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AI Engineer vs ML Engineer vs Forward-Deployed Engineer: What Does Each Role Actually Build?

UNL Solutions — Tue, 20 Oct 2026 12:26:50 +0000

AI-related engineering roles are becoming harder to distinguish.

Open a few job descriptions, and you'll find similar requirements: Python, APIs, cloud infrastructure, machine learning frameworks, LLMs, and production experience.

But an AI Engineer, an ML Engineer, and a Forward-Deployed Engineer don't necessarily solve the same problems.

The difference becomes clearer when you look at what each role is expected to deliver.

1. AI Engineer: Turning AI Capabilities Into Product Features

An AI Engineer typically focuses on integrating AI capabilities into software applications.

Imagine a SaaS company wants to introduce an AI assistant that can answer questions using internal documentation and customer account data.

The team might use an existing language model rather than train one from scratch.

But there's still considerable engineering work involved:

Connecting the model to application APIs and data sources
Implementing retrieval-augmented generation (RAG), when appropriate
Managing prompts, context, and conversation state
Validating model outputs
Handling authentication and access permissions
Monitoring latency, reliability, and API costs

The model is only one component of the feature.

The AI Engineer's responsibility is to make it work within the product's existing architecture.

Typical skills: Python, JavaScript or TypeScript, backend development, REST APIs, LLM APIs, RAG, databases, cloud services, and AI evaluation.

Example deliverable: An AI-powered support assistant integrated into an existing SaaS platform.

2. ML Engineer: Building and Operating Machine Learning Systems

ML Engineers focus more directly on the machine learning lifecycle.

Their work often involves preparing datasets, training models, evaluating performance, building pipelines, and deploying models into production.

Consider a fintech company developing a fraud detection system.

Using a general-purpose LLM may not be the most suitable approach. The company might need a model trained on transaction patterns, historical fraud cases, and domain-specific signals.

An ML Engineer could be responsible for:

Preparing training and validation datasets
Developing feature engineering pipelines
Training and evaluating candidate models
Optimising inference performance
Deploying models through production services
Monitoring model quality and data drift
Managing retraining workflows

Unlike many application-focused AI engineering tasks, this work may require deeper knowledge of statistics, machine learning algorithms, and model evaluation.

Typical skills: Python, SQL, PyTorch, TensorFlow, scikit-learn, data pipelines, experiment tracking, model serving, and MLOps.

Example deliverable: A production fraud detection model supported by automated training, deployment, and monitoring pipelines.

3. Forward-Deployed Engineer: Making Technology Work in Customer Environments

Forward-Deployed Engineers (FDEs) operate at the intersection of software engineering, customer collaboration, and implementation.

Their responsibilities depend heavily on the company and product.

Imagine an enterprise AI platform that needs to be deployed for several customers.

Each customer has different databases, internal APIs, security requirements, and business workflows.

The underlying platform may be the same, but the implementation challenges are not.

An FDE might:

Work directly with customer engineering teams
Understand technical requirements and operational constraints
Integrate the platform with existing systems
Build customer-specific extensions or workflows
Troubleshoot deployment issues
Adapt implementations based on real-world feedback
Share recurring customer requirements with the core product team

An FDE is not simply a consultant who provides recommendations.

The role typically involves hands-on engineering and responsibility for getting a solution working in the customer's environment.

Typical skills: Software engineering, APIs, system integration, cloud infrastructure, debugging, solution architecture, and technical communication.

Example deliverable: A customer-specific deployment of an AI platform integrated with enterprise systems.

4. Comparing the Three Roles

Area	AI Engineer	ML Engineer	Forward-Deployed Engineer
Primary focus	AI-powered product features	ML models and pipelines	Customer-specific implementation
Main responsibility	Integrate AI into applications	Develop and operate ML systems	Adapt and deploy solutions
Typical environment	Product engineering team	ML or data engineering team	Customer-facing engineering
Common challenges	Integration, reliability, latency	Model quality, data drift, MLOps	Legacy systems, customer requirements
Typical output	AI-enabled application feature	Production ML system	Working customer deployment

These are common distinctions rather than strict boundaries.

An AI Engineer may work on model evaluation. An ML Engineer may build application APIs. An FDE may implement complex AI workflows.

The exact responsibilities depend on the organisation.

5. How to Decide Which Engineer You Need

Start with the technical problem rather than the job title.

