This article was derived from a discussion between Matthew Darrow, Vivun CEO, and Joseph Miller, PhD, Chief Data Scientist, about the implications of advancements in AI for selling professions. You can watch the full interview here.

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Agentic AI: A New Wave of Innovation

Sales Engineering work remains critical for every company's B2B revenue stream, yet there has been little discussion envisioning the impact of Agentic AI on technical selling professions.

This article explores three pivotal trends that make this AI wave different from past advancements, and how hybrid roles, like Sales Engineering, can lean into the labor disruption and strategically embrace innovation.

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Trend 1: The Convergence of Top-Down and Bottom-Up AI

For decades, AI research has toggled between two primary approaches:

• Top-Down: Relying on semantic reasoning, predefined rules and logic, such as traditional expert systems.
• Bottom-Up: Learning patterns and behaviors from vast amounts of data, exemplified by neural networks and Large Language Models (LLMs).

Historically, each approach faced limitations. Early AI systems lacked flexibility, while the bottom-up models of the 1990s and early 2000s were constrained by insufficient compute power and data. However, the advent of the internet, improved computational resources, and breakthroughs in neural networks spurned innovation through improved knowledge representation (knowledge graphs) and LLMs.

The real revolution today is the integration of these two paradigms. Modern LLMs combine the structured, domain-specific knowledge from top-down approaches with the adaptability and scale of bottom-up methods.

This fusion allows businesses to represent and engage with complex knowledge structures (e.g., knowledge graphs) via natural language. It’s now possible to leverage natural language interfaces for accessing deep, domain-specific insights, creating unprecedented opportunities across industries.

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Labor Implications:

This convergence enables AI systems to handle complex, high-value tasks previously reliant on human expertise. AI assistants and agents will be able to perform highly skilled work, comparable to specialized human outputs. Thus, a digital workforce will emerge alongside the human one.

Industries that require nuanced decision-making, such as legal work, software engineering, and technical sales, will see a shift toward augmented workflows, reducing dependency on human intermediaries.

At the same time, some business and sales processes will rely more heavily on strategic human intervention points, and certain “soft skills” will be critical to maintaining a competitive advantage.

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Trend 2: Exponential Acceleration in AI Capabilities

AI development is advancing at a staggering pace, challenging human perceptions of progress. Joe Miller aptly described this phenomenon as humans being "linear thinkers" in an era of exponential change. Models like GPT-4 have already achieved milestones in a few months that were once considered years away.

Things that we expect to happen in a year or two years have instead happened in 6 month cycles, which defies human reasoning abilities.

This rapid innovation is creating shorter cycles for technological adoption and integration, leaving businesses little time to adjust. While a year ago, limitations like basic math errors were common in AI, today’s models have surpassed those barriers, offering practical utility across diverse applications. This exponential curve also highlights how quickly yesterday’s revolutionary technology can become today’s standard.

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Labor Implications:

Leaders cannot afford to underestimate what exponential change looks and feels like in terms of AI advancement. Throw the traditional timelines away.

• The acceleration in AI capabilities compresses the timeline for labor market disruptions. Highly skilled roles previously deemed "safe" are now vulnerable to disruption if confined to traditional boundaries and processes. This does not mean AI will take all skilled jobs; rather, people in these roles will have to learn to leverage the technology to create business value in new ways.
• Organizations must prioritize agility to ensure their workforce remains competitive. This not only includes retraining but also requires a new strategic approach to leverage digital alongside human workforces. For example processes must be redesigned to optimize human vs. AI touchpoints (Gartner Report on Agentic AI).

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Trend 3: General Labor Disruption Beyond Sector Boundaries

Unlike past technological disruptions, which predominantly impacted specific sectors (e.g., industrial automation in manufacturing), AI has a broader and more profound reach. As Miller points out, Generative AI is not just disrupting individual jobs—it’s challenging the very nature of labor.

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The Shift from Sector-Based to General Disruption

AI’s ability to perform knowledge work, such as drafting legal documents or writing software code, diminishes the need for labor across industries. Unlike past disruptions, where displaced workers could transition to adjacent roles (Cobb-Douglas Production Function), the substitution of human labor with AI in knowledge-intensive tasks creates a scenario where certain jobs are eventually permanently eliminated without equivalent replacements:

“You're never going to have the marginal cost of an AI machine doing that same knowledge work from a human.”

So, while some retaining will occur, the labor disruption cannot be entirely addressed by retraining.

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Labor Implications:

• Redefining Productivity: While AI enables higher productivity, not all workers will benefit equally. The top performers will dominate value creation, leaving less room for average performers, thereby redefining organizational hierarchies. Moreover, increasing the productivity of employees does not increase demand or market size and thus might not translate into equivalent value for the company.
• Hybrid Roles at Risk: Specialized positions requiring a mix of technical and interpersonal skills, such as Sales Engineering, are particularly vulnerable to this kind of disruption, unless they are recognized as centers of value generation (i.e. not just demo jockeys).

Opportunities for Sales Engineering

At a time when businesses and customers are clamoring to scale technical expertise and capacity, this new AI wave represents a critical opportunity for Sales Engineering teams and the broader Revenue org.

Imagine what’s possible if you codify SE domain expertise, product knowledge, industry/market knowledge, etc. in a way that is accessible across the GTM team and to customers. Agentic AI makes this possible.

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As a technical seller, how can you stay ahead of the game and be a driver of that transformation, not a casualty of it?

First, stay curious and experiment with new tools. Now, as always, fluency in new technology gives you leverage, and AI is just another new leverage point to make your workflows more efficient and effective.

Second, focus on mastering skills beyond technical expertise – like storytelling, asserting political influence, objection handling, champion-building, etc. If SEs are seen just as technical SMEs and demo-doers, AI poses a threat.

But, if you delegate the tactical work to AI (research, solution design, demo/deck building, etc.), you can shift focus to more strategic areas of influence:

• Test new ways to break into untapped markets and industries
• Experiment with your product’s use cases to deliver new value
• Participate in discussions around improving product direction and sales process

This is the type of work that many Sales Engineers and SE leaders want to be doing in the first place, and AI can help them get there faster if they embrace it.

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Conclusion

This wave of AI innovation marks a turning point in technological and economic history, poised to redefine the value and nature of labor. Its implications extend far beyond any single industry or role.

As the pace of innovation becomes exponential, organizations and workers alike must acknowledge these shifts, embrace the tools of transformation, and prepare for an era where adaptability and early adoption will create critical advantages.

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