When management pushes for fast AI implementation—whether to improve productivity or roll out new AI-driven projects—it’s easy to get caught up in the rush.

But rushing things can lead to problems if quality takes a back seat.

Building reliable AI systems requires careful planning and a solid strategy. You’ll likely face challenges like a lack of in-house expertise, which makes it tough to set realistic goals. Inadequate infrastructure can also slow progress and complicate the process.

In this article, we’ll share some practical tips and strategies that have helped our customers successfully navigate these challenges.

‍

Implementing AI comes with its own set of challenges. The two main ones are:

1. Limited In-House Expertise: Navigating AI development is challenging without the right skills, especially since best practices are still evolving. Product teams often find themselves struggling to set realistic goals or test an AI idea — and with good reason — we get new model and technique advancements almost weekly, and figuring out what works well can be tough without the right strategy. On top of that, engineering teams need to embrace new methods and adapt to AI-specific practices that differ from what they’re used to with software engineering. If your team lacks the right expertise, you’re likely to hit roadblocks that slow you down and hurt your bottom line.
2. Lack of Infrastructure: Working with LLMs can be unpredictable, and you need a lot of testing to ensure the system performs reliably in production. To make this work smoothly, your company needs the right infrastructure and tools. With the right setup, your teams can work more effectively—product teams can test prompts, engineers can handle integration and improvements—and you’ll be able to build and launch faster. Without the right infrastructure, you could encounter long delays, increased costs, and uncertainty about the AI system’s performance in production.

If you don’t address these challenges from the start, you’re going to face more obstacles and slowdowns. It’s essential to equip your implementation team with the right infrastructure, best practices and know-how.

We’ve worked with thousands of companies that have successfully implemented AI systems, and we’ve seen a common pattern emerge.

The good news? We’re here to help you overcome these challenges.

Whether you need a partner to help design the system architecture or to provide the tools and infrastructure to move quickly, we’ve got you covered.

In the next few sections, we’ll share some best practices—free of charge—to help you get started on the right foot.

‍

The timeline for AI implementation can vary by project, but there are key stages that every team must cover to build reliable AI systems. Below we cover each of those stages.

1. Ideate Phase

Your first step is to identify where AI can truly add value to your product or service. In this phase, you’ll want to bring everyone together — Product Managers, Engineering Leaders, and SMEs (Subject Matter Experts).

In this pahse, you’ll need diverse perspectives to help you map the entire user journey, instead of zeroing in on isolated touchpoints. By the end of this phase, you should have a solid list of ideas ready for prototyping and validation.

2. Experiment Phase

With your ideas in place, it’s time to start withexperimentation. This is where the real exploration begins, as you start turning concepts into tangible results.

Product Managers and Engineers should collaborate closely to build small Proofs of Concept (PoCs). This phase is critical for testing different models, techniques, and architectures, especially given how unpredictable LLMs can be.

Have in mind that the experimentation phase isn’t just about choosing the right model; it’s also about exploring various prompt techniques, like retrieval-augmented generation or chain-of-thought prompting. These techniques can have a significant impact on your AI solution’s performance, cost, and speed.

The goal is to refine your approach, and tools like Vellum are incredibly useful during this process, allowing you to iterate quickly without putting too much strain on your technical team.

This phase is all about finding the best path forward before you commit to larger-scale projects.

3. Prioritize Phase

After your experiments, it’s time to decide which AI projects are worth taking to the next level. At this stage, you’ll evaluate the PoCs based on feasibility, impact, and resource requirements. This means considering not only which projects are technically viable but also which ones offer the highest potential value for your business.

You’ll want to weigh how confident you are in the feasibility of the project, how much value it could bring to your customers, and whether the work you’re doing can be reused in other areas. Additionally, you’ll need to consider the latency of the solution—how quickly it needs to operate—and the cost implications.

The goal here is to make sure the most promising initiatives get the green light and have the best chance of success.

4. Evaluation Phase

Before any AI system can go live, it has to go through thorough testing to make sure it’s up to par in terms of performance and reliability. This is one of the most crucial steps in the AI implementation process because it’s where you find out if your AI system is truly ready to handle real-world challenges.