Scenario A: You're adding an LLM feature to an existing product

You want to introduce semantic search, document summarisation, or an AI assistant.

Your main challenges involve connecting an existing model to application data, managing outputs, and maintaining reliability.

Likely fit: AI Engineer.

Scenario B: You're developing a custom prediction model

Your product depends on domain-specific data, model training, evaluation, and continuous improvement.

Likely fit: ML Engineer.

Scenario C: Your platform requires customer-specific deployments

You already have a product, but each customer needs integration with different systems, data sources, and infrastructure.

Likely fit: Forward-Deployed Engineer.

Scenario D: Your existing team lacks one specific area of expertise

You have backend and frontend developers, but nobody has production experience with LLM integration or ML pipelines.

You may not need to build an entirely new department.

Adding an experienced specialist through staff augmentation can help address the gap while keeping engineering ownership within your existing team.

The engagement model matters, but defining the technical responsibilities comes first.

6. What to Evaluate During Technical Interviews

The interview process should reflect the actual work.

For an AI Engineer, discuss system design, model integration, retrieval strategies, output validation, and failure handling.

For an ML Engineer, explore model evaluation, dataset quality, training pipelines, deployment, and monitoring.

For a Forward-Deployed Engineer, focus on integration scenarios, debugging unfamiliar systems, technical trade-offs, and communication with customer stakeholders.

A candidate's familiarity with popular AI tools is useful, but production engineering experience is often more revealing.

Ask what they built, how it was deployed, what went wrong, and how they handled it.

Final Thoughts

AI Engineer, ML Engineer, and Forward-Deployed Engineer are overlapping but distinct roles.

The right choice depends on whether your main challenge is integrating AI into a product, developing machine learning systems, or implementing solutions in customer environments.

Before hiring, define the expected deliverables, technical constraints, and expertise already available within your team.

For a broader comparison of responsibilities, skills, and hiring considerations, read the guide from UNL Solutions:

AI Engineer vs ML Engineer vs Forward-Deployed Engineer: Who Do You Need?

AI Integration in Production: 5 Challenges Developers Should Plan For

UNL Solutions — Wed, 14 Oct 2026 15:02:15 +0000

Adding AI to an existing application can look straightforward at first.

Choose a model, connect an API, send a request, process the response. A working prototype might take only a few days.

But production introduces requirements that a prototype rarely has to deal with: inconsistent data, existing system dependencies, security constraints, unpredictable workloads, and ongoing monitoring.

The real challenge is often not getting the model to respond. It's making AI work reliably within the rest of the application.

Here are five areas worth examining before moving an AI feature into production.

1. Your Data Might Not Be Ready

An AI feature is only as useful as the information it can access.

In an existing product, relevant data may live across several databases, third-party services, internal APIs, and unstructured documents.

Even when the information is available, it may contain duplicates, outdated records, or inconsistent formats.

For example, imagine building an AI assistant that answers questions about customer orders. If order statuses differ between the CRM, warehouse system, and billing platform, the assistant could produce misleading answers despite using a capable model.

What to consider:

Identify which systems contain the required information.
Validate data quality before connecting it to the AI workflow.
Define which source should be trusted when records conflict.
Establish how the AI feature will access updated information.

Data preparation isn't necessarily the most exciting part of AI development, but it can determine whether the entire integration succeeds.

2. Legacy Architecture Can Complicate Integration

Not every application was designed with AI features in mind.

Some systems expose well-documented APIs. Others depend on tightly coupled services, older databases, or business logic that is difficult to access independently.

A simple API integration may be sufficient for isolated functionality. More complex workflows might require an additional service layer, asynchronous processing, queues, or changes to existing interfaces.

Consider an AI-powered document processing feature.

The model might extract information successfully, but the application still needs to validate the output, update internal records, handle processing failures, and prevent duplicate operations.

The integration architecture matters just as much as the model.

A useful approach is to start with a clearly defined workflow, understand its dependencies, and introduce only the infrastructure necessary to support it reliably.

Not every AI feature requires an architectural overhaul.

3. Security Cannot Be Added at the End

AI integration introduces additional data flows, especially when external model providers are involved.

Developers need to understand what information leaves the application, where it is processed, and which safeguards apply.

This becomes particularly important when working with personal information, financial records, healthcare data, or internal company documents.

