innovation
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.
Acting Like a Startup
Over the years, I have heard comments like, “We operate like a startup,” “We act like a startup,” and “We are an overnight success that was 10 years in the making.” These statements are often euphemisms for “We are small and not growing as quickly as we would like.”
There are numerous estimates of startup and failure rates in their first few years. One of the best descriptions I have found is from Failory. Investopedia and LendingTree have similar but differing takes on the statistics and root causes. The net result is that failure is much more common than success, especially over time. So, “acting like a startup” is not necessarily good, even when true. Instead, you want to “act like a successful startup!“
Understanding the various causes and the data on success and failure should become significant inputs to business plans focused on long-term success. As a Founder, there are several points that I believe to be key to success:
- You have specific expertise that is in demand and would be valuable to an identifiable number of prospective customers. How would those customers use those skills, and how would they quantify the value? That understanding provides focus on what to sell and to whom.
- A detailed understanding of the market and key players is needed to hone in on a niche to succeed.
- Understand your strengths and weaknesses, then hire the most intelligent and ambitious people who complement your weaknesses and strengths.
- Understand how to reach those potential customers and the messaging you believe will compel them. Find a way to test and refine those assumptions as necessary – before you go all in. Remember, Marketing and Lead Generation are very important but also expensive, so it pays to get this right the first time.
- Have a plan for delivering on whatever you sell before you get your first sale. A startup needs to develop its track record of success, beginning with its first sale.
- Cash flow is king. It is far too easy to run out of money while looking at an excellent balance sheet because of receivables. Understand what matters and why.
- Founders need to understand the administrative side of a business – especially the financial, legal (especially contract law), insurance, and taxes. Find experts to validate your approach and fill in knowledge gaps.
- Consistency leads to repeatable success. You standardize, optimize, and automate everything possible. Wasted time and effort become wasted opportunities.
- Finally, there needs to be sufficient cash on hand to fund the time it takes to find and close your first deals, deliver and invoice the work, and then receive your first payments. That could easily be a 3-6 month period.
Those are the foundational items that are reasonably tangible. What is not as concrete but equally as important are:
- Having or developing the ability to spot trends early and identify gaps that could become opportunities for your business.
- An agile mindset allows you to pivot your offerings or approach in order to refine your business model and hone in on that successful niche. Don’t fall into a sunk-cost fallacy.
- Foster a sense of innovation within your business. Always look for opportunities to deliver a better product or service, improve the efficiency and effectiveness of your business, and create intellectual property (IP) that adds long-term value.
- Focus on being the best and building a brand that helps differentiate you from your competition.
- Become a Leader, not a Manager (lead people, manage processes). Create your vision of success, set expectations for each person and team, and help eliminate roadblocks to their success. Trust your team to help you grow and replace members quickly if it becomes clear they are not a good fit.
Steve Jobs once said, “It doesn’t make sense to hire smart people and then tell them what to do; we hire smart people so they can tell us what to do.” To grow, you need to choose the best people you can afford, guide them, and trust them to perform in the best interest of your company. That is true in any size organization.
Winning is hard, so focus on the journey. Making your customers’ lives easier and allowing your employees to be creative while doing something they are proud of will lead you to your destination. But when things start going well, don’t sit back and convince yourself you are successful. Instead, feel the pressure of the competition – they are out there – and continue to focus on ways to improve and grow.
Success means different things to different people, but longevity, growth, profitability, and some form of contributing to the greater good should be dimensions of success for any vision.
Innovations “Iron Triangle”
The concept of an Iron Triangle is that each side of the triangle represents an item constrained by the other two sides. In Project Management, this is often referred to as a triple constraint. This identifies the fundamental relationships (such as Time, Cost, and Scope in Project Management) without addressing related aspects such as Risk and Quality. It provides a simple understanding of both requirements and tradeoffs.
Yesterday I spoke with Dave Mosby, an impressive person with an equally impressive background. He related Innovation to Fire, noting that in order to create fire, you need fuel, oxygen, and heat. He added that they must be in the right combination to achieve the desired flame. What a brilliant analogy.
Dave stated that for Corporate Innovation to succeed, you need the proper balance of Innovation, Capital, and Entrepreneurship. I found this enlightening because his description substituted “entrepreneurship” for “culture” in my mental model. While the difference is subtle, I found it to be important.
As noted above, simplified frameworks do not provide a complete understanding. But they help understand and plan around the foundational items required for success. Mapping this to past experiences, I gained a better understanding of things that did not move forward as desired and what I could have done differently to be more effective.
One idea was to create a fault-tolerant database using Red Hat’s JBoss middleware. We had a Services partner willing to create a working prototype, tune it for performance, document the system requirements and configuration, and package it for easy deployment. They wanted $10K to cover their costs.
I didn’t have a budget at the time, so I created a purchase request supported by a logical justification. It modeled potential revenue increases for database subscriptions based on the need for a failover installation and growth from potential expanded use cases. This was a slam dunk!
In my mind, this was simple as it was “only $10K,” and I had funded many similar efforts when I had my own company. But that’s the rub. I viewed these efforts as investments in understanding, lessons learned, and growth. Not every investment had a direct payoff, but nearly each had an indirect payoff for my company. It was an entrepreneurial mindset that accepted risk as something required for rewards and success. I now see, many years later, how reframing my proposal as a way to foster innovation and entrepreneurship within the company could have been far more effective, since training is usually a budgeted item that is much easier to justify.
It is never too late to gain new insights and lessons learned. A slightly different perspective on an important topic provided the understanding that should help position projects for future success. This came from a discussion with an interesting person who has “been there, done that” many times.
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“
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