Have you felt that pang of uncertainty? The questionā¦ Should I build my own AI, developed by my team, or can I rely on existing solutions that could save me thousands of dollars and hours of work?
Do you need AI to automate sales, improve customer service, or extract insights from data, but donāt know whether to build it internally or outsource it to a partner?
Both paths offer advantages, but they also hide traps that can cost you time, money, and opportunities.
Discover them all in this blog post! Letās beginā¦
When your company doesnāt leverage AI, this happens:
Slow and manual processes that burn through your teamās hours.
Decisions based on gut feeling, not data.
Frustrated customers due to delayed or generic responses.
Competitors overtaking you with instant personalized offers.
This pain is unique: inefficiency that translates into lost sales and a demotivated team.
Now, imagine a scenario where AI speeds up your workflow and turns every lead into a concrete opportunity. Thatās the promiseāif you choose wisely.
+3% Annual Productivity thanks to generative AI automating repetitive tasks, according to consulting and marketing agency McKinsey.
Increased Revenue: Companies using chatbots convert 3Ć more than those using static forms, according to Appian.
Cost Reduction: Up to 50% lower expenses in support and marketing by delegating workflows to AI.
+61% Customer Satisfaction with instant, personalized responses, according to McKinsey.
Investing in AI is not an expense: it's the lever that accelerates your growth.
Team and Talent
Hire data scientists, ML engineers, and DevOps specialists.
Invest in continuous training and certifications.
Infrastructure
Buy GPUs or rent cloud instances (AWS, Azure).
Set up secure and scalable data pipelines.
Development and Maintenance
Design custom models with your data.
Iterate, test, and adjust continuously.
Governance and Compliance
Implement privacy policies (GDPR, LGPD).
Monitor biases and production performance.
Absolute Personalization: Models tailored specifically to your business.
Data Control: All information remains in-house.
Competitive Edge: Unique solutions your competitors can't replicate.
Initial Investment: 3ā5Ć higher than buying an external solution. You can read more about AI investment specifics for your business in this article: Graphapp.
Development Time: AI projects can take 6ā12 months to reach production.
Talent Retention: Risk of turnover and ābuilderās remorseā if projects fail... See this article for more: Fastcompany.
Ongoing Maintenance: Every data update requires retraining and testing.
A regional bank in Brazil decided to internally develop a credit scoring model. After 9 months and USDāÆ500,000 in infrastructure and salaries, the project struggled to deliver a working model, and the data science team became overwhelmed with maintenance tasks.
Result: Negative ROI in the first year and a need to turn to an external partner.
This shows just how critical it is to conduct an exhaustive evaluation before diving into such an undertaking...
Choose a Reliable Partner
Evaluate experience, success stories, and data culture.
Verify native integrations with your CRM/ERP systems.
Define Scope and KPIs
Set clear objectives: response rate, churn reduction, automation of X% of tasks.
Align on timelines and service levels (SLAs).
Integration and Launch
Connect the external solutionās API or plugin (e.g., Darwin AI).
Test in a pilot environment before scaling.
Continuous Optimization
Review performance reports and fine-tune workflows.
Leverage new features without additional investment.
Rapid Implementation: Ready in days or weeks, not months.
Variable Costs: Subscription or pay-per-use models.
Lower Technical Complexity: No need for an internal ML team.
Automatic Updates: Improvements and patches without disruption.
Less Customization: The model might not fit 100% of your needs, but 90ā95% may be enough. For the remaining 5%, explore other solutions without spending thousands.
Vendor Dependency: Risk of pricing or roadmap changes.
Data Privacy: Ensure no-use clauses for external training. You can read more on this here: Appian.
A dealership chain in Argentina implemented Darwin AIās Bruno to re-engage cold leads.
Results in just 4 months:
Response rate increased by 42%.
50 extra cars soldāwithout hiring a single additional agent.
The monthly cost of USDāÆ199 was recovered in extra sales in the first month.
Below is a chart you can copy and keep handy to present this idea to your colleaguesāsaving time and cutting down debates about what's more convenient.
| Aspect | In-House AI | External AI |
|---|---|---|
| Initial Costs | Very high (3ā5Ć) | Low (subscription or pay-per-use) |
| Implementation Time | 6ā12 months | Daysāweeks |
| Customization | Maximum | High (with limitations) |
| Data Control | Full | Contract-bound |
| Scalability | Depends on your infrastructure and team | Infinite (cloud-based) |
| Maintenance | Internal and costly | Included in the service |
| Risk | High (talent, budget) | Low ā Medium (vendor dependency) |
The Best Alternative Is Strategic Outsourcing with an Expert Partner: Darwin AI
And you can even complement it with free or freemium tools for specific tasks.
Donāt waste months and tens of thousands of dollars on infrastructure and talent.
With over 10 years advising SMEs on digital transformation and AI projects across LATAM, weāve seen both failures and successes. Our approach combines:
Practical experience: Projects delivered in real estate, retail, automotive, and dozens of other industries.
Measurable results:
+35% in productivity
ā40% reduction in sales cycle time
75% of customer interactions handled by AI
If you postpone the decision:
Think of it this way: Every day without AI is a lost lead.
Your team stays stuck in low-value tasks.
Your competitors will take your customers with faster, more personalized experiences.
The truth isā¦ thereās not much to think about.
We can offer you a DEMO to create your own AI assistant for your business, if you're interested ā> Access here
Learning curve: Whether internal or external, AI adoption requires cultural shifts.
Initial adjustments: The first few days may require fine-tuning.
Change management: Involve your team from day one to prevent resistance.
But itās worth it:
Companies that overcome this initial phase typically see an ROI >āÆ200% in 12 months, as shown here: Graphapp.
Diagnose the pain point: Map out manual processes and define goals (sales, customer service, data insights).
Evaluate Build vs Buy: Compare costs, timelines, and degree of customization.
Quick pilot:
If outsourcing, launch a Darwin AI employee in 48h.
If building, launch an MVP with open source libraries and run a small test to see what suits you best.
Deciding between in-house or outsourced AI is one of the most critical strategic choices your SME will face in 2025.
While building in-house offers complete personalization and full data control, it also involves high costs, long timelines, and execution risks.
On the other hand, outsourcing to a partner like Darwin AI gives you fast implementation, variable costs, and an ecosystem of AI employees ready to work from day one.
LEARN THE NECESSARY STEPS and choose the best AI strategy for your business! Donāt fall behind or wait too long.
If youāre interested in learning more about our AI employees, dive in here: Darwin AI ā AI Employees š
Adopting AI isnāt just about modernizing your SMEāitās about ensuring its competitiveness, efficiency, and sustainable growth in an increasingly demanding market.