Artificial Intelligence for B2B Sales: Impacts to Sales Productivity, Efficiency and Jobs

Davis Giedt: Welcome, everyone. Davis Giedt here, director of the analytics and research practice at Alexander Group. Today, we’re going to be talking about AI for sales teams, and particularly some early things we’re seeing around how AI is augmenting productivity, time and the profitability of sales organizations. In our research over the past several months, it’s become pretty clear that AI has the potential to change the nature of the sales organization.

And we’re pretty hard at work unraveling this transition and what it means for commercial jobs and the way that B2B organizations go-to-market. Overall, we’re seeing that companies will need to anchor their AI investment decisions to their commercial growth strategies. For example, using AI to increase cross and upselling using AI to improve inside sales or add e-commerce to their go-to-market model.

And in today’s podcast and video, we’ll talk about the current state of how commercial organizations are using AI. And we’ll also offer some predictions on how we think this will develop over the course of the next year or so.

Today with me, I have two guests that will provide their thoughts. Mitch Edwards, principal at Alexander group, will bring his deep client experience with go-to-market transformations and go-to-market work. And then Armin Kakas, founder and managing partner of Revology Analytics, will bring a more technical perspective from his many years developing and implementing analytical and AI tools for commercial organizations.

So happy to have you both with us here today.

Armin Kakas: Thanks for having us.

Mitch Edwards: Good to be here.

Davis Giedt: So the first topic here is about AI’s impact to sales productivity and sales time. And this is a common one. We’re hearing a lot of folks talk about the potential productivity uplift and time savings capabilities of AI, particularly for salespeople.

A recent survey from HubSpot found that AI and automation tools are already saving sales teams over two hours a day in meeting, scheduling, note taking, outreach creation and editing, and also CRM entry. Some of the other top categories of AI automation tools that sellers are using to augment their manual tasks are data entry, note taking, etc.

They’re also using sales forecasting, lead scoring, pipeline analysis, so AI and augmented tools for that. And we’re seeing a lot of popularity of that. It’s growing in popularity. But I guess the question to Mitch initially, and then we’ll circle back to Armin.

What’s your take? Is this real? Do you feel like organizations are actually saving their sellers two hours per day or more? And then also, what do you expect sellers to do with that sort of additional time savings if it is true?

Mitch Edwards: Yeah, absolutely right. If we think of two hours a day, if we just take one example of something that might be logging what happens after a sales call, right?

Typically, in the past, you would have had to go through the notes. What are the action items? What are the next steps? All that’s got to be loaded into the CRM as well, right? If we talk about the time it takes to do that throughout the day for a number of sales calls, two hours seems a very reasonable estimate for saving some of that time.

The other point to probably call out is that maybe two hours for the organizations that have those types of support in place for their sales teams. I think some of that HubSpot data, it was only one in three sellers that are actually using those tools on a daily basis.

We’ve also recently launched our Sales Pulse survey, which is from a lot of revenue leaders across different organizations. And what we found initially was about 20% of companies actually using some form of AI within their organization today. But about a further third of those are in the process of rolling something out this year. So we’d expect that number to creep up pretty soon here as well. I think in terms of what should someone be doing with that time that they’re getting back, right?

The key for us is the whole idea of engaged selling time And so we want to have more higher quality interactions with our clients in front of them rather than offline In tools working through things that might not be that high value sales work.

The other thing I’d point out is that when we talk about who’s using these tools, even if you’re an organization today that’s not using any of these AI tools, I would imagine your very best sellers are already using those.

If me as a seller, I can go and get a subscription to something like Anthropic that’s going to enable me to do my job faster. If I believe in myself, why would I not spend $20 to try and get an extra 10% quota achievement in a given month?

So I think the key is, yeah, absolutely, time savings are here to be had. And really that time should be spent reinvesting back in front of other customers to hopefully close more sales.

Davis Giedt: Yeah, that’s really helpful, Mitch. And, diving deeper into one of the specific use cases around AI for data driven insights. So thinking about how AI can augment forecasting lead scoring, Armin, tell us a little bit about how have you seen commercial organizations use AI for forecasting pipeline analysis, et cetera. And how does this sort of fit into this idea of saving salespeople time on a daily or weekly basis?