Before implementation, consider:

What data is sent to the model?
Does the request contain sensitive information?
Can unnecessary personal data be removed or masked?
Who can access the AI feature and its outputs?
What information is stored in application logs?
How are model-generated responses validated before being used?

Security also involves controlling what an AI-powered workflow is allowed to do.

An assistant that generates suggestions has a different risk profile from an agent that can modify records, execute transactions, or trigger business processes.

The more authority the AI system has, the more important access controls, validation, and human oversight become.

4. AI Features Need Different Testing Strategies

Traditional software testing often relies on predictable inputs and outputs.

AI-generated responses introduce additional variability.

Two requests with similar inputs may produce different wording, structure, or levels of detail. A response can also be technically valid while still being inaccurate or unsuitable for the business scenario.

That means testing an AI feature requires more than confirming that the API returns a successful response.

Depending on the use case, teams may need to evaluate:

Response relevance and accuracy
Output format and schema compliance
Behaviour with incomplete or unexpected inputs
Error handling and fallback scenarios
Latency under realistic workloads
Integration with downstream services

For structured workflows, output validation is particularly important.

If an AI model extracts information that will be written to a database, the application should verify the expected fields and values before accepting the result.

A successful model response is not automatically a successful business operation.

5. Deployment Is the Beginning of Monitoring

An AI feature that works during testing can behave differently once real users interact with it.

Input patterns change. Request volumes grow. Costs accumulate. External services experience latency or failures.

And model behaviour may change when providers release updates or application prompts are modified.

Production monitoring should therefore cover more than uptime.

Useful metrics may include:

Request success and failure rates
Response latency
Token usage and API costs
Output quality and task completion
User feedback
Fallback frequency
Business outcomes associated with the feature

These measurements help teams identify whether the AI functionality is delivering value rather than simply generating responses.

Monitoring should be planned before launch, not introduced only after users report problems.

Start With One Use Case, Not an Entire AI Transformation

One of the most practical ways to reduce integration risk is to begin with a limited, measurable use case.

Choose a feature with clear value, identify the systems it depends on, and define what success looks like.

Then validate the data, select an appropriate integration approach, test realistic scenarios, and establish monitoring.

Once the feature proves useful in production, expand from there.

This incremental approach gives engineering teams an opportunity to address technical constraints before they affect a much larger part of the product.

Final Thoughts

AI integration is a software engineering challenge as much as an AI challenge.

The model is only one component. Data quality, application architecture, security, testing, and ongoing operations determine how well the feature works in practice.

For a broader discussion of technical and organisational obstacles, including practical approaches to AI adoption, explore the original article by Alex Breeze, Head of IT at UNL Solutions:

Common AI Integration Challenges: What to Expect

What AI Actually Does in Logistics and Maritime Systems

UNL Solutions — Tue, 25 Aug 2026 15:32:40 +0000

When we talk about AI in software, the conversation often starts with the model.

In logistics and maritime systems, it often makes more sense to start with the data.

A logistics platform might have access to historical routes, traffic conditions, warehouse inventory, orders, vehicle capacity, and delivery windows.

A maritime system might be processing vessel telemetry, engine temperatures, vibration, fuel flow, weather data, ocean conditions, AIS data, and historical voyages.

AI becomes useful when those data streams can improve an operational decision.

Here are some practical examples.

Route and voyage optimization

Traditional routing already solves complex optimization problems. ML adds another layer by incorporating historical and real-time conditions.

On land, that can mean:

traffic + weather + capacity + delivery windows → route recommendation

At sea:

weather + currents + vessel performance + fuel consumption → voyage recommendation

The output isn't necessarily a fully autonomous decision. It can simply give operators better options based on more information than they could reasonably process manually.

Predictive maintenance

Vessels generate a lot of machine data.

Engines, pumps, compressors, motors, and generators can provide continuous signals such as:

vibration
temperature
pressure
fuel flow
electrical signatures

Instead of waiting for something to fail — or servicing equipment purely according to a fixed schedule — models can look for patterns associated with degradation or abnormal behavior.

The interesting engineering challenge is rarely just training the model.

You also need reliable telemetry, data pipelines, integration with existing systems, monitoring, and a useful way of delivering the prediction to the people making maintenance decisions.

