AI
Leading Next-Generation Sales Teams: The Mandate for Predictable Revenue
The sales landscape has fundamentally shifted. I keep reading posts and stories about AI replacing sales teams, and it may be for more commodity-type sales, but it will be some time before it replaces Enterprise sales teams. Building relationships and trust is the foundation for an executive to take a risk on your product, especially when it is critical to their success. AI is not yet at that level, and as behavior changes with every key release, building trust in AI will be challenging for many years to come. But today, AI can be a powerful enablement tool for your team when leveraged correctly.
Your prospects no longer need salespeople for information; they need us for Insight. They have done their research. They expect their time to be an investment, not a discovery exercise. Does your presence and knowledge project confidence and inspire trust? Does the prospect view you as someone interested in helping their business, or just someone trying to close a deal? Impress them, and you could earn the opportunity to dig deeper. Disappoint them, and good luck recovering.
Two years ago, I was selling to a Fortune 100 Financial Services company. I understood their business needs, and we met them easily. We demonstrated that we could take a key manual process that typically took 7 weeks to complete, automate it and maintain full compliance, and complete the task within 10 minutes. The SVP told me his priorities for selecting any new vendor were: 1 – Company Stability; 2 – Relationship with the Vendor; 3 – Product Quality; 4 – Product Value to their business; and 5 – Total Solution cost. Before selling their IP, the company fired the sales team, and the remaining execs stepped in and offered the deal at an even greater discount. It never sold because the executive team didn’t understand what mattered to this buyer. The company had a high-level relationship with the stakeholders, but lacked the trust and credibility that I had built over several months. This is one of the main reasons why I believe AI won’t take over Enterprise Sales anytime soon.
So, how do you get it right?
The question for every CRO or VP of Sales isn’t whether their team is busy, but whether their activity is revenue-focused and drives predictable, scalable results. There’s plenty of money to be made, but following the same old tired formulas seldom works.
For leaders aiming to build next-generation teams that deliver zero surprises in the forecast, the approach must be recalibrated around three core pillars: Strategic Preparation, High-Agency Coaching, and Outcome-Focused Messaging (“context”). It is much more than cold calling for two hours a day or having 5-10 meetings per week. Those things matter, but they are just activities if you are not targeting the right companies and people, or if your team blows it once you have found them. This will be a significant cultural shift for many companies.
Strategic Preparation: From “Discovery” to “Insight”
When a prospect books a meeting, they are giving us one of their most precious assets: time. If we treat that time as a standard discovery call, we set negative expectations, which signals the lack of perspective (the ‘P’ in PIE) and perceived value. This isn’t theory—it’s the PIE framework (Perspective, Insight, Experience) I’ve used for years to sell large deals, turn around at-risk customers, and scale teams.
The C-Suite Mandate: Accelerate deal velocity by focusing on specific quantifiable impact for your prospects, and increase win rates by targeting identifiable business pain.
- Come with an Understanding of their Market, Changes, and Competition. Before the first call, we must show we’ve already invested time in understanding their operational constraints, competitive pressures, and budget priorities. The goal is to move the conversation immediately from the tired, “What keeps you up at night?” to “We have seen [problem] with companies in your industry. Is that something you have experienced or have concerns about?”
- Long Discovery Calls or Presentations Typically Won’t Work. Customers are fatigued by generic questions. Every interaction must be purpose-driven and meaningful. If the call runs longer than planned, it must be because the conversation has become mutually valuable, not because the seller was ill-prepared and just kept talking.
- Discussions Must Be Targeted to the Problems They Are Most Likely Experiencing. This is where we leverage Insight (the ‘I’ in PIE). Use your background and AI to hypothesize the top three pain points before you dial. Our role is to validate these points, quantify the impact, and then introduce a Shared Vision of Success (our solution) anchored by measurable business outcomes.
Player-Coach: Enhancing Team Capabilities, Not Just Motivating Activity
If you are a sales leader who only focuses on closing your team’s most challenging deals, you are creating a dependency, not a capability. A Player-Coach must be accountable for the team’s numbers and its health. That can be a big job.
The C-Suite Mandate: Drive organic growth by building repeatable processes and cultivating high-agency talent.
- Not a One-Size-Fits-All Proposition. True coaching is not a template. It requires a methodical but human approach to diagnostics. That takes time, effort, and a genuine desire to help people grow.
- Identify Skills Gaps and Tailor Efforts. Test skills, identify gaps, and create targeted efforts to build skills that address someone’s specific deficiencies. This personalized attention builds the high-performance culture and accountability required to sustain long-term success.
- Leverage the Team – Role Playing and Team Reviews. We must create a culture where knowledge sharing and feedback loops are the norm. Leverage team reviews and structured role-playing to sharpen execution. This is how we transform luck into a predictable process.
