3 Stages for Integrating AI into Your Products, Workflows, and Technology
When integrating AI into your business, product, and UX development processes, the best approaches combine research and experimentation. As you uncover, explore, and test opportunities to drive impact with AI, you must remain intentional and open-minded at every step. Large language models (LLMS), in particular, are easy to connect to applications. On the other hand, figuring out how well they perform in specific situations and optimizing their performance is complex and unpredictable.
Below is the three-stage process Robots & Pencils uses to solve these problems and align leading LLM technology with your strategic goals. These stages offer a well-planned trajectory for integrating AI while rapidly de-risking efforts and accelerating return on investment.
Stage 1: AI Strategy and Planning
This stage involves learning, understanding, and aligning stakeholders around your top AI use case or product opportunity. Clients will undergo a guided discovery process. Specifically, you and your teams will consider what product or process you want to optimize. You’ll discuss short- and long-term goals and decide how to measure success. You’ll also identify known risks. With this information, you can begin to select and prioritize features for your AI-based product.
At this point, our consultants support you in thinking about change management. Addressing this early will help employees stay aligned and on board with integrating AI into their workflows. Proper change management prevents changes from seeming too abrupt. It also alleviates fears about AI and its implications for your team’s jobs.
You can often complete the planning stage in a couple of weeks. However, expect to return to it regularly for different applications and business opportunities.
Stage 2: Risk and Solution Exploration
In stage 2, you explore and address risks to pave the way for a smooth road ahead. Here, we help you build out and determine how AI integrates and engages your technologies and systems. This phase focuses on defining the UI framework, platform, prompts, tech, data, sources, and training models. We cover everything you need for building a new product or integrating AI into an existing system. As we analyze front- and back-end requirements, our designers create illustrative flows for the UI. At the same time, our engineers experiment with conversational prompts. The goal is to determine what best generates the desired conversational flows.
Together, we will locate essential tools, technologies, and integrations we can leverage to build the platform. We will identify data sources, ML models, and custom training needs. This stage also covers evaluating tools and platforms that may accelerate learnings or actual product-build efforts. We also collaborate with you to identify industry-specific regulations and other business risks. As needed, we consult legal or compliance teams to de-risk these items ahead of development and launch.
Stage 3: Prototyping and Development
In this stage, we bring your ideas to life with a functional AI-powered prototype. Here, we design and build your Proof of Concept, connect integrations, and measure and test efficacy.We focus on the experience first and iteratively refine AI prompts. From there, we measure the consistency and quality of the AI output and perform usability testing.
To provide an experience you and other stakeholders can easily interact with, we prototype the key UI features that govern the expression of the LLM. These features can include chat, visualization, and video. On the back end, we connect the prototype front end to the LLM, data sources, and other functional integrations. This period is also an opportunity for building a road map with project findings, prioritized features, and remaining risks.
Keys to Successfully Integrating AI into Your Tech
Applying design thinking to your LLM project is imperative to success in every stage. In design thinking, you empathize with and prioritize user needs in selecting and defining product features. This approach ensures that the resulting product aligns with actual use cases and directly addresses user challenges and desires.
Another vital step is de-risking features to identify potential pitfalls or challenges early on. Using Agile development is also valuable. Agile installs a safety rail for your feature development. It enables incremental improvements, feedback loops, and adaptability so that your AI prototype evolves efficiently and effectively to meet the desired outcomes.
In all, the process of introducing AI will vary for everyone. Still, by following a systematic framework like the one above, your company can better navigate the complexities of AI implementation. Using a proven framework will ensure a strategic and effective deployment of artificial intelligence technologies for your employees and customers.
Ready to get started on your LLM project?
Learn more about our AI & Data Science practice online, and email us at hello@robotsandpencils.com!
