The build vs. buy decision is one of the most consequential choices mid-market leaders face when it comes to AI. It is not simply a technology question. It is a question about where your company should invest its finite resources, how quickly you need to see returns, and what kind of operating architecture will position you for compounding performance over the next decade. Companies between $10 million and $100 million in revenue sit in a particularly difficult position: large enough to need enterprise-grade AI capabilities, but rarely resourced enough to build and maintain them independently. The wrong choice here does not just waste a budget line. It sets the trajectory of the business, for better or worse, for years to come.
This article examines the real costs, risks, and trade-offs of building AI in-house versus outsourcing, and introduces a third path that is emerging as the preferred model for operationally serious mid-market companies: managed AI operating architecture.
What Does It Take to Build AI Capabilities In-House?
Building AI capabilities in-house requires assembling a specialized team, investing in infrastructure, and committing to a timeline that is almost always longer and more expensive than initial projections suggest. Most mid-market companies underestimate the scope of this undertaking by a significant margin.
At minimum, a functioning in-house AI operation requires the following roles:
- Data Scientists to design models, analyze datasets, and translate business problems into quantitative frameworks. A single senior data scientist commands a salary between $150,000 and $250,000 annually, and you will need more than one.
- Machine Learning Engineers to build, train, deploy, and monitor production models. These are among the most competitive hires in technology. Mid-market companies compete for this talent against firms that can offer compensation packages two to three times higher.
- Data Engineers to build and maintain the data pipelines that feed AI systems. Without clean, reliable, well- structured data infrastructure, even the most sophisticated models produce unreliable outputs.
- DevOps and MLOps Specialists to manage the infrastructure that supports model training, deployment, and monitoring in production environments. AI systems do not run on spreadsheets. They require GPU-accelerated compute, containerized deployments, CI/CD pipelines tuned for model artifacts, and monitoring systems designed for statistical drift rather than simple uptime.
- Compliance and Governance Officers to ensure AI systems meet regulatory requirements, manage bias risk, and maintain audit trails. As regulatory frameworks around AI tighten globally, this role is becoming non-optional.
Beyond personnel, the infrastructure costs are substantial. Cloud compute for model training alone can run $10,000 to $50,000 per month depending on model complexity. Add data storage, development environments, monitoring tools, and security infrastructure, and the annual technology spend for a mid-market AI team easily exceeds $500,000 before a single model reaches production.
The timeline is equally demanding. From the point of initial hiring to the point where AI systems are delivering measurable business value, most organizations should expect a minimum of 12 to 18 months. That window assumes you can hire the right talent quickly, which is itself a major assumption. The average time to fill an ML engineering role is over 120 days, and mid-market companies without established AI teams often face an even longer cycle because they lack the employer brand recognition that attracts top talent.
Then there is the ongoing maintenance reality. AI systems are not software you deploy and forget. Models degrade as data distributions shift. Pipelines break as upstream systems change. New regulatory requirements demand retraining and re-validation. Maintaining an AI system in production is a permanent operational commitment, not a one-time project.
What Are the Advantages of Building AI In-House?
Despite the costs and complexity, building AI in-house offers genuine strategic advantages for companies that can sustain the investment. The decision should not be dismissed outright. It should be evaluated honestly against the company's resources and strategic priorities.
Full control over the technology stack. When you build in-house, you own every layer of the architecture. You choose the models, the frameworks, the data infrastructure, and the deployment patterns. There is no vendor dependency, no contract renegotiation, and no risk that a third party deprecates a feature your operations depend on. For companies with unique operational requirements that do not map to off-the-shelf solutions, this level of control can be genuinely necessary.
Deep customization aligned to business logic. In-house teams can build AI systems that reflect the exact nuances of how your business operates. A professional services firm with a proprietary methodology for client engagement can encode that methodology directly into its AI systems, something no generic vendor tool will do out of the box. The models learn your data, your workflows, and your edge cases.
Institutional knowledge retention. When AI capabilities are built internally, the organizational knowledge about how those systems work stays within the company. Team members understand the assumptions behind the models, the limitations of the data, and the history of decisions that shaped the architecture. This institutional knowledge is valuable and difficult to replicate when systems are built by an external party that eventually moves on.
Long-term cost efficiency at scale. For companies that achieve sufficient scale, the per-unit cost of in-house AI eventually falls below the per-unit cost of outsourced solutions. The fixed costs of the team and infrastructure are amortized across a larger volume of use cases and transactions. This crossover point exists, but it typically requires a level of AI maturity and organizational scale that most mid-market companies have not yet reached.
What Are the Risks of Building AI In-House for Mid-Market Companies?
