Building or buying an AI project: ROI analysis

Deciding whether to build an artificial intelligence project in-house or purchase a turnkey solution is a major strategic challenge for SMEs. Beyond the initial cost, it’s all about measuring return on investment (ROI), time to value, technical risks and the ability to leverage existing data. This article proposes a pragmatic method for comparing building or buying an AI project, decision criteria adapted to SMEs, and ROI calculation tools. It also explains how Les Communicateurs helps companies turn this choice into a measurable competitive advantage.

Why this subject is essential for companies

Small and medium-sized businesses face severe constraints: limited budgets, small IT teams, the need for rapid results and competitive pressure. In this context, deciding whether to build or buy an AI project is not just a technical question, it’s a financial and strategic decision. Several factors make this subject critical:

Pressure on turnaround times: managers demand visible short-term impacts (cost reduction, productivity gains, improved customer service).
Scarcity of skills: recruiting data scientists, ML engineers and cloud experts is expensive and time-consuming.
Data quality: without clean, structured datasets, an AI project is unlikely to achieve what it promises.
Risk of vendor lock-in or technical debt if the solution is developed without a clear roadmap.
Strategic impact: AI can be a lever for differentiation when properly integrated into business processes.

Research shows that a well-targeted AI project can generate strong returns:

“a well-designed AI project can generate a positive ROI quickly, often as early as 3 to 9 months, with much higher returns (100% to 200% or even more) on the horizon of a year for high-volume use cases, especially in B2B.”

These figures show that a well-thought-out strategy, prioritizing high-volume, repetitive use cases, generally offers the best compromise between risk and value.

How Communicators turn challenges into opportunities

The Communicators support SMEs at every stage: diagnosis, design, implementation and impact measurement. Their approach aims to minimize risk, accelerate time-to-value and maximize AI ROI. In concrete terms, the agency works in four complementary areas:

Audit and strategic scoping: assessment of digital maturity, data quality, identification of priority use cases and initial financial simulation.
Build vs. buy decision: costed comparison of options (initial costs, recurring costs, implementation time, operational risk).
Proof-of-Value (PoV) and agile management: setting up prototypes to validate earnings hypotheses before full investment.
Industrialization and monitoring: integration, go-live, MLOps, team training and ongoing KPI measurement.

This pragmatic methodology provides a clear financial and operational basis for decision-making: how much will the project cost today, what concrete gains can be expected, and how quickly will payback be achieved?

Communicators also put mechanisms in place to preserve long-term value: data governance, modular architecture to avoid lock-in, and ongoing training to ensure adoption by business teams.

Strategies, tools and practical examples

The comparison between building and buying must be based on quantified scenarios, relevant KPIs and real-life examples. Here’s a toolbox and case studies used by Les Communicateurs.

Process automation: a typical use case

One of the most profitable use cases for an AI SME is the automation of repetitive tasks (data entry, classification, verification). Example: processing customer files.

Assumption: 30 minutes saved per file, 5,000 files/year, average hourly cost €40.
Annual direct gain = 5,000 files × 0.5 h × €40 = €100,000.
If the project costs €60,000 in initial build + €20,000/year in run, the first year ROI = (100,000 – 80,000) / 80,000 = 25%; from the second year onwards, the margin improves considerably.

For this type of case, The Communicators often recommend a PoV solution in 6 to 12 weeks to validate the gains before any massive investment.

Personalized marketing with AI

Personalized marketing (segmentation, scoring, recommendation) generates significant indirect benefits: higher open rates, increased average basket and better retention.

Example: a 5% increase in the conversion rate on a customer base of 50,000, average customer value €120, equals €300,000 in additional sales per year.
Cost of an AI marketing SaaS solution: variable, often billed on a subscription basis; in-house development costs: recruitment + infrastructure + maintenance.

Communicators evaluate the commercial leverage effect and design A/B tests to accurately measure impact before generalization.

Case study: building vs. buying – costed simulation

To help you decide, here is an example of a 3-year financial simulation:

Option A – Build in-house: Initial costs 150 k€ (team, infra), Annual costs 50 k€ (maintenance, cloud), Expected annual savings 120 k€ from year 1.
Option B – Purchase a solution: Initial cost 40 k€ (integration), Annual subscription 70 k€ (license + support), Expected annual savings 100 k€ from year 1.

Simplified ROI calculation (over 3 years) :

Build: Cumulative gains 360 k€ – Cumulative costs 250 k€ = 110 k€; ROI = 110 / 250 = 44%.
Buy: Cumulative gains 300 k€ – Cumulative costs 250 k€ = 50 k€; ROI = 50 / 250 = 20%.

Interpretation: building appears to be more profitable over 3 years if the company controls production and plans to reuse the AI bricks. Buying may be preferable if the need is immediate, internal capacity is low, or if the external solution provides standard functionalities at low initial cost.