Fuel and vessel performance optimization

Fuel consumption depends on a combination of vessel characteristics and operating conditions.

This is also an area where AI doesn't necessarily require a brand-new platform.

In our Marorka project, UNL engineers worked on an existing maritime solution for ship monitoring and route optimization. It's a useful real-world example of how software and data can be used to improve vessel operations without rebuilding the entire underlying system.

Project details:
Marorka: Optimization of Ship Monitoring and Routes

Demand and inventory forecasting

On the logistics side, forecasting models can combine historical demand with external signals to improve inventory decisions.

Better predictions can affect much more than stock levels.

They influence procurement, warehouse capacity, transportation planning, and ultimately the amount of working capital tied up in inventory.

Port and terminal automation

Ports introduce another interesting engineering problem because many independent processes have to coordinate in real time.

Think:

vessel ETA → berth allocation → crane scheduling → yard capacity → truck arrivals

When one variable changes, the rest of the plan may need to change too.

AI-based optimization can help continuously recalculate these decisions instead of relying entirely on manual replanning.

The model is only one part of the system

That's probably the biggest takeaway.

For logistics and maritime software, successful AI implementation depends on much more than choosing a good model.

You need data that can be trusted.

You need access to existing systems.

You need infrastructure capable of processing operational data.

And you need to integrate the output into workflows where it can actually influence decisions.

Our Head of IT, Alex Breeze, recently put together a deeper overview of AI applications across logistics, supply chains, ports, fleet management, and maritime operations.

Read the full article:
How AI Is Used in Logistics and Maritime Software

AI Staff Augmentation: When Does It Make Sense for Product Teams?

UNL Solutions — Tue, 18 Aug 2026 13:44:18 +0000

Building AI capabilities doesn't necessarily mean building an entire AI team in-house.

Your product might need a Data Engineer to prepare pipelines, an ML Engineer to get models into production, or an MLOps specialist to handle deployment and monitoring.

But you might not need all of them permanently.

That's where AI staff augmentation can be useful.

Instead of spending months recruiting specialized AI talent, product companies can bring external engineers directly into their existing teams. They work within your development process, use your tools and standards, and contribute to your existing codebase while you retain technical ownership.

When does this model make sense?

AI staff augmentation can be particularly useful when:

you're running an AI pilot without enough in-house ML expertise
your existing AI team has a temporary workload spike
you need a specialist for one particular AI feature
you're missing specific expertise for the next 3–6 months
your roadmap requires different AI skills at different stages

And "AI talent" isn't one role.

Depending on the product, you might need an ML Engineer, Data Engineer, AI/Prompt Engineer, MLOps Engineer, Data Scientist, or AI Product Manager.

That's also why simply hiring "an AI developer" doesn't always solve the problem.

But staff augmentation isn't always the answer

If you need someone to build deep institutional knowledge or take a permanent engineering leadership role, an in-house hire may make more sense.

And if you'd rather hand over technical ownership of a clearly defined project, outsourcing or a dedicated team could be a better fit.

The important part is matching the hiring model to the problem you're actually trying to solve.

Our author Anastasia Krivorotova explored the topic in much more detail, including the different AI roles, engagement process, benefits, alternatives, and what to look for when choosing an AI staff augmentation partner.

Read the full guide:
https://unl.solutions/blog-ai-staff-augmentation-product-companies/

AI Development Costs in 2026: What Actually Drives the Price?

UNL Solutions — Tue, 11 Aug 2026 14:35:36 +0000

AI can save businesses money. But building it isn’t free.

And the cost of an AI project goes far beyond model API fees.

Data preparation, infrastructure, integrations, engineering time, testing, security, monitoring, and ongoing maintenance can easily become a bigger part of the budget than the model itself.

The range is also huge.

Adding an AI-powered feature to an existing application is very different from building a production-ready AI system from scratch.

So what actually determines the price?

Some of the biggest factors include:

model choice and API usage
data quality and preparation
integration complexity
infrastructure and compute
security and compliance requirements
development and testing
monitoring and maintenance
expected usage and scale

We recently broke down these costs in detail, including typical price ranges and the less obvious expenses businesses often overlook when estimating an AI project.

If you're planning to integrate AI into an existing product or build something AI-first, here's the full breakdown:

AI Development Costs in 2026: Complete Pricing Guide

What Actually Separates a Good Developer Hire From a Bad One

UNL Solutions — Tue, 28 Jul 2026 10:56:01 +0000

Most engineering teams have a hiring process. Far fewer have a hiring philosophy.

