Build an AI roadmap that actually delivers value

What a year in the trenches building with LLMs taught me about delivering value with AI

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Artificial intelligence (AI) is rapidly becoming an integral part of modern engineering. From automating mundane tasks to driving groundbreaking innovations, AI offers unprecedented opportunities for organizations to gain a competitive edge.

I’ve spent over a year in the trenches building with LLMs and even longer working with applied ML. My company has shipped nearly 100 AI agents into production for our healthcare customers, achieving a 95% reduction in operational expenses and a 140% boost in staff productivity. We were able to achieve this by identifying the areas where AI could create the most value for our customers, pinpointing high-value opportunities.

Identify areas of impact

To maximize impact when building your AI roadmap, focus on areas where AI can truly move the needle for your customers and your business. Consider using a structured framework to systematically identify high-impact areas and prioritize AI opportunities based on customer needs, business value, and technical feasibility. This approach ensures your AI roadmap focuses on high-impact projects that align with your strategic objectives and timelines.

Start with projects that have clear ROI and potential for quick wins to build momentum for your AI initiatives and demonstrate value to your customers.

I recommend focusing on the following key areas where AI can deliver a significant impact:

Providing creative AI value

Developing AI systems that can generate content or solutions customers can’t easily create on their own is a great way to provide value to them.

Potential implementations include:

These tools significantly boost your customers’ output and creativity, allowing them to produce high-quality work faster and more efficiently.

Help synthesize information for your customer

Using AI to curate, synthesize, and surface relevant information from vast datasets can improve customer decision-making and reduce time spent searching for information.

Potential implementations include:

Improve process efficiency

Automating procedural tasks capable of independent action and decision-making within defined parameters frees up customer time to focus on higher-value activities.

Potential implementations include:

Combine intelligence for complex scenarios

Combine human and artificial intelligence to enhance your customers’ decision-making and problem-solving capabilities in complex scenarios.

Potential implementations include:

These augmented intelligence initiatives leverage the strengths of both humans and AI, leading to superior outcomes for your customers in complex tasks.

Understand patterns of implementation

As you identify areas where AI can make a significant impact, it’s crucial to understand the high-level implementation patterns that can guide your roadmap. Recognizing these patterns helps in selecting the right approach for your specific needs, ensuring efficient resource allocation and maximizing the value delivered.

Here are the primary patterns to consider when building your AI roadmap:

Retrieval-Augmented Generation (RAG)

(Source: [Retrieval Augmented Generation (RAG) for LLMs Prompt Engineering Guide](https://www.promptingguide.ai/research/rag))

RAG is a pattern that combines the capabilities of large language models (LLMs) with search functionalities. It involves retrieving relevant information from your data sources and using it to generate more accurate and contextually appropriate responses.

If your goal is to enhance information retrieval, provide detailed answers to user queries, or generate content based on specific data, RAG is an effective approach. For example:

Implementing RAG can be facilitated by tools like vector databases for efficient search and frameworks that integrate retrieval with LLMs.

AI Agents

(Source: [LLM Agents Prompt Engineering Guide](https://www.promptingguide.ai/research/llm-agents))

AI agents are systems that can perform tasks autonomously by making decisions and executing actions based on predefined objectives and real-time data. They can handle both attended tasks (requiring human oversight) and unattended tasks (operating independently).

For initiatives aimed at workflow optimization, process automation, or complex decision-making, AI agents are the go-to pattern. They excel in scenarios like:

Building AI agents may involve using platforms that support agent orchestration, integration with various data sources, and capabilities for monitoring and adjusting agent behavior.

Hybrid Approaches

Sometimes, combining patterns like RAG and AI agents yields the best results. This hybrid approach leverages the strengths of both patterns to address more complex challenges.