Armin Kakas: For sure. Yeah, I’m happy to share my opinion, Davis.

I have lots on this topic. First of all, like Mitch mentioned, AI, machine learning, all these enabled sort of solutions will absolutely transform commercial insights. And we’ve seen that firsthand in a variety of companies across all different maturity levels. There’s really no difference in impact to the organization, whether it’s a sales org that uses turnkey solutions, or you have simpler tailor-made homegrown capabilities, the trick is always last mile adoption, right?

So is your sales team actively leveraging the tools that are built for them to be more productive or more impactful? And if the answer is yes, you end up driving more meaningful increases in sales productivity and operating profit. And we typically see that in the first 12 months of building out these capabilities. And of course, if the adoption is sparse or generally lacking outside of your top sales performer, incremental effects will be minimal, right? Or non-existent.

And so some of the specific use cases that we’ve seen worked really well in terms of AI-enabled sales insights. I’ll try to list these in order of complexity.

We talked about more surgical lead scoring. But it’s more surgical and more advanced and that it doesn’t just leverage your customer profiles, but also conversation history, employee profiles of that company, various 3rd party data. And these obviously help the sales team focus their efforts on the most promising leads, right? And actually optimizes their time and grows revenues.

You asked about sales forecasting and so real time sales forecasting that doesn’t just use transactional data, but also things like customer inventory forecasts, natural language processing of customer communications, competitive trends, right? This is incredibly powerful. And so these capabilities go beyond just sales forecasting.

Generative AI can also do trigger based alert systems. So if you have a key customer, Davis, that needs more sales, it’s about to miss the forecast for the next month. It could be an automated solution that personalizes either an outreach to you as a salesperson or an outreach to the customer. And these are capabilities that are available today.

You asked about insights. So when I talk about insights that are AI enabled, I talk about prescriptive automated insights. And so what I mean by that is sales team members are actually receiving event based recommendations on which customers to reach out to. When? How? Do you text the customer? Do you email? What sort of creative to use, right? What has the highest propensity to actually reach that customer? And why is the customer about to churn in the next 30, 60, 90 days? And why is that? Do they need a product upsell or a cross-sell? And including what to say, right?

So hyper personalized communication, hyper personalized outreach, offering promotions and credit limit increases and things like that. And think about these capabilities as a proactive sales outreach that automates this idea of what is the next best action that I should take with my customers. So it’s a lot more than just task automation.

And the last thing that I’ll touch base on is sales knowledge graphs. It’s not a new concept. Knowledge graphs have been around for decades and a couple of centuries, really, but large language models actually augment knowledge graphs for the sales team to enable anything from dynamic sales and marketing insights to customer training optimization to automatically recommending product cross-sells and things like that.

And these are all amazing capabilities among big caveat that I’ll mention here, guys. It’s really the quality and the relevance of the data that’s being used. That’s probably one of the most important things as we think about AI enabled capabilities for commercial organizations.

It’s probably the number one thing outside of strategic intent and CEO and board support to make these things real.

Davis Giedt: Got it. Okay. As a sort of follow up question to wrap up this section. Just a question to both of you. So how do you expect AI to impact sales productivity throughout 2024 heading into 2025?

And then where do you think commercial organizations will be getting the most value from AI in the next one to two years?

Mitch Edwards: Yeah, so I think in terms of what are the changes going to be, one we talked about a little bit in the last section which is it’s going to become more prevalent. Those that have not yet adopted those tools are likely going to go down that path of trying them out.

I think the other part is we’re going to see more of a maturation in the tools as well. A lot of this. Particularly around generative AI, we’ve heard a lot about the large language models. But they are just the model where you’ve probably heard the term prompt engineering, right? But that takes one person to work out, what is a good prompt that I can use to get the output that I want? And so the maturation that’ll take place with a lot of these tools is, how do we scale that? So one individual doesn’t need to know, what is that right prompt to plug in? How is that pre-programmed so that you can click a button saying I’m reaching out to a marketing executive? What’s the relevant copy at this organization that’s going to resonate with them as part of that sales process?