For mid-market companies specifically, the risks of building AI in-house are disproportionately high relative to the potential benefits. The challenges that large enterprises absorb as a cost of doing business can become existential distractions for companies operating between $10 million and $100 million in revenue.
Talent acquisition is brutally difficult. The market for AI talent is one of the most competitive in the history of professional hiring. Mid-market companies are not just competing against other mid-market companies. They are competing against Google, Meta, OpenAI, and thousands of well-funded startups, all of which can offer higher salaries, larger equity packages, and more technically interesting work. Hiring one strong ML engineer is hard. Assembling an entire AI team is a multi-year effort that diverts leadership attention from core business operations.
Total cost consistently exceeds initial estimates. Our experience across dozens of mid-market engagements is that the actual total cost of building AI in-house runs three to five times higher than the initial projection. This is not because companies are bad at budgeting. It is because the scope of what AI requires, particularly the data infrastructure, governance, and ongoing maintenance layers, is genuinely difficult to estimate until you are deep into the work. A project budgeted at $300,000 routinely becomes a $1 million commitment once you account for hiring delays, infrastructure costs, re-architecture cycles, and the operational overhead of managing a technical team.
Opportunity cost is the hidden killer. Every dollar and every hour of leadership attention spent on building AI infrastructure is a dollar and an hour not spent on the company's core business. For a professional services firm, that might mean senior partners spending time in technical hiring committees instead of with clients. For a marketing agency, it might mean capital allocated to GPU infrastructure instead of talent acquisition or market expansion. The opportunity cost is real even when it does not appear on a balance sheet.
Technology moves faster than in-house teams can adapt. The pace of change in AI is unprecedented. Models that represented the state of the art 18 months ago are now considered baseline. Techniques that dominated last year have been superseded by fundamentally different approaches. An in-house team that spent a year building on one architectural paradigm may find that the paradigm has shifted beneath them before they reach production. Staying current requires continuous investment in research and experimentation, which is itself a significant resource commitment.
When Does Outsourcing AI Make Sense?
Outsourcing AI makes sense when a company needs to move from strategy to measurable results faster than an internal team can be assembled, trained, and deployed. For most mid-market companies, this describes their current situation precisely.
When speed to value is the priority. If the competitive landscape demands that your company has AI-powered capabilities operating within months rather than years, outsourcing is the only realistic path. An experienced external partner brings pre-built frameworks, proven architectural patterns, and a team that has already navigated the learning curve. What takes an in-house team 12 to 18 months to build from scratch, an experienced partner can deliver in a fraction of that time.
When technology is not your core competency. A professional services firm exists to serve clients, not to train machine learning models. A marketing agency exists to create and execute campaigns, not to manage Kubernetes clusters. When AI is a means to improve operations rather than a product you sell, there is a strong argument that the implementation and operation of AI systems should be handled by specialists whose core competency is exactly that.
When capital constraints demand operating expense over capital expenditure. Building in-house is a capital-intensive undertaking. You hire a team, buy infrastructure, and invest heavily before seeing any return. Outsourcing converts this capital expenditure into a predictable operating expense. For mid-market companies managing cash flow carefully, this financial structure can be the difference between being able to pursue AI transformation and having to defer it indefinitely.
When the company needs proven patterns, not R&D. Most mid-market companies do not need to push the boundaries of what AI can do. They need to apply well-understood AI capabilities, intelligent document processing, predictive analytics, workflow automation, natural language interfaces, to their specific operational context. This is implementation work, not research. An outsourced partner who has implemented these patterns across dozens of organizations brings a depth of applied experience that no newly assembled in-house team can match.
What Is a Managed AI Operations Model?
A managed AI operations model is the third option that most build vs. buy discussions overlook entirely. It goes beyond traditional outsourcing by providing not just development and deployment of AI systems, but ongoing operations, optimization, and evolution of the entire intelligent operating architecture. The provider does not deliver a project and walk away. They operate the systems continuously, tuning performance, adapting to changing business conditions, and expanding capabilities over time.
This is fundamentally different from both building in-house and from traditional outsourcing in several important ways:
- Whole-lifecycle ownership. A managed operations partner owns the performance of your AI systems from deployment through ongoing optimization. They monitor model accuracy, retrain when data distributions shift, expand automation coverage as processes mature, and handle infrastructure scaling. Your internal team stays focused on the business while the AI architecture compounds in value.
- Continuous improvement, not static delivery. Traditional outsourcing delivers a product at a point in time. A managed operations model delivers improving performance over time. The systems get smarter, more efficient, and more valuable with every month of operation because someone is actively optimizing them against your business outcomes.
- Aligned incentives. When a partner is responsible for operating your AI systems and is measured on the results those systems produce, their incentives align with yours. They are not trying to sell you the next project. They are trying to make the current systems perform better because their continued engagement depends on demonstrable results.
