Is AI That Good, Or Are We That Bad?

The answer to the question, “Is AI that good, or are we that bad,” is resounding YES!   To both.

Everyday, we see great applications of AI, particularly when coupled with outstanding human based thinking.  At the same time, I see endless examples where AI isn’t doing anything better, it isn’t outthinking sellers, it’s simply outperforming sellers in fundamentals.  It outperforms them in the things they should be doing, but simply aren’t, or they are doing them poorly.

What we see is a knowing-doing gap in how so many sellers and GTM professionals work.

We know what we should be doing in our jobs and engaging customers.  We know we should focus on the customer and their problems.  We know each outreach and engagement needs to be deeply personalized to the challenges both the organization and individual face.  We know how we should be executing the selling process, how to do great discovery, how to create value in every interaction.

We have tech stacks that can improve our ability to execute these things more efficiently, and sometimes more effectively.

As leaders we know we have to be coaching and developing our people.  We know we have to get them the resources they need to help them perform.  We have endless reports, analyses, and insights to help us identify performance challenges.

While at all levels, we know what we should be doing, why it’s important, and how to do these things effectively and efficiently.

Our problem is seldom a lack of knowledge.  Our problem is that we fail to execute those things.

There are any number of reasons we fail to do this.  Constant shifting of direction and priorities, inconsistency in execution, failure to identify and address performance, lack of engagement, and, sometimes, laziness.

The majority of the time it simply comes down to uninspired, unimaginative, undifferentiated execution.

So often, we just see AI/LLMs doing the basics much better than we are.  Consistency in researching and prepping, consistent data hygiene, consistent follow-ups, consistency in writing–whether emails, call/meeting prep, presentations.

While, so often, we see these as not great, they are often better than what we do because they do what we know we should be doing.  But they do these things consistently, never making excuses, never taking short cuts, never doing it half heartedly, never just going through the motions.

We are “impressed” with AI because it is doing what we should be doing, consistently and without excuses.

We seem to have normalized mediocrity.

What do we do about this, where do we go?

Instead of using AI as a crutch for our bad habits, we need to focus on elevating our own execution—at all levels.

We do this through owning the basics:

• Doing the things we know we should be doing, but neglect.  Focusing on our ICP, focusing on what the customer needs, not what we want to talk about.  Researching, preparing for each interaction, customizing each outreach to the individuals we are engaging, understanding what customers value and demonstrating how we create value with them.  It’s using the tools, the processes as we’ve intended to.
• It is through systematic execution.  The right targeting, executing the processes, keeping good habits in data hygiene, updating, understanding and building high quality pipelines.  This is not just about hitting the numbers, but it is hitting them doing the right things with the right people in the right way.
• Ownership, accountability, vicious prioritization, intentionality.  Rather than creating excuses, focusing on understanding what is happening, what we should change, what we should improve, how we grow as individuals and organizations.

The magic is, that as we more consistently execute these basics, we can leverage AI/LLMs with far greater impact.  We already see top performers getting more out of the very basics of AI than all others.  They are using it in more creative ways.  They are using it, less for giving them the answers, more for helping them think more deeply about what they are trying to achieve.

As we look to the future of AI/LLMs, we can see previously unimaginable opportunities.  The promise of agentic AI helping each of us perform at higher levels is tremendous.   And as we look at this, we also have to look at the things AI can’t do, and where human strengths/capabilities are the differentiator:

• Emotional intelligence, the ability to connect deeply with the people we are working with.
• Contextual intelligence, the ability to understand what is most important at “this moment of time with this individual.”
• Adaptability and creativity, the ability to recognize what’s most important at this moment in time and to generate ideas in moving forward.
• Collaborative engagement, the ability to engage others innovating in ways that each individual couldn’t do alone.  Stated differently, it’s making 1+1=7, rather than 2 or less.
• Critical thinking and problem solving, the ability to constantly challenge assumptions, the status quo, and collaboratively develop new ways of looking at things.

When these skills are combined with the capabilities of AI, we will achieve co-intelligence.

Afterword:  This is the AI generated discussion post.  It’s very good and much more succinct than prior discussions.  I still get amazed about how these AI characters talk about AI as if it is a distant third party.  Enjoy!