In cases where you need both advanced information retrieval and autonomous task execution a hybrid approach is beneficial. For example:

Rapidly deliver value

It’s crucial to quickly demonstrate the value of your AI initiatives to your stakeholders and customers by following these guiding principles:

Start simple

When implementing AI solutions, simplicity is key to providing immediate value without unnecessary delays or costs. Here’s how you can achieve this:

Build, measure, and learn

After initiating your AI projects with simple implementations, it’s crucial to adopt an iterative approach to refine and improve your solutions. This cycle of building, measuring, and learning ensures that your AI initiatives continue to deliver increasing value over time. Here’s how to integrate this mindset:

Manage expectations

As you transition from proof-of-concept to production, it’s crucial to manage the expectations of all stakeholders – including users, team members, and leadership – to build trust in your AI-powered systems. Proper expectation management ensures that everyone understands the capabilities and limitations of your AI solutions, which is essential for delivering consistent value.

Prepare for the future

While delivering immediate value is essential, it’s equally important to design your AI initiatives with the future in mind. By anticipating changes and building adaptability into your systems, you ensure that the value you deliver today can be sustained and enhanced over time. Here’s how:

Close the talent gap

The rapid rise of AI has created a significant demand for skilled AI engineers and data scientists. Building an AI-ready team requires a multi-pronged approach.

Hiring applied AI talent

When hiring for AI talent, it’s crucial to understand the distinction between research and applied AI engineering. Researchers focus on pushing the boundaries of AI, developing new algorithms, and publishing academic papers. Applied AI engineers, on the other hand, focus on taking those advancements and translating them into real-world products. They are the bridge between cutting-edge research and practical implementation.

(Source: The Rise of the AI Engineer - by swyx & Alessio)

What to look for in candidates

When evaluating candidates for AI engineering roles, prioritize the skills that enable them to build and ship AI-powered products.

The Rise of the AI Engineer emphasizes the importance of strong software engineering skills. Look for proficiency in languages like Python and JavaScript, experience with software development best practices, a deep understanding of data structures and algorithms, and a knack for building scalable and maintainable systems.

While theoretical knowledge is valuable, prioritize candidates with hands-on experience using popular AI tools and frameworks. This includes familiarity with:

Seek candidates who are passionate about building products and solving real-world problems with AI. Look for a demonstrated ability to translate AI concepts into tangible user benefits.

Lastly, prioritize candidates who are adaptable, eager to learn new technologies, and can keep pace with the latest advancements in the field.

By focusing on these practical, product-oriented skills, you can build a high-performing AI team capable of delivering real value to your organization.

Upskill your existing engineers

As AI becomes increasingly integrated into various aspects of software development, the lines between “AI engineering” and “software engineering” will blur.

It’s important to cultivate a basic understanding of AI concepts and principles across your entire engineering team, empowering everyone to contribute to the success of your AI initiatives.

Provide opportunities for your current engineers to upskill and learn AI concepts and tools.

Use workshops and hackathons as learning devices

To help upskill the talent you already have, organize hands-on workshops and hackathons focused on AI. Bring in external AI experts or leverage internal knowledge to lead these events, focusing on real-world applications relevant to your business.

Encourage cross-functional teams to tackle actual business problems using AI during hackathons, providing valuable learning experiences and the potential to accelerate the AI roadmap. By showcasing successful projects company-wide, you can inspire and motivate other team members to engage with AI technologies.

Rotation and hands-on experience for upskilling

Regularly cycle team members through AI-focused projects or teams, allowing them to gain hands-on experience with various AI applications.

This rotation program serves multiple purposes: it provides practical, real-world experience with AI technologies, exposes engineers to different use cases and challenges, and helps disseminate AI knowledge throughout your organization.

As engineers work on diverse AI projects, they’ll naturally build a broader skill set and a deeper understanding of how AI can be applied to solve business problems. Moreover, this rotation strategy can help identify hidden talents and interests among your engineers, potentially uncovering AI champions who can further drive innovation in your organization.

Final thoughts

Building and executing an effective AI roadmap is an ongoing journey that requires careful planning, experimentation, and adaptation. By embracing a structured approach, prioritizing practical implementation, and remaining adaptable to the ever-evolving AI landscape, engineering leaders can successfully navigate the challenges and opportunities of AI adoption, leading their teams and organizations toward a brighter, AI-powered future.


This article was originally published on LeadDev.com on Oct 14th, 2024.

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