Finding a technology partner for AI adoption isn’t difficult, there are plenty of companies that can build models and plug them into your systems. On the surface, everything looks promising: polished demos, confident timelines, impressive credentials.
Then the cracks appear. The model doesn’t solve your actual problem, struggles to work with your data, and starts producing unreliable outputs.
It might seem like a technical failure. But dig deeper, and the real issue becomes clear: the strategy was flawed from the start. The company jumped into execution without clearly defining the problem, aligning on goals, or setting success criteria.
While finding a technology partner for AI adoption is easy, choosing the right one is not. To start, you need to understand your needs first and ask questions that reveal whether a partner can truly deliver what you need.
See also: Understanding AI basics: types, terminology, and uses
Understanding business needs
It’s tempting to jump on the “AI for everything” bandwagon.
Since rushed AI initiatives often fail, take time to ask: What are you solving? What would success look like? Are you ready to support AI with the right data, people, and processes?
Define goals
Saying “We want to use AI to improve internal operations” is a start, but it’s too vague to drive meaningful outcomes. A clearer objective might be: “We want to delegate parts of the recruitment process to AI, targeting a 30% improvement in candidate quality as measured by post-hire performance.”
When your goals are specific, your team and your vendor can stay aligned. But if your objectives are unclear, you’re likely to end up with something that sounds innovative but fails to deliver results.
Scope the project
Are you launching a one-off initiative or driving a company-wide transformation? Are you testing an idea or building a scalable feature for thousands of users?
These aren’t just planning questions, they shape the entire trajectory of your project. Scoping helps define not only what you’re building, but also what kind of technology partner can support it. An independent consultant might handle a quick prototype, while a multi-phase rollout may require a full-scale vendor with the capacity to deliver at scale.
Estimate the budget and timelines
You don’t need a fixed budget on day one, but you do need guardrails. Know how much flexibility you have, and where the limits are. AI projects tend to grow in scope, cost, and complexity, especially as new data or edge cases surface.
Without clear boundaries, you risk endless, unmanageable scope creep. So, stay in control from the start.
See also: Future-proofing your business: The strategic advantages of AI
Choosing the right AI partner
Once you’ve clarified your internal needs, it’s time to look outward. To find the best partner for artificial intelligence development, ask questions and evaluate the partner’s capabilities.
Define your partnership needs
Start by deciding what kind of help you need.
AI consultants are ideal if you’re still shaping your strategy. They can help define use cases, validate ideas, and build early prototypes. Their strength lies in flexibility and speed, but they may not be the best choice for long-term implementation at scale.
AI vendors often come with packaged solutions or strong industry specialisation. They might offer ready-made components that can shorten time to delivery, but this also means less customisation and possibly less alignment with unique business processes.
Make a choice based on the phase you’re in and the complexity of the problem.
Evaluate the partner's track record
A slick portfolio doesn’t tell you much, and shiny case studies can hide a lot. Ask for real details. Look for projects that have progressed beyond pilots and are still in production. Find out:
What exactly did the partner build?
Who maintained it after launch?
What challenges came up and how were they handled?
Projects with domain overlap or similar technical complexity are especially valuable since they offer insights you can actually apply.
Assess partner's technical skills
It’s not just about data science. The right AI partners understand how to build AI that fits into a real system and know how to navigate integration challenges that might emerge in the process.
Ask questions like:
How do they test and validate models?
What tools and stack do they use?
How do they handle versioning, monitoring, and updates?
You don’t need to quiz them on neural net internals, but you should walk away with a clear understanding of how they develop, ship, and maintain their AI solutions.
Check industry-specific insights
In strictly regulated industries, having general AI knowledge isn’t enough. Whether you’re in healthcare, education, or commodity trading, domain expertise can make the difference between success and costly missteps.
If your field involves heavy regulation, unique workflows, or niche data structures, a generalist team may struggle.
That’s why it’s important to ask potential partners whether they’ve worked with similar companies.
If not, find out how they plan to get up to speed quickly and effectively.
Ask about their AI practices
Beyond technical skills, mature AI teams demonstrate strong operational habits that protect your investment and ensure smooth collaboration. Key areas to investigate include:
Code ownership: Determine whether you’ll have access to the code after the project ends and who owns the IP and code.
Data security: Check the company’s compliance processes and how they handle data.
Development practices: Focus on how the vendor manages version control, documents workflows, and plans for model retraining.
If a partner can’t clearly explain their processes in these areas, consider it a red flag.
Natalia Semak, Chief Delivery Officer at Altamira, recommends asking potential AI partners questions like:
How do you identify and prioritise AI use cases for a specific business?
How do you test feasibility before moving into full development?
What kind of data do you need to begin, and how do you assess its quality?
Can you explain your AI development process (from data collection to deployment)?
Do you build custom models or fine-tune existing ones?
What frameworks, tools, and infrastructure do you typically use?
How do you ensure data security, especially with sensitive or regulated data?
Are your practices compliant with GDPR, HIPAA, or other industry standards?
Can you integrate the AI solution into the existing systems or workflow?
Do you provide documentation and knowledge transfer for internal teams?
Can your solution support continuous learning and improvement?
Look under the hood.
Beyond pitch decks and demos, strong AI teams have mature development and ethical practices.
Why Altamira can be your best fit
Before making large investments, you need to know whether the project is feasible. That’s why we begin with a Proof of Concept (PoC)—a focused, working prototype that tests multiple use cases using your real data.
This helps identify the highest-impact opportunities and surface any technical risks early on. Clients often walk away with surprising insights, and sometimes the most promising ideas aren’t the ones they expected.
Turn raw data into a reliable foundation
No AI solution is better than the data behind it. Our team analyses your data for completeness, consistency, and usability to ensure it’s ready to train accurate, high-performing AI models. By the end of this step, you’ll know what is usable, what needs improvement, and what is possible.
Keep AI performance on track over time
AI isn’t a one-and-done deployment. Models drift, data shifts, and new edge cases emerge. That’s why we implement post-deployment monitoring from day one.
We set up tools to monitor performance, detect model drift, and enable automated retraining to ensure reliability over time.
Build on secure and compliant ground
Deliver real value, not just code
Some clients come to us looking for customer-facing features like AI assistants or recommendation engines. Others want to reduce repetitive work by automating internal processes.
Either way, we focus on building AI that delivers measurable improvements—be it faster decisions, lower manual effort, or better user engagement.
Move from experiment to production
Once a model is validated, we turn it into a scalable API, integrate it into your environment, and make sure it works under real-world conditions.
Stay in control, long after the launch
We guarantee transparency. You get full project documentation and hands-on training for your team.
This means no black boxes and no vendor lock-in.
You get the solution your team can understand, maintain, and improve on their own.
See also: Building AI-powered products: From concept to deployment
Final words
AI partnerships can be powerful, but they’re not plug-and-play solutions. The best outcomes come from clarity, context, and careful partner selection.
Before signing a contract, be clear about what problem you’re solving, how far you’re willing to go, and the type of support you truly need. Then find a partner who’s not only skilled in AI but also comfortable navigating your industry and business environment.
