Don’t Fall for These 5 Anti-Patterns in GenAI Project Delivery
Have you ever wondered what makes a successful GenAI project? Is it the model, the data, the tech stack, or something else? At Creative Dock, we have been working on various GenAI projects internally and helping other companies use GenAI to optimise their businesses or launch innovative solutions. Along this journey, we have discovered that the most crucial factor for GenAI's success is often overlooked – the team crafting the solutions.
Author: Soheyla Mirshahi, Photos by: Midjourney, Creative Dock archive
The teams are the ones dealing with new tech, sorting through the noise, and finding the right path for their organisations. However, they also face many challenges that are not technical but more human. These challenges can affect the team's motivation, creativity, and productivity. This article will reveal these challenges and how to avoid them. We call them the “Five Anti-Patterns in GenAI Project Delivery That Set You Up For Failure.”
Anti-Pattern 1: Getting Paralysed by Overthinking and Over-defining
Despite the hype around GenAI, putting them into production remains tricky. Even if you have the support of the company's board, increasing their confidence in the investment is crucial. So, dive boldly into the first Proof of Concept (POC). Begin coding to test your ideas.
Don’t wait until you have all the details and answers. GenAI is changing fast, and you might miss the chance to innovate. Be ready to face the unknown and take some risks. Accept the messy and unpredictable nature of the initial POC as a natural part of the process.
Knowing everything is not the goal; it is building confidence by doing. For sure, your first POC will not be flawless, but it is the crucial step needed to build a foundation for future success. Be flexible and adaptable in your AI development, and avoid the anti-pattern of waiting until everything is perfectly defined.
Anti-Pattern 2: Assuming Your Awesome GenAI Project Speaks for Itself
You have completed the first Proof of Concept (POC) for your GenAI solution. Congratulations! But don’t stop there. After completing the project, don’t sit back and expect others to recognise its value. It is on you to move beyond the technicalities and communicate the value of your POC to others, especially the decision-makers in your company. They are not as familiar with the technical details as you are and are bombarded with AI hype daily. How can you make your POC visible and relatable? The answer is value-focused storytelling.
“You must craft a compelling narrative showcasing your AI innovation's strengths, utility, and real-world value. You need to explain what problem it solves, how it solves, and why it matters. You need to use clear, simple, and engaging language.”
While it may be tempting to dive into the complexity of the process to show how difficult it was and how much you suffered, resist that urge! Remember, your role is not just to showcase the AI’s capabilities. Instead, you have to make its use case understandable and compelling to a broader audience within the company. Keep the daunting details to yourself. Clean up any internal chaos before presenting the solution externally. Make the complexities appear simple, and only go into technicalities when specifically asked. Remember, your role is not just to showcase the AI's capabilities but to make its use case understandable and compelling to a broader audience within the company.
Anti-Pattern 3: Falling Into the Hasty Project Sequel Syndrome
You have done your first successful GenAI Project. Well done! Now, you need to take a strategic step back and reflect on what you have learned and how to improve. Don’t rush into the next project right away. Your team has collectively learned from the mistakes made in the initial phase, so it is time to acknowledge their effort and gather feedback and lessons learned. Conduct a detailed post-mortem meeting involving all team members, not just to release some steam but to structure and agree on the following project’s delivery process.
- Gather lessons, define processes, and share knowledge.
- Streamline the workflow from the discovery of the idea to development, evaluation, and delivery.
- Make everything transparent and well-defined, especially the success parameters of the projects and the scope of deliverables.
Remember, the first project was a learning experience. Cleaning up any internal mess and ensuring the accumulated knowledge becomes the foundation for a more organised and efficient process on the next project.
Anti-Pattern 4: Neglecting Team Harmony by Ignoring Role Dynamics
Teamwork makes the dream work, especially in GenAI. You need the right people in the right roles to make your GenAI solution successful. But who are the right people, and what are the right roles?
On the development side, there are often the following roles in a GenAI team: data scientists & prompt engineers, the IT dev team, and the product/project team. Each category has different sub-roles that require different skills and expertise. However, a crucial aspect to consider is the individuals for whom the solution is developed – the business owners.
The term "data scientist" often oversimplifies a diverse skill set. Different flavours of data scientists are needed, depending on the company's goals and the specific Proof of Concept (POC).
What kind of data scientist do you need, exactly?
Suppose you want to use pre-trained language models (LLM) for your GenAI solution. You can either use existing APIs that provide access to LLM or train or fine-tune your LLM from scratch. Depending on your choice, you will need different types of data scientists. If you must integrate LLM into your current systems, you will need data scientists with software engineering skills who can integrate LLM into your solution, often called ML engineers. If you train or fine-tune LLM, you will need ML researchers with profound learning and mathematics skills who can create and optimise LLM for your specific problem.
Your project managers have to understand GenAI solutions
You must also consider the roles changing or emerging in the GenAI era. For example, product managers need to know GenAI’s strengths and, more importantly, its limitations. Quality assurance does not just look for bugs but also tests the user value and experience of GenAI. Prompt engineers are a new role responsible for designing and optimising the prompts interacting with the GenAI models. You need to define these roles' responsibilities and skills and ensure they are aligned with the rest of the team.
“Having the right roles is not enough, though. You also need to have the right team harmony. You need to create a culture of collaboration, where each role respects and supports the others.”
Business owners play more critical roles than before.
Business owners are more vital than ever for GenAI projects. These projects are new and subjective, so they need constant input from business owners who understand the problem. To create a better GenAI solution, you need synergy, communication, and harmony among all roles.
Anti-Pattern 5: The Mirage Trap - Unmasking Unrealistic LLM Expectations
Large language models (LLM) are excellent tools with capabilities that are widely accessible and user-friendly, which makes them exciting for everyone to try out. Some examples are text-to-text and text-to-image LLMs, such as GPT and DALL-E models by OpenAI. Many companies organise hackathons to motivate employees to create something with LLM and solve a problem creatively. These fast prototypes can be inspiring and fun but can also be misleading if one is not careful about their capabilities. The key is to use them effectively, responsibly, and ethically. It is crucial to understand their strengths and limitations, design appropriate prompts and feedback mechanisms, fine-tune them on relevant data and tasks, and evaluate their performance and impact.
Final thoughts:
GenAI research and development is an exciting but challenging journey. You need to be bold and curious but also strategic and careful. You need to avoid the common mistakes that can derail your GenAI projects, such as overthinking, assuming your project speaks for itself, rushing to the next project without reflecting on the lesson learned, ignoring the team’s skill set and harmony, and falling for the mirage trap.
As we enter the new era of AI, we need to create a culture that supports GenAI innovation. We need to be adaptable, transparent, and eager to learn. We need to build GenAI teams that are strong, diverse, and collaborative, and most importantly, we need a team to turn our GenAI ideas into real solutions that make a positive difference in the world.