So I think that’s a really big part of this too, which is the tools will become more mature and much more scalable as we see them roll out.

Armin Kakas: Yeah, I totally agree. You think about AI and generative AI, right? It’s essentially the same sort of foundational change that we had with like electricity back in the day. So it’s only going to become more and more relevant commercial organizations. I think more and more companies, like Mitch said, will actually become comfortable with the idea of integrated benefit or processes.

What I would say, guys, and I’ve seen this with data science, and perhaps I’ll expand on this a little bit, but maybe over the next couple of years, depending on industry, one to five years, the biggest value will be coming from things like task automation and enhanced decision making. So it’s all about augmenting the human decision. Like sales teams are still bogged down with just a host of administrative tasks that’s preventing them to actually fulfill their potential.

And so the other part, which is decision making I’m talking about again, capabilities that help the sales team be much more proactive and much more insights driven about their sales approach and help them actually anticipate market trends and competitor reactions and customer behavior.

And so we’re talking about automated capabilities that actually do either some part of the salesperson’s job or some aspect of their job or significantly augments their efforts. And so I think that sort of short term, long term, we’re talking about next 5 to 10 years. I think it’s going to become more and more prevalent and the efforts are going to be a lot more impactful.

As you think about AI right in the commercial setting, right? Think about any decision-making process that relies on a set of facts or inputs, a complex set of rules and creativity. And so all this can actually be fully automated, and I expect AI and derivations of it they have a much more central role, not just in the tactical pieces of the sales process, but also the strategic piece.

And I’m very excited about these efforts, and I think maybe the last piece that I mentioned here. There’s also the stark contrast with the majority of the commercial organizations, the capabilities that we’ve seen, at least in the kind of mid-market space, upper end of mid-market, 100,000,000 to about a billion dollars.

About 50 to 70% of those companies are still struggling with kind of below average sales and marketing enabled capabilities today. All right, so half the companies in the survey that we had of 150 commercial executives, they still can’t quantify the impact of the marketing investments.

60-70% of them don’t have tools for their Salesforce that is proactive about telling them which customers to go after to better manage churn or do upsell or things like that, or they don’t have any customer facing analytics solution.

So I think for a lot of companies this is really exciting. These capabilities are going to be game changing, but also I think for a lot of companies, there’s a tremendous opportunity to work on these sort of foundational sales and marketing enablement analytics first.

Mitch Edwards: Davis, you also asked before about where in the organization we might see some of these benefits. And I think it’s really where we see scale in an organization. Going back to that Sales Pulse survey that we had where people have responded where we might see the biggest productivity uplifts. A lot of the respondents thought that would be around teams where there is more scale, things like custom success, things like revenue operations, things like inside sales.

So I think that’s where we’ll see a lot of the benefit to this is really around where we might have more of that scaled motion where like Armin mentioned, there is data available to help automate that process.

Armin Kakas: A hundred percent.

Davis Giedt: Yeah. And we’ve seen in survey data for years that those are three of the top five roles that organizations are using to drive leverage and growth within their sales organizations as they move some way shape or form away from a traditional field sales model.

So switching gears here talking about AI’s impact to profitability just a stat to anchor us: the world economic forum took place recently in the last couple weeks and AI was a major topic of discussion. Ahead of the forum, there was a survey of about 5000 global chief executives on the topic of generative AI in particular. And one of the topics was impact to profitability. There was essentially a 50/50 split on leaders in terms of whether they expect generative AI to make a large impact to profitability in the next 12 months versus little to no impact. So very split there.

First question is to Armin and what’s your sense on how this impacts market sales and services, this notion of profitability and generative AI impacting profitability.

What types of commercial organizations will have the greatest positive uplift and profitability from leveraging generative AI?

Armin Kakas: Yeah, so I think before I go deeper, Davis, maybe the one piece that’s important for me to qualify for those that are listening is this fundamental difference between machine learning and AI especially generative AI that’s been the latest hype.

I do want to talk about the hype cycle. So if you recall, maybe maybe a decade ago, there was this tremendous executive hype about data science, right? And almost like all the fortune 500 companies, majority of the fortune 1000 rushing to build these data science COEs. And it was a very innovative function, right? It was very much in its hype phase with business leaders, depending on the industry for about five to 10 years.

