Artificial Intelligence
Lessons Learned from GTM Consulting
For the past two years, I have performed part-time, contract go-to-market consulting. My wife had a surgery that went wrong 18 months ago, so I needed something that would allow me to take care of her, stay sharp, earn money, and help companies grow. What I encountered was quite different from what I expected, so I thought I would pass it along.
Most of the work was with small to midsize companies, but the problems and needs mirrored what I have encountered at larger companies. The main difference is that large companies tend to look to software to address problems. In contrast, smaller companies often lack the budget for what they view as a solution that increases complexity.
Here are my Top 5 findings:
- GTM plans are often developed at the highest levels, often in isolation, without market testing and validation.
- An interesting aside is that the company is often really seeking sales optimization but believes it is doing things “well enough” today and therefore needs to focus on new offerings and revenue streams.
- New perspectives on past performance and failures are well received but more surprising than anticipated. This leads to a better understanding of needs, which builds consensus moving forward.
- Sales teams are sometimes pitted against one another, rather than working together to help everyone achieve more (Coopetition – “A rising tide lifts all boats.”)
- Sometimes the competing team isn’t sales, but support. The team wants to help the customer (which is great), but works outside its defined scope instead of bringing in the services and sales teams to work jointly to solve the customer’s problem.
- Sales teams are focused on selling features rather than solving business problems.
- Training those teams on solution selling and understanding the prospect’s needs pays off.
- CRMs are not consistently used and often reflect idealized fiction rather than reality.
- Old, dead, or unqualified opportunities; lack of recent contact or interaction; deals that have slipped more than once; and a lack of understanding (company, needs, players, business environment) all point to an unrealistic pipeline.
- Sales management and teams are not leveraging AI to help focus their efforts.
- Conversely, they may view AI as a panacea, investing time and money in tools that supplement a strong team rather than focusing on strengthening the team.
Here are the related Lessons Learned:
- Selling is a byproduct of problem-solving. You can’t solve problems if you don’t know what they are. Every interaction with a prospect should focus on gathering information, building trust and relationships, and leveraging prior interactions to demonstrate that your solution will solve their problem and ease their pain.
- Here’s solution sales again. Teaching teams to ask better questions, listen more, and validate their understanding increases their standing with prospects.
- Identifying common business problems and describing how your product or service solves them should be the foundation of the plan.
- Perform market analysis. How do other companies describe those problems? Their terminology, often found in job postings by competitors and your target audience, can help create effective messaging that resonates. Work to become the natural fit for what your prospects are seeking and the problems they are likely dealing with.
- Individual contributors get paid to win, but sales management needs to create incentives for collaborative efforts that lead to both wins and ongoing customer growth.
- Paying sales teams for net new business only causes them to ignore install base expansion opportunities. And, if another vendor solves their problems, it is only a matter of time before they replace you.
- For one company, I convinced them to implement a 2% SPIV (like a SPIFF, but team-focused) for every team member who actively contributed to team improvement. SPIV payments were quarterly, and there was a running total so the team could see the fund growth. Initial indications of a positive impact are good.
- Another benefit of collaboration is that it helps teams focus on approaches that work due to ongoing testing and refinement. Collaboration also helps teams focus on a more accurate ICP (ideal customer profile). Sales management can then feed their findings back to Marketing to tailor and fine-tune their efforts.
- CRMs often either lack information or are full of wishful thinking. They focus on activities, and not progress and next steps.
- Using MEDPICC as a foundation for qualification is a much better start.
- Sales managers need to validate the information independently to ensure their teams are upfront and honest. Trust, coaching, and collaboration work together for the win.
- Chasing deals that are unlikely to close wastes valuable resources.
- AI is not a panacea, but it is very effective for research, market validation, prospecting, and meeting preparation.
- Going in prepared builds respect and credibility, saves time, and helps you quickly qualify prospects in or out.
