Artificial intelligence is creating revolutionary changes for businesses worldwide. Our experts project that AI will impact more than 40 percent of tech jobs, and those changes are already popping up in many areas.
Revenue generation is a primary source of workforce costs. Historically, companies estimated their revenues using the simple math of quota times sales headcount. Therefore, to significantly increase revenue, adding headcount was required to access more opportunities.
In a poll we conducted in May, 73% of tech professionals indicated that their company’s sales headcount will remain stagnant or decrease in the foreseeable future. However, those same companies are not reducing their anticipated revenue. So how are they doing it?
First, we need to understand that the purchasing process is changing.
The Internet has undeniably transformed consumer purchasing habits. Purchasing goods and services solely from physical locations is becoming obsolete for individuals under thirty.
Although more complex and often more costly, prognosticators have long expected a similar transition in B2B buying.
That remarkable shift is underway, mirroring the transformation witnessed in B2C buying behavior. Integrating advanced technologies has revolutionized complex sales processes, enabling technology to skillfully manage tasks traditionally handled by human professionals.
Stalwarts have long argued that the B2B buying process is too complex to reduce the human element. How could a machine understand the intricacies and differences between complex businesses to help them make the right purchasing decisions? Moreover, how can technology understand the challenges that human beings are facing and identify solutions?
The reality is that all of these factors are data points, and AI feeds on data.
AI presents a significant opportunity in B2B sales. Web-based AI technology enables tools to model a company’s structure and functions utilizing publicly available information. This model can be further refined based on prospect inputs. The technology then processes numerous business-related variables, compares them to existing solutions’ strengths and weaknesses, and offers data-driven recommendations. This eliminates the need for humans to spend countless hours comparing products and analyzing their organizations’ needs.
Additionally, the influence of humans describing and promoting features becomes irrelevant, as data now identifies the most suitable and appropriate solution for the business. This portion of the sales process becomes streamlined, data-driven, and requires significantly fewer person-hours.
We see these changes are already taking place. Consider this snippet from a recent round of layoffs by technology giant, Google:
In December, we heard that job cuts might be coming for Google’s ad sales division, and it’s here. Business Insider reports Google is laying off “hundreds of employees” from the ad sales team. The cuts are mostly in the “Large Customer Sales” (LCS) team, which serves the company’s biggest advertising clients.
We expected this. The Information detailed that layoffs would come to Google’s Ad division this month. That report said that many of those are being laid off or reassigned because AI is replacing them. Google has been packing Google Ads—its most important product—with tons of generative AI features lately. One is a natural-language chatbot that helps people navigate the large selection of ad products; another is a system that can just make ad assets like images and text on its own based on a budget and goals given by the ad purchaser. Google’s generative AI ad system is part of a product called “Performance Max,” which works by autonomously remixing and tweaking your ads using the click-through rate as an instant feedback system. Google used to have humans do sales guidance for its products, create art assets, and decide on text and layouts, but now AI can do it a thousand times a second.
Generating revenue goes beyond the initial sale. Successful B2B sales functions prioritize eliminating churn through effective customer success initiatives. Widespread adoption and utilization are essential to enhancing product loyalty. If a product falls short of delivering the expected value, it will likely suffer budget cuts.
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AI solutions can now monitor, analyze, and compare customer engagement with a product. This technology can also assess the likelihood of a customer renewing or increasing their purchase. This provides a significant opportunity for customer success teams to address potential churn risks and foster long-term customer relationships proactively.
Our team’s research indicates that tech enterprises are heavily investing in AI for success plans. This emphasizes the importance of using AI to personalize and guide customer outcomes. This technology enables automated and tracked human-like interactions.
Moreover, robust onboarding and adoption efforts are crucial for building strong customer relationships. Significant investments in AI-powered onboarding and adoption frameworks aim to optimize customer time-to-value and product stickiness. These tools help clients realize product benefits quickly, leading to more successful adoption.
However, churn remains a significant concern for organizations. AI technology is leveraged for low-touch, digital engagement, and customer success scoring. This focus on AI helps proactively identify at-risk customers. By highlighting potential benefits and providing engagement guidance, technology can combat churn. Increased adoption and engagement are key revenue drivers.
As we look across the market, revenue-generating positions are decreasing, but revenue expectations are rising. Coupled with where we see companies investing, we can see that AI is recreating how tech companies do business. The predominant question on the horizon for professionals should be, “How can I offer new and greater value to my company and customers by leveraging these new functions?”
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