For years, AI capabilities have been integrated into software platforms as an additional resource, bundled into subscriptions, offered as preview features, or wrapped into productivity tools. For many businesses, AI has felt incremental and largely “included.” That era is ending.
As the market moves beyond the initial AI “gold rush,” a more practical question is emerging for business leaders: how do you know AI is delivering value? For small and midsize businesses (SMBs), this question is especially critical, as tighter margins and fewer resources mean AI investments must translate directly into measurable outcomes.
At the same time, rising costs to develop and operate AI push vendors toward various forms of monetization, turning AI into a recurring operating expense and making it essential for SMBs to connect spend directly to business outcomes. Without a clear understanding of what drives AI costs and how those costs connect to business outcomes, SMBs risk paying more each month for capabilities that generate activity, but not necessarily value.
The companies seeing the greatest return on their investment are those embedding AI directly into the systems where work happens every day, using real-time financial and operational data to drive decisions, improve efficiency, and deliver outcomes across core workflows. When paired with disciplined oversight of AI spend, this approach helps SMBs avoid surprises and compete more effectively with larger enterprises.
AI Pricing Is Shifting Faster Than Many SMBs Expect
Analysts expect 2026 to be the year of AI monetization. Driven by rising infrastructure costs and the need for predictable revenue, many vendors are experimenting with different payment structures and tiered AI access.
What can help keep rising AI costs in check? For starters, an organization needs to have clearly defined outcome-driven use cases. When AI is introduced broadly and haphazardly, usage of AI features tends to grow, but without a corresponding business impact.
This gap between activity and outcomes is where AI costs begin to escalate without delivering real value. Scattered data stored in various disconnected platforms, systems and processes only compounds this problem.
For instance, a 50-person distribution company might track inventory in one system, invoicing in another, and customer orders in a spreadsheet their office manager built ten years ago. When AI reconciles those three sources every time it generates a recommendation, you pay for that reconciliation work on every query, and the outputs are only as reliable as the weakest data source. To avoid greater costs, organizations should examine where operational data live and what it would cost to consolidate it, before expanding AI use. That audit cost is likely less than the ongoing inefficiency of running AI on fragmented inputs.
Over-automation adds another potential layer of costs when a single prompt can trigger multiple downstream tasks with each action coming at a price of inefficiencies.
Outcome-Driven AI vs. Spend-Driven AI
As AI becomes more central to business operations, managing AI costs becomes dependent upon identifying those capabilities that create true value.
AI embedded into systems that already contain clean, real-time operational data can automate meaningful tasks, such as invoice reconciliation and report generation. These are areas where even incremental improvements produce tangible financial benefits. This embedded intelligence approach gives SMBs access to capabilities once reserved for large enterprises, leveling the playing field through advanced analytics tied directly to business outcomes.
Business Management Foundations Keep AI Monetization in Check
Although AI adoption is accelerating, its success still depends on the quality of the core business system.
Business management platforms, such as ERP solutions, serve as the “central nervous system” of a company, connecting financials, operations, inventory, project management, and customer data to create a digital replica of the business. When this foundation is modern and unified, AI can more efficiently operate on consistent data with clear process boundaries, enabling accurate predictions and reliable automation.
When systems are fragmented or outdated, AI struggles to interpret inputs, leading to higher costs, lower accuracy, and increased reliance on manual oversight.
AI Pricing Models Will Shape SMB Adoption
As AI becomes a recurring expense, pricing models will influence how SMBs adopt and scale these capabilities. Traditional per-user licensing can limit access and constrain value, forcing businesses to ration tools across teams. For a 30-person manufacturer or a 15 person logistics firm, per-user pricing often means paying for seats that go mostly unused, while the two or three employees who actually need deep AI access hit capability limits.
In contrast, consumption-based pricing aligns cost with value, allowing organizations to scale AI adoption based on measurable outcomes rather than paying for unused features or licenses. This approach is particularly important for mid-market businesses, where traditional pricing models often lead to overinvestment in capabilities that go underutilized.
What’s a practical starting point to controlling AI costs? Identify one workflow where errors or delays are already costing you measurable time or money. Pilot AI there exclusively for 90 days, track a single metric (e.g., error rate, hours saved, etc.), and use that data to justify or halt further investment. Resist pressure from vendors to expand the scope before the pilot is complete.
By aligning cost to actual usage and outcomes, this model also provides the flexibility to scale alongside the business while enabling broader access to AI across teams without constraints.
How SMB Executives Can Manage AI Costs
For those SMBs that may not have the staff and budget to track all aspects of AI adoption, designate one person to review AI usage reports monthly, set a cost threshold that triggers a mandatory review before usage can continue scaling, and build that threshold into your vendor contract from day one.
Before signing any AI contract, ask vendors three questions:
- What is the maximum cost per user action or API call?
- How do you alert customers when usage spikes unexpectedly?
- Can you show me a case study from a company of our size with measurable before and after metrics?
If a vendor can’t answer all three, that’s a red flag.
Equally important is ensuring AI is usable and accessible. Intuitive tools and features, allow those users without technical expertise to access insights, increasing AI’s practical impact. Above all, SMBs should view AI as part of a broader digital modernization strategy. When built on a strong operational foundation, AI can enhance performance while managing costs and expenses.
AI is set to become a standard line item in SMB budgets. The question is whether that line item reflects deliberate investment. SMBs that treat AI like any other capital investment, with clear use cases, defined success metrics, and clear vendor expectations, will find AI as one of their most valuable assets.
About the Author
As Chief Product Officer, Jon is responsible for Acumatica’s technical strategy and product roadmap, development, and direction. His 25-year career spans leadership roles at major tech and payments companies, including Worldpay, Dell, Intel, Polaroid, and Asurion, with expertise in product management, development, planning, and marketing.
Jon, most recently, served as Chief Product Officer, and later as General Manager, at Procare, where he led product managers and UX designers in developing childcare center management SaaS and payment solutions. His expanded responsibilities included sales, marketing, product development, and customer support. He also served as SVP and Chief Product Officer over Worldpay’s U.S. core product. At Asurion, as VP of Product Management and Development, he led the creation of Soluto™, a premium tech support service for smartphone users with over 40 million monthly subscribers.