- Access to evolving expertise. A managed operations partner maintains a team that is continuously learning and adapting to the latest developments in AI. When a new technique emerges that could improve your systems, the partner evaluates it, tests it, and deploys it, all without your company needing to hire new specialists or invest in research.
At Hendricks, our Operations capability is built around exactly this model. We do not simply build AI systems and hand them over. We design, install, and operate intelligent operating architecture as a managed performance system. The architecture we deploy for our clients is designed to compound in value over time, and our ongoing operations ensure that it does.
How Do You Choose the Right Approach for Your Company?
The right approach depends on a clear-eyed assessment of five factors. None of them are purely technical. All of them are strategic.
Company size and stage. Companies under $25 million in revenue almost never have the resources to build and sustain an in-house AI team. The overhead is disproportionate to the organization's scale. Companies between $25 million and $75 million may be able to support a small internal team supplemented by external partners. Companies approaching $100 million can begin to consider larger in-house investments, but should still evaluate whether AI infrastructure is the best use of that capital relative to other growth investments.
Technical maturity. If your company does not already have a strong data infrastructure, clean and well-governed data assets, and a technology team capable of supporting production AI systems, building in-house means building from the foundation up. This adds 12 to 24 months and significant cost to the timeline. Companies with low technical maturity benefit most from a partner who brings both the architectural expertise and the operational capability to stand up the entire stack.
Strategic priority. Is AI a differentiator for your business, meaning it is part of the product or service you sell? Or is AI an operational enabler, meaning it makes your existing products and services more efficient and profitable? If AI is a differentiator, in-house investment is more defensible because the AI capabilities are core intellectual property. If AI is an operational enabler, the argument for external partnership is stronger because you want results, not IP.
Budget and financial structure. Building in-house requires significant capital investment with uncertain returns over a long time horizon. External partnerships and managed operations models convert this into predictable operating expense with measurable returns on shorter cycles. For companies with constrained capital or boards that demand near-term ROI, the financial structure of the engagement matters as much as the technical approach.
Timeline expectations. If leadership expects AI-driven operational improvements within the next two quarters, building in-house is not a viable path. If the company is willing to invest over a two to three year horizon before expecting full returns, in-house development becomes more feasible. Be honest about internal timelines and the patience of stakeholders.
Can You Start with One Approach and Transition to Another?
Yes, and this is often the smartest strategy. The build vs. buy decision is not permanent. It is a starting point. The most operationally mature mid-market companies we work with treat it as a phased evolution rather than a binary choice.
A common and effective pattern looks like this:
Phase 1: Advisory and Architecture. Begin with an external partner who can assess your current operational landscape, identify the highest-impact opportunities for AI, and design the target operating architecture. This is the Advisory phase of engagement. It requires no internal AI team and can be completed in four to eight weeks. The output is a strategic blueprint that guides every subsequent decision.
Phase 2: Engineering and Implementation. Work with an engineering partner to build and deploy the AI systems defined in the architectural blueprint. This is the Engineering phase. During this period, you may begin building a small internal team focused on understanding the systems being deployed, managing data quality, and serving as the internal interface between the business and the technology.
Phase 3: Managed Operations. Once systems are deployed, transition to a managed operations model where the external partner handles ongoing optimization, monitoring, and evolution of the AI architecture while your internal team focuses on applying the outputs to business decisions. Over time, your internal team can absorb more operational responsibility as their capabilities mature.
Phase 4: Selective Internalization. As the organization develops deeper AI maturity, selectively bring specific capabilities in-house where it makes strategic sense. This is not an all-or-nothing transition. Some capabilities, like model monitoring and data governance, may stay with the managed operations partner permanently. Others, like business-specific model development, may move in-house as the internal team reaches sufficient scale and expertise.
This phased approach mitigates the biggest risks of both building and buying. You get speed to value from the external partnership, you build internal capability progressively, and you never take on more operational complexity than the organization can absorb. At Hendricks, our three integrated capabilities, Advisory, Engineering, and Operations, are designed to support exactly this kind of phased evolution.
The bottom line: The question is not build vs. buy. The question is: what operating architecture gives your company compounding returns? The right answer is rarely pure in-house development, and it is rarely pure outsourcing. It is an architectural approach that matches your company's current capabilities to its strategic ambitions, and evolves as both mature. Companies that frame this as a technology procurement decision get technology. Companies that frame it as an operating architecture decision get performance.
If your company is navigating the build vs. buy decision and wants a clear-eyed assessment of which approach will deliver the best results for your specific situation, start a conversation with our team. We will help you diagnose where you are, architect where you need to go, and determine the right operating model to get you there.