Recommended tools and indicators

To monitor the performance of an AI project, The Communicators recommend tracking these KPIs:

Financial ROI: (Gains – Costs) / Costs.
Payback period: number of months required to cover the initial investment.
Automation rate: % of processes automated vs. total.
Reduction in processing time: in minutes/hours per file.
Quality indicators: CSAT, NPS, residual error rate.
Adoption rate: % of active users and frequency of use.
Inference costs: operational costs linked to calls to models in production.

Complementary methodologies: NPV, IRR, best/worst/most-likely scenarios, and sensitivity analysis on key assumptions (volume, adoption rate, hourly cost).

Long-term benefits for your company

Beyond the immediate financial gains, a well-run AI project brings lasting benefits that strengthen a company’s competitive edge:

Data capitalization: enhance the value of information assets for future uses (analytics, recommendations, forecasting).
Operational agility: the ability to automate new processes rapidly thanks to reusable building blocks.
Improved customer experience: personalization, rapid response, consistent service quality.
Brand image and appeal: innovative positioning, talent attraction.
Reduced operational risk: fewer human errors and better traceability.

These indirect benefits are often underestimated in conventional ROI calculations, but they explain why some companies accept a higher initial cost to retain control of their solution (build): strategic flexibility and the ability to exploit AI on multiple future use cases.

Conversely, AI outsourcing can free up resources to focus on the core business, accelerate industrialization and transfer part of the risk to the supplier, which is an important advantage for SMEs with limited teams.

Build or buy an AI project: decision criteria for SMEs

The decision must be based on a grid of weighted criteria. Here is a suggested checklist used by Les Communicateurs:

Data maturity: clean, historical and structured data? If not, the preparation effort can make the purchase more attractive.
Volume and repeatability: high-volume use cases = faster ROI for construction.
In-house skills: ability to develop, test and maintain models (MLOps)? Without this, outsourcing is often preferable.
Need for differentiation: should the algorithm be an exclusive competitive advantage? If so, building it is a strategic choice.
Implementation time: need immediate impact? Buying often reduces time-to-value.
Budget and risk: upfront vs. recurring costs, risk appetite and financial flexibility.
Roadmap and reuse: the possibility of reusing AI components for other use cases increases the value of building.

In practice, most SMEs opt for a hybrid strategy: start with a PoV with an external solution or specialized agency, then gradually industrialize and internalize the critical bricks once proof of value has been established.

Best practices for maximizing AI ROI

To secure investment and accelerate return, Les Communicateurs recommends several operational practices:

Prioritize use cases: focus first on those with high volumes and low business complexity.
Implementation of a limited PoV: validate production gains on a reduced scope.
Continuous measurement: monitor KPIs in real time and adjust models.
Data governance: define responsibilities, quality rules and anonymization process if necessary.
Training and adoption: invest in training to ensure adoption by business teams.
Modular architecture: design interchangeable components to avoid lock-in.
Comprehensive financial analysis: use NPV, IRR and sensitivity scenarios to present a solid case to senior management.

These best practices reduce the risks associated with hidden costs (maintenance, inference costs, model drift) and significantly improve recovery times.

Concrete examples supported by Les Communicateurs

Here are some simplified examples of assignments carried out by Les Communicateurs, illustrating the build vs. buy approach:

Industrial company (B2B): automation of quality control using computer vision. PoV achieved in 8 weeks; solution licensed to accelerate production. Result: 35% reduction in defects and ROI achieved in 7 months.
Insurance brokerage: automatic scoring of incoming files. Started with an external solution to set up scoring and train teams; then partial internalization of critical models. Result: 25% reduction in processing time and improved NPS.
SME e-commerce: product recommendations and email personalization. SaaS solution chosen to limit initial costs; after 18 months, in-house development of proprietary algorithms for high-value segments. Result: 7% increase in average shopping basket (ROI > 100% in 12 months).

These cases show that there is no one-size-fits-all answer: strategy has to reflect business situation, internal capacity and long-term vision.

Conclusion: take action with Les Communicateurs

Building or buying an AI project is a multi-dimensional decision that requires rigorous financial analysis, risk assessment and strategic vision. For an AI SME, the challenge is to maximize AI ROI by choosing the path that best matches data maturity, internal capacity and growth objectives.

The Communicators provide a proven methodology: audit, rapid PoV, financial simulation (ROI, NPV, IRR), agile production implementation and adoption support. The objective is simple: to transform a technical challenge into a measurable performance lever – saving time, reducing costs, improving quality and differentiating on the market.

To find out whether your next AI project should be built in-house or purchased, The Communicators offer a customized diagnostic and ROI simulation. Request a consultation for :

assess the maturity of your data and prioritize use cases,
simulate different financial scenarios and measure the payback period,
design a roadmap (PoV → industrialization → governance) tailored to your SME.

Contact The Communicators to find out how to improve your marketing and operational performance with AI, request a consultation or explore AI automation and outsourcing solutions designed to generate tangible, sustainable ROI.