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What An MBA Didn’t Teach You About Sales

The sales profession is challenging. You need to work hard at it to succeed. You need to learn from the best. You need to improve your skills continuously. If you think you can sell since you are a hit at parties and have a lot of friends, you may soon find that you are a failure as a salesperson. Blunt truth:

because the sales profession is so hard, you have to focus on doing everything in sales very well, or you will be considered a failure.

I call this blog, Skinned Knees because I try to relate all of the learning that I have done over the past 4+ decades (while skinning my knees in the learning process).

I hope that you learn from my mistakes so that your business will grow!

Automating Sales Workflows: When to Use Automation Over Chat

In sales management, there’s often some confusion about when to use artificial intelligence chat interfaces versus automation workflows. Chat interfaces are ideal for creative problem-solving, learning, and strategic research, while automation excels in repetitive, high-volume, data-driven sales tasks. The trick is to recognize when consistency and scalability are more important than customization.

Automation delivers consistent execution, eliminates human error, and operates 24/7. Sales leaders can rely on it for triggered communications, data synchronization across systems, CRM updates, and compliance tasks that require accuracy and complete audit trails. By moving these routine tasks into automated workflows, sales teams free up valuable time for relationship building, revenue generation, and refining sales strategies.

Real-world examples highlight the impact: a team once spent three hours daily crafting manual follow-up emails. Shifting to automated sequences not only saved time but also improved messaging consistency and pipeline response rates. Similarly, another team utilized automation to synchronize sales data across six systems, thereby eliminating bottlenecks and enabling sellers to focus fully on sales.

Hybrid approaches really take things to the next level! By merging human creativity in chat interactions with the quick and precise power of automation, businesses can craft workflows that beautifully balance personalized service with the ability to grow. This type of teamwork enhances value-driven selling, sharpens business skills, and accelerates revenue management throughout the sales journey.

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Choosing the Right AI Stack for Your Sales Organization

A VP of Sales recently confided in me: “We have six different AI tools, but our reps are still doing manual work. What went wrong?”

This is the AI tool proliferation problem. Sales leaders often collect tools without a strategy, mistaking a pile of features for a cohesive system. It’s like buying a hammer, screwdriver, saw, and drill without realizing you’re actually trying to build a house. An effective AI stack means integration. When tools work together, they amplify each other’s value. When they don’t, they add complexity, confusion, and wasted money.

Why Strategy Beats Random Adoption

Random tool adoption is rampant across sales organizations. Teams chase shiny new software, often ending up with overlapping features, siloed data, and productivity lost to tool-switching. Instead of solving problems, the stack itself becomes the problem.

But when built strategically, the benefits are profound. Integrated systems reduce manual data entry, accelerate response times, and deliver actionable insights for reps. Three well-chosen, well-connected tools can outperform six isolated ones. Integrated stacks also improve adoption rates by providing consistent interfaces and reducing training overhead.

The Five-Layer AI Stack Framework

To avoid the chaos of random adoption, I use a five-layer framework for structuring sales AI tools:

  1. Data Foundation – Your CRM and data management system, enriched and maintained for accuracy.
  2. Intelligence & Analytics – AI-driven insights, lead scoring, forecasting, and market intelligence.
  3. Automation & Workflow – Sequences, task automation, and cross-platform orchestration.
  4. Content & Communication – AI writing, proposal generation, and customer-facing tools.
  5. Optimization & Learning – Conversation analysis, performance tracking, and continuous improvement.
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