machine learning

Leading Next-Generation Sales Teams: The Mandate for Predictable Revenue

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An image of a hand pointing to an AI generated dashboard on a computer screen.
Image created by Nano Banana

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.

Six Ways AI Can Become a Sales Management Enhancer

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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

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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.

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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:

  1. Things that are accidental but predictable because of human nature.
  2. Things that are predictable based on other events and interactions.
  3. 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

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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.

Blockchain, Data Governance, and Smart Contracts in a Post-COVID-19 World

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The last few months have been very disruptive to nearly everyone across the globe. There are business challenges galore, such as managing large remote workforces – many of whom are new to working remotely and managing risk while attempting to conduct “business as usual.” Unfortunately, most businesses’ systems, processes, and internal controls were not designed for this “new normal.”

While there have been many predictions around Blockchain for the past few years, it is still not widely adopted. We are beginning to see an uptick in adopting Supply Chain Management Systems for reasons that include traceability of items – especially food and drugs. However, large-scale adoption has been elusive to date.

Image of globe with network of connected dots in the space above it.

I believe we will soon see major shifts in mindset, investment, and effort toward modern digital technology driven by Data Governance and Risk Management. I also believe that this will lead to these technologies becoming easier to use via new platforms and integration tools, which will lead to faster adoption by SMBs and other non-enterprise organizations, and that will lead to the greater need for DevOps, Monitoring, and Automation solutions as a way to maintain control of a more agile environment.

Here are a few predictions:

  1. New wearable technology supporting Medical IoT will be developed to help provide an early warning system for disease and future pandemics. That will fuel innovations across industries, including Biotech and Pharma.
    • Blockchain can provide data privacy, ownership, and provenance to ensure the data’s veracity.
    • New legislation will be created to protect medical providers and other users of that data from being held liable for missing information or trends that could have saved lives or avoided other negative outcomes.
    • In the meantime, Hospitals, Insurance Providers, and others will do everything possible to mitigate the risk of using Medical IoT data, which could include Smart Contracts to ensure compliance (assuming a benefit is provided to the data providers).
    • Platforms may be created to offer individuals control over their own data, how it is used and by whom, ownership of that data, and payment for the use of that data. I wrote about this in 2013.
  2. Data Governance will be taken more seriously by every business. Today, companies talk about Data Privacy, Data Security, or Data Consistency, but few have a strategic end-to-end systematic approach to managing and protecting their data and their company.
    • Comprehensive Data Governance will become a driving and gating force as organizations modernize and grow. Even before the pandemic, there were growing needs due to new data privacy laws and concerns around areas such as the data used for Machine Learning.
    • In a business environment where more systems are distributed, the risk of data breaches and Cybercrime Increases. That must be addressed as a foundational component of any new system or platform.
    • One or two Data Integration Companies will emerge as undisputed industry leaders because of their capabilities in MDM, Data Provenance and Traceability, and Data Access (an area typically managed by application systems).
    • New standardized APIs akin to HL7 FHIR will be created to support a variety of industries as well as interoperability between systems and industries. Frictionless integration of key systems becomes even more important than it is today.
  3. Anything that can be maintained and managed in a secure and flexible distributed digital environment will be implemented to allow companies to quickly pivot and adapt to new challenges and opportunities on a global scale.
    • Smart Contracts and Digital Currency Payment Processing Systems will likely be core components of those systems.
    • This will also foster the growth of next-generation Business Ecosystems and more dynamic collaborations.
    • Ongoing compliance monitoring, internal and external, will likely become a priority (“trust but verify”).

All in all, this is exciting from a business and technology perspective. Most companies must review and adjust their strategies and tactics to embrace these concepts and adapt to the coming New Normal.

The steps we take today will shape what we see and do in the coming decade, so it is important to get this right quickly, knowing that whatever is implemented today will live, evolve, and hopefully improve over time. Don’t wait for perfection, as the risks are too high.