Before deploying your AI system, evaluate it on key metrics (e.g. accuracy, latency, fairness, factuality, context retrieval). Here are some common strategies when it comes to evaluation:

• Real-World Data Testing: Create and expand a validation set that mirrors the inputs your AI system will encounter in production.
• Evaluation Tools: Use tools like Vellum’s evaluation framework to run large-scale tests, providing consistent and repeatable comparisons across different models and configurations.
• Iterative Testing: Continuously test and refine your models and prompts, ensuring ongoing improvement and early detection of issues.
• Guardrails and Inline Evaluations: Implement automated checks to monitor for specific problems, and embed inline evaluations to catch errors in real-time, adding an extra layer of security.
• LLM-Based Evaluation: Use another AI model to evaluate your system for more subjective criteria or to compare different output options for the best result.

5. Lifecycle Management Phase

The Lifecycle Management Phase is all about ensuring that your AI system remains robust, reliable, and effective long after it’s been deployed.

A solid infrastructure is essential for continuously managing the system, as AI development is often unpredictable.

You need to maintain clear version control to handle updates and enable rollbacks if needed. Continuously track key metrics, set up alerts for issues, and gather user feedback to identify areas for improvement. Make iterative updates based on feedback, testing them thoroughly before deployment. Ensure stability with regression testing, keep detailed documentation to aid knowledge sharing, and plan for scalability to handle increased loads as your system grows.

6. Continuous Improvement Phase

AI development is an ongoing process that requires continuous improvement.

This phase involves regularly reviewing performance metrics, gathering user feedback, and iterating on the AI system to enhance its performance.

For example, if an AI-powered customer service chatbot frequently provides incorrect responses, capturing explicit feedback from users (like a thumbs-up or thumbs-down on responses) allows the system to learn which answers are effective and which are not. Implicit feedback, such as the frequency with which users need to ask follow-up questions, can also indicate areas where the AI's responses need refinement.

You need to set up feedback loops to continuously capture this information and make necessary adjustments, ensuring the AI system remains effective and aligned with user needs as new data and insights become available.

‍

Cost is another crucial factor that companies need to consider when planning their AI implementation.

The cost of building an AI project can vary widely depending on the size of the project, the teams involved, feature complexity, data quality, how well it integrates with existing systems and time spent on evaluation to meet the desired quality threshold. According to this article, expenses can range from as low as $5,000 for simple models to over $500,000 for more complex solutions.

Using a framework can make the process faster and more cost-effective.

‍

When it comes to AI implementation, one of the key decisions you'll need to make is whether to build your AI system from scratch or use an existing framework.

Choosing an implementation framework can tackle the two biggest challenges companies face today: expertise and infrastructure, which are crucial for reliable and fast development.

Implementation frameworks like Vellum are uniquely positioned to tackle the biggest challenges in AI development today—because solving these issues is what we focus on every day. The tools we build around these best practices are designed to stay current with emerging technology, which, as we all know, is constantly evolving.

We understand that keeping up with everything is tough, alignment is even more challenging, and building the right set of tools takes time.

That’s why we built a complete AI implementation framework that helps engineering and product teams build AI systems quickly and reliably. We’re also committed to being a hands-on partner for each of our customers, offering dedicated support to get their AI solutions into production faster.

To learn more and get expert advice — book a call with one of our experts here.

There are some instances where you’d want to outsource your AI implementation team, and in that case you can work with AI implementation agencies.

‍

If you're looking for additional support, working with specialized AI implementation agencies can be a great option. Two agencies that we highly recommend are Left Field Labs and Codingscape.

Left Filed Labs are a mixed team of engineers, designers, strategists, information architects and creative technologists who have built software solutions for Amazon, Starbucks, Cisco and DataRobot. Left Field Labs takes a customized, collaborative approach to every AI project, guiding you from discovery to deployment to meet your unique needs.

Codingscape can lower the time you need to publish your AI initiative. They can assemble a senior AI software development team you need in 4-6 weeks. Zappos, Twilio, and Veho are just a few companies that trust them to build software.

There are plenty of AI implementation agencies out there, so it’s important to evaluate them based on the expertise they offer and the tooling they use or can build for you.

‍

Getting your AI implementation strategy right is not just about moving fast—it's about moving smart.

By focusing on experimentation and evaluation, staying current with best practices, and using robust implementation frameworks like Vellum, product and engineering teams can build reliable AI systems more efficiently.

Here are some other resources that might be helpful:

• Prompt engineering for Claude models
• Prompt engineering for improving GPT model output
• Evaluating RAG systems
• How to solve for hallucinations