- There may be opportunities to nurture prospects who have potential but aren’t qualified for immediate deals, seeding the pipeline for future opportunities. This could be a great place to leverage AI for personalized journeys with highly relevant curated content.
So, what are your thoughts? Have you seen some of these problems yourself? How did you handle them? Let me know in the comments below.
And if you are looking for assistance with your business, contact me.
Six Ways AI Can Become a Sales Management Enhancer
As a VP of Sales, I would spend the first 60-90 minutes of every day reviewing a dozen news sources for new technology, competitor announcements, proposed legislation, and M&A news. I looked for anything relevant in critical industries, news about our customers or their top customers, and staffing changes within their companies.
My goal was to identify anything that could negatively impact deals in play, threaten the customer base, disrupt the run-rate business, or create opportunities to break into a new company or displace a competitor. You feed your findings and speculations back to your team, along with suggestions, talking points, or specific directions to help them maintain or increase their success for the current quarter plus the next few quarters.
You look for trends and leading indicators that could help your team and your organization achieve greater success. Winning feels good, and the rewards drive you to achieve even more.
Artificial Intelligence (AI) can do all this and more. It will be more consistent, analyze information without bias, and do so faster.
1. Automated Intelligence Gathering
Focus your efforts where they add the most value. AI can automate the collection and analysis of data from multiple sources, including news feeds, legal updates, social media, and competitor websites. This automation can save considerable time and minimize the chances of missing relevant information. Natural Language Processing (NLP) can identify, categorize, and correlate relevant information, providing actionable insights without manual review.
2. Enhanced Lead and Opportunity Identification
In addition to the correlations above, Machine Learning (ML) models can analyze trends and patterns in data to identify potential leads or opportunities for expansion. By understanding market movements, customer behaviors, historical behavior, and presumptive competitor strategies, AI can suggest new targets for sales efforts and highlight areas where teams could gain a competitive edge.
3. Improved Internal Communication and Collaboration
Sales is not just about selling; it also requires internal collaboration to create the best possible products and services and identify the best approaches to generate awareness and interest in your offerings.
AI systems can serve as a central hub for information that benefits various departments within a company. By integrating with CRM, marketing, support, and other internal systems, AI can distribute tailored information to different teams, ensuring everyone has the best insights to perform their roles effectively and promoting a more cohesive, coordinated approach to achieving long-term business objectives.
4. Forecasting and Predictive Analytics
With the ability to process vast amounts of data, AI should significantly improve forecasting accuracy. Predictive analytics can estimate future sales trends, customer demand, and market dynamics, giving businesses more opportunities to make better-informed decisions—leading to better resource allocation, optimized sales strategies, and, ultimately, higher revenue.
5. Increased Efficiency and ROI
By automating routine tasks and providing deep insights, AI can free up sales and management teams to focus on strategic activities. The efficiency gains from AI can result in significant cost savings and a higher return on investment (ROI) as teams do more with less, capitalize on opportunities faster and more effectively, and ultimately make more money for themselves and their company.
6. Continuous Learning and Improvement
Machine learning models will improve over time as they process more data, meaning the insights and recommendations provided by AI will become increasingly accurate and valuable, helping businesses continuously refine their strategies and operations for better outcomes. AI will also provide constructive feedback and suggest next steps in real time, helping everyone using it upskill.
Future Perspectives
While AI may not yet be ready to take over complex roles like enterprise sales, its potential to enhance these roles is undeniable. As AI technology continues to evolve, its ability to provide highly accurate forecasts, improve win rates, shorten sales cycles, and enhance competitiveness will only grow. The future of AI in sales and business management is not just about automation; it’s about augmenting human capabilities to create more effective, efficient, and thriving organizations that can compete in an increasingly competitive global landscape.
So, what do you think? Will this work? Will it be good enough if everyone is doing it? Leave a comment and let us know.
