Agentic AI in SMBs: Efficiency, Advantage, and Strategic Outlook
Thesis
Agentic AI is radically repricing the economics of SMB operations. In the past, improving business processes yielded only incremental gains, but today the cost of "cognitive" work performed by AI is plummeting at an unprecedented rate.
This deflationary cost curve for AI labor contrasts starkly with human labor costs (which tend to rise over time), implying that companies embracing AI-driven operations are moving onto a fundamentally different—and lower—cost trajectory than those reliant on traditional staffing.
This order-of-magnitude cost collapse in operational "compute" means that many tasks which were uneconomical or too slow to automate in the past can now be handled by AI at negligible marginal cost. Early evidence from deployments supports significant operating leverage: Duke Energy's use of autonomous agents eliminated entire workflows and cut outage incidents by nearly one million per year—changes that cannot be matched by human-speed processes.
AI isn't just a new IT tool—it enables a step-change reduction in unit costs and process latency that savvy operators can translate into competitive advantage.
SMBs: The Ideal Adopters
SMBs with the right capabilities can "rehouse" their business logic into AI systems and reap outsized efficiency gains. Unlike large enterprises, which often carry complex legacy systems and bureaucratic change processes, smaller firms can pivot their workflows relatively quickly.
They are using AI agents to:
- Automate bookkeeping entries
- Chase down late invoices
- Update CRM entries
- Answer routine customer queries
- Manage inventory triggers
The result is businesses that operate faster and leaner: routine transactions get processed continuously without human prompting, decisions happen in real-time, and employees are freed from low-value busywork to focus on creative or strategic tasks.
Compounding Competitive Advantages
These efficiency gains can compound into durable competitive advantages. Because AI agents keep learning and improving with each cycle, early adopters can build a self-reinforcing lead. Every additional task automated or decision accelerated is an iteration in organizational learning that competitors cannot easily copy later on.
Companies aggressively reshaping their cost base with AI are "building advantages competitors cannot easily replicate," while those clinging to legacy cost structures face a compounding disadvantage each quarter.
The traditional advantages of scale are somewhat neutralized here: implementing a powerful AI workflow no longer requires a huge IT team or budget—a small firm with the right strategy can subscribe to an AI service or use low-code tools to deploy an agent in hours.
Operational excellence powered by AI could become a more important determinant of SMB success than traditional factors like sheer scale or legacy market share. Large incumbents may struggle to adapt quickly due to their complexity, whereas a smaller organization can architect itself around AI from the ground up.
Counterarguments
Platform Shifts Could Eclipse Internal Efficiency
We are on the cusp of major platform shifts—augmented reality, virtual reality interfaces, and AI-agent marketplaces are quickly growing. If consumers and partners migrate to these new platforms, success may hinge more on being an early mover in these experiences—something that typically favors larger players with bigger marketing budgets or exclusive platform deals.
The competitive advantage might shift towards those who capture mindshare in new mediums, not those who merely run existing processes at lower cost. Presence could trump efficiency.
"Agent marketplaces" are emerging as the distribution layer for AI capabilities, where discoverability is becoming a key challenge—"distribution becomes harder than development" in a world where many AI solutions exist but getting them in front of users is the real hurdle.
Commoditization Risk
A secondary counterpoint is that AI tools themselves become ubiquitous and commoditized, eroding any early-mover advantage. If every company can use the same generative AI services, internal efficiency might become table stakes rather than a differentiator—necessary to stay in the game, but insufficient to win.
Rebuttal
Internal loop-speed, far from being irrelevant, governs how well a business can adapt to any external change.
SMBs that have built connectors and internal execution layers with AI can plug into new platforms at minimal marginal effort. If AR commerce surges, the retailer with an AI-driven content pipeline can generate AR-ready product models and data feeds overnight, while another retailer takes months to manually produce AR assets.
Superior workflow execution enables faster and smarter moves in the distribution game. It's not a binary choice of workflow vs. distribution—internal speed is the engine that lets you accelerate when the road turns.
We also rebut the notion that internal AI will be merely commoditized with no edge. There's a difference between having access to a technology and operationalizing it optimally. The real competitive moats form in how organizations integrate AI into their unique processes and continuously improve them.
Strategic Implications for Operators
1. Treat AI-Enabled Cost Restructuring as Strategic Priority
Operators should identify high-volume, rules-based workflows (data entry, scheduling, routine customer interactions) and aggressively automate these with AI agents. The strategic framing should be: "If we started this business today with minimal staff, how would we architect our processes using AI and software?"—then drive towards that vision.
2. Build Technical Fluency and "Connective Tissue"
Even if certain AI use cases don't seem immediately applicable, operators should lay groundwork by modernizing IT systems and embracing APIs and integration platforms. The ability to plug into new tools or data sources quickly can be a competitive lifesaver.
3. Maintain Strategic Focus on Customer-Facing Innovation
The operator should not allow excitement about AI efficiency to blind them to shifts in customer behavior and channels. Use the "dividend" from AI improvements (time saved, cost saved) to fund explorations of AR/VR experiences, agent integrations, etc.
4. Embed AI Governance and Skill Development
Encourage teams to treat AI as "co-workers"—incorporate the outputs of AI agents into regular meetings or reports, and train employees to interpret and fine-tune AI outputs.
Watchpoints / Falsifiers
Rate of AI Adoption
If by 2026 still only ~10% of SMBs have gone beyond basic AI usage, it may indicate that barriers (cost, expertise, cultural resistance) are higher than anticipated.
SMB Performance Divergence
Watch for widening gaps in profit margins, employee productivity, or cycle times between AI-forward companies and those that haven't embraced agentic AI. If by 2027 there is little performance differential, the thesis is weakened.
Behavior of Large Enterprises
A falsifier would be if Fortune 500 companies succeed in transforming at equal pace, using scale + efficiency to extend rather than lose advantages.
New Platforms and SMB Participation
If a clear pattern emerges where being present on a platform (even with inefficiencies) beats being absent (despite efficiencies), that could falsify the primacy of internal advantage.
AI Cost Trajectory
Conclusion
The highest-performing SMBs will likely be those that combine internal excellence with external awareness—operational excellence and external vigilance.
The early signals—dramatic cost declines, successful pilot cases, rising SMB AI adoption—support the view that agentic AI can be a true strategic pivot for those who embrace it. However, this is not a "set and forget" thesis. Operators should continuously monitor watchpoints and be ready to course-correct.
The recommendation: Lean into the transformation (given the asymmetric upside of being right about a durable advantage), while hedging by staying alert to external shifts that could redefine what that advantage needs to be.