Before, those folks realize that the payoff was not nearly as drastic as they initially hoped for, right? And so I think those same CEOs and CXOs slowly started to realize it’s really not about the technology, but about the commercial adoption that drives actual measurable results and solves real problems.

And so I think Gardner coined this term about kind of innovative technologies that go through these cycles. We had the sort of the hype cycle, peak of inflated expectations, followed by this kind of period of disillusionment after the impact was not there. And you had this slope of enlightenment, right?

You had these business leaders, they’re starting to come up with actually, better, more focused ways to monetize data inside their companies, but not the high level grandiose types of initiatives. So I think in a similar way, we’re very much seeing this early hype cycle with AI. And I think it’s probably reflective of the sort of this 50/50 split by the executives on the survey and based on learnings that they had with data science and machine learning initially.

And the second piece, maybe I’ll touch base and I’ll talk about organizations that can leverage it. But, in terms of the fundamental difference between machine learning and AI. So think about machine learning that’s a predictive enabler of the sales and the commercial function, right? So it’s all about analyzing historical transactions and customer data for lead scoring, sales forecasting, all the things that we talked about.

What generated AI does today it actually augments that by offering human like interactions. Content creation, hyper personalized communications, very naturally sounding sales scripts. And we talked about hyper personalizing, advertisement, promotions, pricing, offerings and things like that. So it’s really an extension of machine learning that sort of applies artificial creativity at scale to machine learning.

And Mitch talked about this earlier. He was spot on in terms of the types of commercial organizations that can benefit the most. It really come down to organizations that are data rich and have lots of customer interactions. Think of e-commerce retailers have a ton of customer data. They can use generative AI to do hyper personalized pricing, promotional marketing efforts at scale, right? All these things that are increasing conversions, loyalty and profitability. B2B company is the same thing. They can actually leverage generative AI for lead scoring, market analysis, and so forth. On the other hand, you do have organizations where the sales cycle is longer. You have products that are high ticket. You have sales relationships that require much more of a human touch. So medical devices, aspects of distribution, consumer product goods, you’re dealing with a retail buyer.

They’re still going to be able to leverage generative AI, but I think it’s going to be mostly outside of their commercial organizations. So things like supply chain, et cetera.

Davis Giedt: Yeah, and Mitch maybe you can layer on some insights from some of your conversations with the clients. What are some steps you’re seeing that organizations can take this year to improve their profitability using generative AI in particular?

Mitch Edwards: Yeah, absolutely. So I think it’s really understanding where the biggest pain points within your organization today. Is it something that’s more upstream around customer segmentation? Are we targeting the right people? Is it that we need a message to them in a more effective way? Are there challenges with your sales force or really the overall efficiency that you have within your sales model?

So I think number one is really understanding what are those pain points today and then start an initiative around introducing generative AI to start tackling some of those. I would say begin that process if you’re not part of that 20-50% that’s already in the process today. Start joining that group and find something to do.

But I would begin that process with intentions. Understand it’s to solve a pain point that you have today, but also set a goal for what you expect to see is an uplift. In the opening, we talked about saving a seller two hours a day. What is it that you really want that seller to be doing with that extra two hours now? Being really prescriptive in here’s the change that we expect and here’s where we expect to leverage that time is really important. So I would say find that initiative and start working towards it to drive some profitability within your organization.

Davis Giedt: All right, so our final topic here, not to end on a downer of sorts, but we’re hearing some noise in the market about major tech companies slashing their sales forces in favor of increased investment in AI.

For example, Google is cutting hundreds of jobs from its advertising sales unit in a restructuring that will favor AI adoption. It was also a topic at the recently mentioned World Economic Forum. The preform survey found that a quarter of the execs expected generative AI to lead to headcount reductions. This goes beyond just sales, but headcount reductions of 5% or more in 2024.

So I guess this is a question first to Mitch. Do you think this will become a major theme in 2024? Particularly as it relates to rebalancing the sales force or will companies be taking a completely different route?