That distinction matters more than it sounds. A process tells you which steps to run — post the job, screen resumes, do a technical round, check references. A philosophy tells you what you're actually optimizing for. And if you skip that second part, you end up with a process that looks rigorous on paper but consistently produces mediocre outcomes.

I've been thinking about this a lot lately, mostly because I keep seeing the same failure pattern repeat across teams of very different sizes: a company writes a job description for a "full-stack developer," interviews five people who can technically write full-stack code, hires the one who interviewed best, and then discovers three weeks in that the role actually required 80% backend depth and almost no frontend work at all. The new hire is frustrated. The team is frustrated. And the root cause was never the candidate pool — it was that nobody had defined the job before advertising it.

The problem isn't finding developers. It's defining the role.

Before any sourcing happens, there are two questions worth sitting with:

What will this person actually be responsible for in their first 90 days?
What specific gap in the team does this hire need to close?

If you can't answer those with precision, no amount of clever sourcing or interview design will save the hire. You'll end up screening for the wrong signal, because you don't actually know what signal matters.

This is also where the generalist-vs-specialist debate gets resolved in practice, not in theory. Small teams — say, under 20 engineers — usually get more value from generalists who can move across the stack as priorities shift. Larger orgs with well-established domains tend to need depth in a specific area more than breadth. Neither is "better." They're answers to different problems.

Sourcing: stop assuming the best candidates are job-searching

Here's an uncomfortable truth: the developers who'd be the best fit for your team are often not the ones actively applying to jobs. They're heads-down on something else, not browsing job boards, and would only consider a move for the right reason.

That changes where you should be looking:

GitHub — not for stars or follower counts, but for real contribution history to projects adjacent to your stack. It's one of the few places you can observe actual code quality and how someone collaborates in an existing codebase, before you've spent an hour of interview time.
Competitive coding platforms — useful less for the puzzle-solving itself and more as a proxy for speed, correctness under pressure, and comfort handling edge cases.
Bootcamp graduates — an underrated pool. At the junior-to-mid level, bootcamp grads frequently outperform CS graduates on practical, shipping-code skills, simply because their training was oriented toward building things rather than passing exams.

What technical interviews should actually test

Everyone tests for correctness. Fewer teams test for the traits that predict whether someone will actually be good to work with two years from now:

Ownership under ambiguity. When requirements are unclear, does the candidate ask who needs to be looped in, surface the risk early, and push for a decision — or do they quietly build the wrong thing and hope it's close enough?
Communication outside the engineering bubble. Someone who can't explain a technical tradeoff to a non-engineer will eventually become an invisible bottleneck, usually right when you need speed the most.
Product thinking. Does the candidate ask "why" before "how"? That single habit is a strong predictor of whether their code will solve the actual problem or just satisfy the literal spec.
Genuine adaptability. Not "will adopt any new tool immediately," but the more useful trait of evaluating new approaches critically and adopting them where they add real value — AI-assisted coding included.
Net positive contribution. The best hires make the whole team faster, not just themselves.

None of these show up cleanly in a whiteboard exercise. The better approach: give candidates a scaled-down version of a real problem your team recently solved, and evaluate how they approach requirements, tool selection, and tradeoffs — not just whether they arrive at a working answer. Looping in current team members to weigh in on the evaluation also catches things a hiring manager alone will miss.

A trial period is worth more than another round of interviews

Interviews are a simulation. A trial period is closer to the real thing. During it, pay attention to how someone handles ambiguity in practice, how they collaborate with people already on the team, and — just as tellingly — the kinds of questions they ask.

It's worth remembering this runs both ways: candidates are also evaluating whether your team is one they actually want to join long-term. Set a clear time box and explicit success criteria up front, so neither side is guessing at the end of it.

