Embarking on a generative AI project is a venture into uncharted territory. It’s hard, especially when you have never done it before. And even though the journey is filled with promise, it is also full of traps and obstacles — as evidenced by Gartner’s findings that only 53 percent of AI projects complete the transition from prototype to full production.
The failure of many AI projects to move beyond the prototype stage can often be attributed to a combination of factors, including insufficient project scoping, a lack of understanding of the technology’s demands, and inadequate stakeholder engagement. These challenges highlight the importance of a robust strategy that encompasses not just the technological aspects but also the alignment of the project with broader business objectives.
To successfully navigate the intricacies of generative AI implementation, organizations must adopt a flexible and adaptive approach, one that allows for iterative learning and adjustment based on ongoing feedback and evolving project needs.
In this article, we will show you some common traps you can fall into when starting a generative AI project and how to avoid them.
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Mistake 1: Premature Requirements and RFPs
Starting with a Request for Proposals (RFP) and defining use cases and requirements right off the bat can seem like the logical first step. Don’t be fooled, though. It’s a trap, especially if you’re new to generative AI.
First, your RFP is likely to miss essential elements simply because you’re unaware of them. Drafting an RFP at the outset without a foundational understanding of generative AI is akin to charting a map before knowing the terrain. You risk setting a course that overlooks critical opportunities or, worse, leads to dead ends—which, in our case, means burning your time and budget.
Second, your use case of choice, no matter how great it looks on paper, may be unviable or not cost-effective, considering current AI capabilities and your internal organizational conditions. The misalignment between expectations and reality can lead to wasted resources and disillusionment with the technology.
So, what to do instead of writing a detailed RFP?
Before drafting an RFP or cementing your requirements, participate in a workshop led by experts who have navigated generative AI implementations. Such sessions can uncover unthought-of considerations and enable you to formulate a strategic plan, including a realistic project roadmap and preliminary budget considerations. They will also help you ensure that your project is grounded in real organizational needs and potential AI capabilities, setting a solid foundation for your AI project.
Read more: Generative AI Workshops: Start Your Gen AI Project Smart
Mistake 2: Excluding End-users and Key Stakeholders
Let’s say you’ve moved past the initial planning and organized a workshop, tapping into the experiences of someone who’s navigated similar projects. Attending these workshops alone, based on the assumption that you know exactly what’s needed, is a pitfall.
One of the cardinal sins in any project—more so in one involving cutting-edge technology like generative AI—is building based on assumptions rather than on the needs and feedback of the end-users. The siloed approach can lead to a misalignment between the project’s direction and the practical needs of those it’s intended to serve, causing the project to falter at the assumption stage. This disconnect not only jeopardizes the project’s acceptance and usability but can also lead to costly revisions and rework down the line.
Instead, form an interdisciplinary team that includes end-users (if it’s an internal tool that you want to develop) or those who are in touch with users (e.g., the support team).
An interdisciplinary team, comprising members from various backgrounds and expertise, can provide diverse perspectives, leading to more innovative and practical solutions. Moreover, involving end-users in the development process fosters a sense of ownership and acceptance, significantly enhancing the likelihood of successful adoption and implementation. In other words, it ensures that the project addresses real needs and workflows, increasing motivation and buy-in from the people who will ultimately use the AI tools.
Read also: GPT-4o vs. GPT-4 vs. GPT-3.5 Comparison in Real-World Scenarios
Mistake 3: Preparing a Detailed Blueprint
So, you’ve moved past the planning phase, you’ve organized a workshop session involving interdisciplinary teams and end-users, and you would like to see the plan. How detailed do you want it to be?
If your heart beats faster when you think of a detailed blueprint describing everything down to granular details, if you feel excited when imagining a comprehensive roadmap for a project from start to finish detailing every requirement, process, and outcome with an almost religious fervor for detail — stop.
Generative AI initiatives are inherently exploratory and iterative. The initial hypotheses may fail. Your model of choice may not be technically feasible for your use case. Failures in performance can require iterative refinements or even a complete change in approach. Because of that, when working on generative AI-powered solutions, you need to be flexible and open to exploring different ideas and methods. A detailed blueprint is on the opposite side of this approach. It works fine for fixed-priced projects with minimal uncertainty. But it won’t work in the case of a Gen AI project.
Generative AI development is always an R&D project. It requires you to quickly verify hypotheses and iterate, working in an agile manner. A blueprint will not allow you for this flexibility. It’s like riding a freight train with many cars loaded with heavy raw materials — while you need a powerful, agile 4×4 vehicle that can handle rough conditions and get you anywhere you need.
Project Roadmap to the Rescue
Instead of scripting every move and more about setting a direction, you need a framework within which innovation can flourish. Instead of a detailed blueprint, you need a project roadmap.
A project roadmap provides enough structure to guide the project’s direction while allowing the flexibility to adapt to changes quickly. It allows for iterative development and continuous refinement, focusing on long- and short-term goals, technology recommendations, team composition, and key milestones. This adaptability not only reduces the risk of project failure but also ensures that the final product is more closely aligned with user needs and your business goals.
Read more: From Detailed Designs to Dynamic Directions. Blueprint vs. Project Roadmap for Product Development
Mistake 4: Skipping the PoC phase
A leap to full implementation assumes that the project’s initial assumptions are flawless and that the envisioned solution will seamlessly integrate into existing systems and workflows without significant challenges. A bold proof of self-confidence and… a risky proposition in the complex and unpredictable realm of AI.
The allure of rapid progress often leads to bypassing foundational steps: validating the feasibility of the proposed solution, identifying potential limitations that might not be apparent in the project’s planning stages, the technical hurdles, data quality issues, or integration challenges that could impede the project’s success.
Such an approach not only jeopardizes the project’s alignment with user needs but also exposes the organization to considerable financial and operational risks. If the AI solution fails to meet expectations or requires substantial modifications, the costs of making adjustments can be exorbitant, not to mention the potential delays and negative impact on stakeholder confidence. In extreme cases, these issues can culminate in project failure, undermining the investment and effort poured into the initiative.
A PoC is not merely a preliminary trial; it’s a strategic tool that offers a preliminary reality check for the envisioned AI solution. By validating the feasibility of the proposed solution in a controlled environment, a PoC helps:
- identify whether the conceptual promises of a project are attainable in practice,
- assess the technical viability of the AI model,
- Gather feedback from the end-users and adjust the solution, if necessary, to make sure that it addresses their actual pain points and preferences.
Without a PoC, you risk committing substantial resources to a full-scale implementation that may not meet users’ needs or expectations. This can result in costly adjustments, delays, and, in some cases, project failure.
How to succeed with a generative AI implementation?
Starting a generative AI project is a complex and dynamic endeavor fraught with potential pitfalls that can derail your efforts. From the temptation to set premature requirements to the oversight of skipping the validation phase, these common mistakes highlight the need for a thoughtful, flexible approach to AI implementation. The journey from prototype to production is not a straightforward path but a winding road requiring constant adaptation and learning.
To ensure success, it’s crucial to resist the allure of rigid planning and instead embrace a strategy that allows for exploration and iteration. Engaging with experts, involving end-users, creating adaptable roadmaps, and conducting a thorough Proof of Concept are key steps in this process. By avoiding these four common traps, organizations can better align their generative AI initiatives with real-world needs and business goals, paving the way for more effective and impactful solutions.
Navigating the complexities of generative AI requires a balance of strategic foresight and operational agility. By learning from others’ missteps and prioritizing flexibility and user engagement, you can set your project on a path to success, avoid costly errors, and maximize the transformative potential of generative AI.
