strategy framework. Collaborative synergy and accessibility enhance AI's role in your strategy framework.
- Machine learning strategies. Trust in AI requires quality data, transparency, and adaptability in machine learning strategies.
Recent research shows that 79% of B2B companies anticipate incorporating more AI strategy this year — and that's a conservative estimate. While most organizations see the possibilities with AI and want to make the most of it, many don’t know where to start. And, an even bigger question: Can we trust it?
To navigate the complex landscape of AI, you need a robust AI strategy framework to evaluate its usefulness. Here’s what to look for when trying to figure out if there’s a place for AI in your go-to-market (GTM) tech stack.
Let's take a look at AI strategy.
AI Strategy: Are the AI Solutions Real?
Any time there’s tech as buzzy as AI, vendors everywhere start boasting about their solutions that provide it. But, as with all technology, not everything is equal. Just because a company says its software uses AI doesn’t mean that it does or that it’s more of a light layer on top of a non-AI stack. And even if a vendor is truly using AI, there’s also the issue of innovation versus imitation.
For example, consider the sales space right now. Pretty much every sales engagement vendor you can think of is touting the fact that AI can write emails for users now. Well, OK. Technically that might be true. But does it really matter? Does it truly move the needle for that sales department’s goals or the goals of the greater business — especially when each email still needs to be reviewed and edited by a human? Oftentimes, this sort of thing is nothing more than shiny pixie dust. When you choose a solution that claims to use AI, it should be solving a real problem that was formerly manual.
Here’s an example on the B2C side. My kid went to sleepaway camp this summer, and the camp shares photos every evening with the parents. But, there are endless faces in every image and in the past it would take hours to sift through them all to find pictures of your child. This year? The camp's picture sharing platform was using AI tech (facial recognition), so it only sent the photos with my daughter. This is a great example of using AI strategy in a seamless way to take a previously hard problem and make it a delight.
This instance might not seem revolutionary, but it did solve a problem. In your business, make sure the AI tech you’re evaluating does the same thing for your team. It needs to solve a real problem and be strategically aligned with your goals and the business’s goals. If you’re unsure about whether it can check these boxes, seek a proof of concept.
Related Article: Generative AI in Marketing and Sales: 8 High-Impact B2B Use Cases
Does AI Empower Humans?
Whenever we talk about AI strategy, we can’t avoid the sticky narrative about its potential to replace humans. Let’s get this out of the way. In my opinion (and that of others), AI in the workplace won’t take your job on its own, but other people who know how to use AI might, if you don’t learn to use it, too. That aside, many leaders believe that AI should be employed to empower humans and make their lives easier and better. (I also think schools should be teaching students to use it responsibly as a tool and not banning it from classrooms.)
As an example, think about using an AI strategy framework to score accounts. This puts the technology to work uncovering the best opportunities, and then enables the human to spend their valuable time on those opportunities that have the most potential. It’s an ideal machine/human collaboration.
As you evaluate AI solutions, look for collaborative synergy. If your marketing team stands to benefit most from a given tool, are sales and customer success also able to tap into some of those benefits? The other side of the collaboration coin is accessibility. Can everyone get access who may need to? How hard is it to use? How difficult is it to incorporate into your strategies and everything you’re doing?
Additionally, take into account how the technology will free you and your team up for more creative freedom. It’s not just about having AI remove the grunt work from your day (although that’s certainly nice); it’s also about giving you time back that can be used for creativity, strategy and problem-solving.
Related Article: AI in Marketing: More Personalization in the Next Decade
Can AI Be Trusted?
Trust is critical in every relationship, and the relationship between you and AI is no exception. In order to use it in your GTM, you have to have confidence that it’s reliable, accurate and consistent. The best way to ensure that? Excellent data quality.
Quality data creates quality AI. On the flip side, inaccurate data can lead to flawed insights and decisions. In other words, garbage in, garbage out. Part of your responsibility is to assess the data that's feeding into the AI. Look into the sources of the data it’s getting, what it’s trained on and other related factors.
The next part of the trust equation is transparency and explainability. We’re all familiar with the problems of GPT hallucinations, e.g. making up facts. But if the facts it cited had transparent citations, we could trust it more. In GTM, if the AI tells you a certain account is hot, is it a black box or can you see WHY it’s hot?
Trust and transparency are also inextricably tied together with ethics. Is the platform compliant with data privacy laws? Are its data capture practices fair? Do they have the right to use the data they use? Is it non-discriminatory? All of this needs to be evaluated when you’re looking into a vendor’s AI claims.
Related Article: 4 Rules to Preserve Brand Trust When Using AI in Digital Marketing
Is the AI Adaptable?
Finally, how adaptable are your AI solutions to your business? Going back to the example above of the technology telling you that an account is hot, what if you know it’s actually not? So the AI flags an account as hot, but you know that the key decision-maker at the company has just formed a partnership with your competitor. Are you able to adjust the AI to incorporate the information you have?
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Can you change the AI as you gain more knowledge, and adjust it to meet your specific business requirements? Will it scale with you? Make sure to dig into customization flexibility as you’re evaluating solutions.
In closing, the integration of AI into your B2B go-to-market tech stack is more than a trend; it's a strategic imperative for sustained market leadership. But navigating AI strategy is nuanced, requiring a blend of skepticism and optimism. Use the framework outlined here as your blueprint for informed decision-making, ensuring that you're not just adopting AI, but mastering it to drive tangible business outcomes.
This is the future of B2B, and the future is now — so be sure to equip your team with the insights and tools they need to excel in this AI-accelerated environment.
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