Integrating artificial intelligence is the new standard. Leaving it aside means missing out on a true engine that boosts productivity and transforms working models across all industries. It is that simple.
According to a 2024 McKinsey report, 72% of companies stated that they were already using artificial intelligence in at least one function. Adoption is high, and all projections confirm that this trend will continue in the future.
Having your own specialized team involves either building it internally or relying on an external provider. Each alternative has different impacts, but don’t worry. Choose the best option using the decision matrix we have built.
Decision Matrix
| Dimension | In-house Development | Technology Partner |
|---|---|---|
| Agility and Time-to-Market | Building the team may take 3 to 6 months to see a first pilot and 6 to 12 months to generate visible value. | Arrives with proven stack, processes, and best practices. Functional pilots are deployed in weeks. |
| Total Cost of Ownership (TCO) | TCO includes salaries, recruitment, benefits, tools, infrastructure, and leadership time spent building the area from scratch. | Transforms fixed costs into variable ones: you pay for capacity, milestones, or projects without permanently financing the entire chain. |
| Learning Curve | The team usually starts without deep experience in generative AI, MLOps, and prompt engineering best practices. | The partner’s talent already comes with a completed learning curve from other projects and industries, with ready-to-use know-how. |
| Team Scalability | Scaling requires new recruitment cycles, onboarding, and cultural alignment; adjusting team size when demand changes is slow and costly. | Allows capacity to scale up or down almost in real time based on demand peaks or stabilization phases, without internal friction. |
| Profile Flexibility | As use cases grow, diverse profiles are required, which are difficult to concentrate in a single team without underutilization. | Providers have pools of specialized profiles that activate according to project needs. |
| Talent Retention | The departure of a key person causes delays. Dependency on specific individuals increases operational risk. | Risk is distributed: continuity depends on a team and documented processes. |
Agility and Deployment Speed (Time-to-Market)
Choosing between in-house development or a partner mainly defines when you will see the first AI solution in production, not just how it will be built.
In a context where companies that delay AI adoption accumulate greater competitive disadvantage, time-to-market becomes a central decision criterion.
In-house Development
An internal AI team rarely starts delivering in weeks. Hiring and aligning the right talent can take 3 to 6 months.
Your competition may use this time to test AI pilots with real users while your team is still forming.
Additionally, the first generation of enterprise AI solutions often faces trial-and-error due to:
- Defining use cases
- Data governance
- MLOps pipelines
- Success metrics and security
Without prior experience, the first AI project typically takes 6 to 12 months to generate visible business value.
Technology Partner
Validated external talent arrives with a proven stack including data ingestion patterns, architecture templates, and RAG, evaluation, and observability best practices.
This accelerates pilot development and allows planning in weeks rather than quarters.
Providers also run parallel low-scope experiments across operations, customer support, and revenue, enabling data-driven prioritization.
Total Cost of Ownership (TCO)
TCO reveals hidden costs that increase investment beyond salaries. In AI, this includes salaries, tools, GPUs, rework risks, and delay impacts.
In-house Development
The total cost of an internal AI team goes far beyond base salary, including:
- Recruitment costs
- Employee benefits
- Acquisition and licensing costs
Hidden costs often raise AI budgets to hundreds of thousands of dollars annually.
Technology Partner
A partner converts fixed costs into variable spending tied to projects or capacity.
This reduces overinvestment risk and avoids underutilized talent.
Talent Learning Curve
The speed at which teams adopt AI best practices determines how many projects reach production.
In-house Development
Training teams in prompt engineering and AI operations takes months, while AI evolution demands constant upskilling.
Technology Partner
Partners bring refined know-how from multiple industries and transfer knowledge through documentation and collaboration.
Talent Scalability and Flexibility
In-house Development
Scaling internal teams is slow and costly, often leading to overloaded teams and growing backlogs.
Technology Partner
Capacity adjusts dynamically without organizational friction.
Retention and Operational Risk
In-house Development
The departure of a key AI leader causes severe operational impact and loss of undocumented knowledge.
Technology Partner
Risk is distributed across teams and processes, ensuring continuity even when individuals rotate.