Using Themes for Enhanced Problem Solving
Thematic Analysis is a powerful qualitative approach used by many consultants. It involves identifying patterns and themes to better understand how and why something happened, providing context for other quantitative analyses. It can also be used when developing strategies and tactics because of its “cause and effect” nature.
Typical analysis tends to be event-based. Something happened that was unexpected. Some type of triggering or compelling event is sought to either stop something from happening or to make something happen. With enough of the right data, you may be able to identify patterns that help predict what will happen next based on past events. This data-based understanding may be simplistic or incomplete, but often it is sufficient.

But people are creatures of habit. If you can identify and understand those habits and place them within the context of a specific environment that includes interactions with others, you may be able to identify patterns within the patterns. Those themes can be much better indicators of what may or may not happen than the data itself. They become better predictors of things to come and can help identify more effective strategies and tactics to achieve your goals.
This approach requires that a person view an event (desired or historical) from various perspectives to help understand:
- Things that are accidental but predictable because of human nature.
- Things that are predictable based on other events and interactions.
- Things that are the logical consequence of a series of events and outcomes.
Aside from the practical implications of this approach, I find it fascinating relative to AI and Predictive Analysis.
For example, you can proactively monitor data, activities, and patterns by understanding recurring themes and triggers. That provides actionable intelligence that can be automated and incorporated into a larger system. Machine Learning and Deep Learning can analyze tremendous volumes of data from various sources in real-time.
Combine that with Semantic Analysis (and products like AllegroGraph) to harness the power of language through taxonomies and ontologies. The monitoring system would better understand what is happening (not just be aware of it), possibly determine how and why it happened, and result in even more focused and accurate predictions. Finally, include spatial and temporal data such as IoT, metadata from photographs, etc., and you should be able to view something as though you were very high up – providing the ability to “see” what is on the path ahead. It is obviously not that simple, but it is exciting.
This approach provides a multi-dimensional view of events and their causality, giving you the means to identify and prevent complex problems before they negatively affect a business.
Keeping these thoughts in mind will help you see details others have missed. Better tools can make for better analysis, better strategies, and better outcomes. Who wouldn’t want that?
The Coming Changes to Manufacturing
Recently, I spoke with someone on a team analyzing ways to “mitigate the risk of exclusive manufacturing in China” without fully divesting their business interests in a growing and potentially lucrative market. This bifurcation exercise got me thinking about how many other companies are evaluating their supply chain relationships, inventory management, and the predictability of their cost of goods sold.

In the mid-1990s, I had done a lot of work with the MK manufacturing software that ran on the Ingres database. Some issues were performance-related and fixed by database tuning; some were fixed by using average costs instead of a full Bill of Materials (BOM) explosion with dozens of screws in a window; but some were more interesting and more business-focused.
After NAFTA became law, one manufacturer built a facility in Mexico and started manufacturing a few basic but important parts. When I arrived as a Consultant, the main problem they faced was a reject rate of roughly 20% and additional related QA costs. My suggestion was to treat this part (a single piece of steel, like the rotor from a disk brake system) as a component and build in the cost of both scrap and QA. They could then benchmark the costs against other suppliers in an apples-to-apples comparison to determine if they really saved money. That approach worked well for them.
While that approach helped manage costs, it did not address the timeliness of orders or lead time required – important aspects of Just-in-Time (JIT) manufacturing. Additionally, it should be possible to estimate shipping costs by considering changes in petroleum costs or anticipated changes in demand or capacity.
Systems out there claim to estimate the cost and availability of commodities based on various global factors and leading indicators. It is tricky, to say the least, and we can’t anticipate an event like a pandemic. But companies that manage their inventory and production risk best will likely be the ones that succeed in the long run. They will become the most reliable suppliers and have increased profits to invest in further growth and improvement.
The next 2-3 years will be very interesting due to technological advances (especially AI) and geopolitical changes. Those companies that embrace change and focus on real transformation will likely emerge as the new leaders in their segments by 2025.