Give teams the latitude to adjust their messaging and test approaches. Adapt messaging to business trends, changes in the competitive landscape, and changing terminology. Then, have your team share their experiences and findings (good and bad) for review, feedback, and refinement. Structured agility helps your team maintain its competitive edge.
The Leadership Mandate: Context Over Content
We are past the AI hype cycle. The C-Suite doesn’t care about the tool; they care about the ROI and the risk of poor execution. As leaders, we cannot just hand our teams a login and say, “Go use AI.” That’s a recipe for chaos and a quick erosion of professional credibility. We must lead by example.
We need to teach our teams that AI generates content (not always accurate), but humans provide context. The two work hand-in-hand.
- Do use AI to deepen your understanding of the prospect’s industry so you can become a true consultant. Use the technology to gain understanding and market intelligence and tie it to your Experience (the ‘E’ in PIE) as preparation before any call.
- Don’t use AI to automate a thousand bad emails. Mass communication is cheap; individualized insight is priceless.
- Do use AI to research the one hundred prospects that actually matter. Focused efforts yield significantly better results.
- Don’t use AI to fake expertise. This can quickly kill credibility, as any good consultant will tell you.
The “Million Dollar Deal“ isn’t won by a bot. A human wins it by understanding the nuances of the prospect’s business, building trust, and navigating the internal structure and politics. AI is simply the tool that clears the path so you can do that work faster and with better data. AI is leverage, not a crutch.
Call to Action: Are You Building a Team or a Capability?
The next-generation sales leader understands that customer success is at the heart of everything we do. We win when they succeed. Your most valuable asset isn’t your pipeline—it’s the predictable capability of the individuals on your team. Consistently doing the right things is critical to success.
The challenge for every business leader today is this: Are you enabling your teams to sell like consultants, or are you still measuring them (and driving their behavior) on activity-based metrics? Focus on building intelligent and creative teams that deliver consistent results with zero surprises. It doesn’t happen overnight, but it is an investment in your future success.
Let’s discuss how we can implement the PIE framework and position your team to deliver scalable, organic growth.
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.
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?
Biometric Identity Theft
Recently, I have been researching the potential for fraud and identity theft using fingerprints from photos posted on social media. Last week, Amazon released its “Amazon One” Palm Scanner to pay for purchases. That announcement made me wonder: What are the potential implications for fraud and identity theft using biometric data taken from images?
Technology continues to improve rapidly, which often means, “Where there is the will, there’s a way.”
There are a surprising number of ways to accurately identify someone from a photo or video. Moreover, technology can copy fingerprints from social media photos taken up to three meters away. New technology has been proven effective at using 3D printing technology to create “fake fingerprints” that bypass many fingerprint scanners. Other factors may be at play (such as temperature), but that is speculation at this point.
Since fingerprints can be copied from photos taken up to three meters away, can a palm print be copied from a photo taken 5-10 meters away? That question led to an interesting but unscientific experiment where I took pictures of my hand, enlarged them, measured the distance between the ridges and furrows of my fingers and my palm, and compared the results. Spoiler – probably not (yet) – but likely not far away given the rapid advancement of AI.
Several areas showed similar distances for both my fingers and palm. But there were also areas on my palm where the average distance between “landmarks” was 3-5+ times greater. It turns out that a palm image is often segmented into 3-4 distinct regions for identification purposes, likely due to this type of variation. This link helped me understand the process.
This research led to an idea for a chip-based embedded filter for smart devices and laptops. It would obfuscate key biometric information when extracting the data for display without affecting the integrity of the original stored image. This functionality would automatically provide an additional layer of privacy and data protection. It would require optimized object detection capabilities (possibly R-CNN) that were highly efficient and run on a capable but low-energy processor like the ARM Cortex-M. Retraining and upgrades would be accomplished with firmware updates.
Edit 2020-10-13: This article on “Tiny ML” from Medium.com is the perfect tie-in to the abovementioned idea.
While Amazon’s technology is much newer and presumably at least partially based on their 2019 Patent Application (which does look impressive), it makes you wonder how susceptible these devices might be to fraud given reports of the scans occurring “almost instantaneously.” Speed is one aspect of successful large-scale commercial adoption, but from my perspective, the system’s accuracy and integrity matter far more.
Time will tell how robust and foolproof Amazon’s new technology really is. Given their reach, this could occur sooner rather than later. Ultimately, multiple forms of biometric scans (such as a full handprint with shape, palm, and fingerprints or a retina scan 2-3 minutes prior to the palm scan to maintain performance) may be required for enhanced security, especially with mobile devices.
Additional Resources:
- An in-depth NIH document that helps explain the process and issues.
- A Simplified Guide to Fingerprint Analysis
- This is a 2012 Fingerprint and Palmprint Recognition Approach Report from the European Journal of Scientific Research that has many images and very good explanations.
- “A Textural Approach to Palmprint Identification“
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.
Where Ideas, Experiences, and Lessons Learned Intersect 