Mitch Edwards: Yeah. So this is one of those scary questions when it comes to AI, right? Where people generally want to avoid it. I think a lot of the positioning around AI is this is really a compliment to an existing job rather than a replacement. But if we go back to some of the things we talked about initially, if we are seeing productivity gains of 20, 30% there’s only so much more you can do when you’ve now got your whole sales team doing an extra 30%.

That comes into a real rebalancing question. Where it might not just be, we’re going to reinvest that into further growth, there’s a rebalancing, or in reality, people might lose some jobs as part of this. And so what I think we’ll see going forward is that there’s going to be a real catalyst from AI around the area of sales transformation where this is now your opportunity to reset and look at based on AI and incorporating that into our organization, has AI changed where we need to play with segmentation, are our AI enabled sales plays more effective for a certain subset of customers, and we need to rethink how we define our territories and a segmentation model to better target and narrow in on where that’s going to have benefit.

Do we have the right roles? That are now there to support that AI enabled play. Do we have the right talent in those roles to really be supportive of the model that we’re building? And so what I think we’ll see from an Alexander Group perspective as well is we’ll be doing a lot more sales transformation work really around addressing some of those areas that have come up because AI being this catalyst for change.

Davis Giedt: That makes sense. Armin, what’s your sense on this topic?

Armin Kakas: Yeah, I totally agree with Mitch and maybe I’ll be the downer to end this conversation, Davis.

I’ve read the Google article. I think it’s an interesting signal, right? And let’s be honest, guys, most companies, in a capitalist economy, they’re actually not known for their altruism. If the trade off is between meeting or exceeding your shareholder expectations versus taking care of employees, we typically know what happens. And I don’t anticipate that to change until companies are actually forced to embrace more of a total stakeholder type of you.

So in that sense, I’m certainly very encouraged. I’m also perhaps a bit pessimistic. I think the impact on the workforce both in terms of size and structural will depend on industry. So we talked about industries that have high levels of routine tasks. There are certain elements of inside sales, customer success. I think those will likely see some of the more adverse impacts. Over the coming years, and ideally to Mitch’s earlier point, it will be much more about workforce rebalancing. So how do we shift people across teams and functions beyond sales and followed by a much more judicious headcount planning, which has been an issue, particularly for startups and smaller companies and things like that.

But I think, industries that require this higher degree of human interaction, more complex set of sales and decision-making processes, it’s going to be a lot harder to codify for now. And AI is going to be a lot more sort of a stronger compliment to the sales teams.

And I think my suggestion for all commercial organizations is learn how to strike this very healthy balance between leveraging AI for your productivity improvements and actually maintaining and embracing a very highly skilled, intellectually curious sales force that can leverage AI and interpret the outputs and interpret the insights. I think it’s going to be really key to growing profitably and getting a competitive advantage because everybody is trying to figure out how to leverage AI.

And lastly, guys, I’ll just say, outside of sales any job that consists of routine manual role tasks, the adverse impacts over the coming years will be heavy. And I think we’ve seen this in parts of physical retail. We’ve seen this in warehousing, customer service. And so AI will definitely accelerate the depletion of some of those opportunities. And so I think that’s why, whenever I talk to folks in companies, especially younger people that I mentor, I always encourage them to spend a considerable amount of time, free time, outside of school or outside of their jobs, nights and weekends, figure out how AI will play a role in your career and your job, and really becoming expert at leveraging it. That’s going to be really important.

Davis Giedt: So, thanks to Mitch and Armin. We’ve learned a ton about productivity and how I can impact sales productivity and sales jobs as well as talking about the overall profitability of commercial organizations and how that impacts profitability at the sort of higher level, the overall business level.

And of course, we’ve talked about certain jobs that may be more heavily impacted by AI and the potential to augment and transform sales organizations as a result of AI over the next year or two.

So we’re just scratching the surface on this topic. It’s the beginning of an entire year of research on this topic for Alexander group. We will be putting out additional podcasts and videos on how AI is impacting other elements of the commercial model, including marketing service, customer success and rev ops to look out for those. We’re also conducting research on this throughout the year. We’ll be putting out additional content on the topic.

And thanks everybody for watching and listening.