Onboarding is part of the hiring decision, not a separate step

A strong hire who gets a weak onboarding will underperform for months — and you may never be sure whether the person or the process was the problem. A reasonable baseline:

A structured first week covering tooling, codebase orientation, and introductions
Written documentation of workflows, deployment process, and coding standards
Pair programming with a senior engineer early on
Concrete, achievable first-sprint goals
Scheduled check-ins at 30, 60, and 90 days

The models: in-house, freelance, dedicated teams, staff augmentation, agencies

Which hiring model fits depends on your stage, your tolerance for coordination overhead, and how central engineering is to your product's value. A quick way to think about it:

In-house gives you the deepest alignment and control, but a senior hire can take three to six months to close, and turnover hurts more if institutional knowledge isn't documented.
Freelancers are a good fit for well-scoped, short-term work, but the low hourly rate can hide real management overhead.
Dedicated teams — a group of specialists from an external vendor working exclusively on your product — offer faster assembly and lower overhead than in-house hiring, at the cost of somewhat less direct control.
Staff augmentation slots individual contractors into your existing team under your direction — useful when you already have a strong internal team and just need more hands.
Agencies make sense when you want to hand off an entire, well-defined project with minimal internal management.

There's no universally "safest" option here. Early-stage validation tends to favor speed and flexibility; scaling after product-market fit tends to favor continuity and retained knowledge.

The mistakes worth naming

A few patterns show up again and again, even on experienced teams:

Rushing a hire under deadline pressure, and rationalizing compromises you'll regret in three months
Writing a job description for a "unicorn" instead of being specific about your actual top priority
Skipping technical vetting because a referral came from someone trusted
Optimizing purely for stack match while ignoring whether the person understands the product's direction
Moving too slowly and losing strong candidates to a faster-moving competitor
Advertising "full-stack" for what is actually a backend-heavy role, and being surprised by the resulting attrition

None of these are exotic. They're just easy to fall into when a hiring process is run on autopilot instead of intention.

I went deeper into cost benchmarks by region and seniority, and a full breakdown of when each hiring model actually makes sense, in the original piece here: How to Hire Software Developers: A Complete Guide for Product Companies.

Curious how other teams think about this — especially anyone who's run trial periods for engineering hires. Did it change who you ended up hiring?

5 Things Every Engineering Team Should Do Before Adding AI to an Existing Product

UNL Solutions — Thu, 02 Jul 2026 15:04:11 +0000

Artificial intelligence is becoming a standard part of modern software products. But while everyone is talking about AI features, many engineering teams are still asking the same question:

Where do we actually start?

One pattern we've seen across AI integration projects is that successful implementations rarely begin with choosing an LLM. They begin with understanding the product itself.

Here are five things every engineering team should do before integrating AI into an existing application.

1. Solve a Business Problem First

Don't start with ChatGPT, Claude, or Gemini.

Start with questions like:

Which manual process consumes the most time?
What frustrates users the most?
Which decisions could benefit from better recommendations?
Where do employees repeatedly search for information?

The best AI features solve existing problems instead of creating new ones.

2. Audit Your Existing Architecture

AI should become another service inside your architecture—not a separate product.

Before writing any code, review:

APIs and integration points
Authentication and permissions
Data sources
Logging and monitoring
Performance requirements
Rate limits and third-party dependencies

Strong architecture makes AI integration significantly easier.

3. Prepare Your Data

Even the best models can't compensate for poor data.

Ask yourself:

Is the data complete?
Is it consistent?
Can the model access the necessary business context?
Does sensitive information require masking or filtering?

Most AI integration issues are actually data quality issues.

4. Start Small

Many teams try to build an "AI-powered platform."

Instead, identify one workflow that can deliver measurable value.

Examples include:

document summarization;
customer support assistance;
internal knowledge search;
content generation;
intelligent recommendations.

A successful pilot builds confidence for larger initiatives.

5. Measure Business Impact

Shipping an AI feature isn't the finish line.

Track outcomes such as:

time saved;
reduced manual work;
faster response times;
improved customer satisfaction;
higher productivity.

If you can't measure the impact, it's difficult to evaluate whether the integration was successful.

Final Thoughts

Adding AI to an existing software product doesn't require rebuilding your entire platform.

The strongest projects start with clear business goals, solid architecture, clean data, and gradual implementation.

Engineering teams that approach AI as another component of their existing ecosystem usually achieve faster adoption—and better long-term results.

We've recently published a more detailed guide covering AI integration strategy, common implementation mistakes, architecture considerations, and practical recommendations.

👉 Read the complete guide here:

https://unl.solutions/how-to-integrate-ai-into-existing-software-products/

We'd love to hear how your team approaches AI integration.

What has been your biggest challenge so far?