There has been a huge surge of interest in Generative AI in recent months. Generative AI is being used by businesses to automate workflows, create content more efficiently, and support improved decision-making. However, most organizations are falling into the trap of running independent experiments that are neither strategically planned nor scalable. This is typically due to a lack of an adequate development roadmap to guide these organizations in their Generative AI investments.
An effective Generative AI development roadmap provides direction and allows for deploying early-stage curiosity and innovative ideas into long-term, positive impacts on the organization.
Identify the Problem to Solve
The most successful AI projects are generated with clearly identified business drivers. Rather than consider “How can we implement generative AI?” consider the question “Where do we lose time, money, or opportunity?“.
For instance, marketing departments may be experiencing poor content production speed; customer service departments may be inundated with repetitive inquiries. Generative AI can meet both of these needs, provided that the type of need is clearly defined at the outset, especially when leveraging generative AI development services tailored to specific use cases.
The outcome-based approach to your strategic plan will keep you ahead of any “hype” cycle.
Evaluate Your Readiness Level
Before proceeding with AI implementation, it’s very important to assess your company’s readiness for AI implementation. This does not require a formal assessment but rather a realistic view of your organization’s current capabilities.
Data is usually the most difficult part of the equation. If you cannot properly access and utilize your organization’s internal knowledge, you will not have the data to produce good results from even the most advanced AI models. Having sufficient infrastructure that allows systems integration and development of APIs and moving data securely will also be critical to success.
Your team also has a significant impact on the success or failure of your implementation strategy. Your team members must know how to effectively utilize AI tools; without that knowledge, the processes you have put in place will fail. Your plan will demonstrate both what is possible to accomplish using AI technology at this time and what your company will be able to achieve using AI technology now.
Focus on a Few High-Impact Use Cases
Examine minimal use cases with potential significant benefit to your company’s overall growth and sustainability.
Many businesses are unsuccessful in developing a comprehensive AI strategy because they try to implement too many things at once, leading to too much variability and making it difficult to focus their efforts on any one task or function.
Early success will demonstrate return on investment (ROI) for those people involved in the early implementation of those use cases. As people see positive results (e.g., time savings and quicker turnaround times), it creates momentum for the expanded use of AI and therefore encourages individuals and organizations to use more AI in other ways.
Eventually, businesses will expand to use more sophisticated and strategic use cases. However, the roadmap for evaluation should continue to always start with a focus instead of a broader reach or scale.
Selecting the Correct Complexity Level
Not every organization requires a unique solution; the majority of companies prefer to work with existing technologies (or application programming interfaces).
The primary purpose during the early phase is not necessarily characteristically high-performing or highly advanced artificial intelligence (AI) applications; however, instead, they solve known business issues in a timely manner. In reality, many AI applications have been successfully implemented by integrating an existing pre-trained model with some of the internal organization’s data rather than building a model from the ground up.
When a company becomes more advanced, they typically transition to developing customized solutions instead of taking this approach; however, they generally find that its organization is better off in the early phases of development with simple solutions.
Building Your Roadmap in Phases – Not All at Once
Building a strong roadmap is an iterative process that starts small with exploration and experimentation, resulting in prototype projects that will demonstrate various ways of validating whether prototype projects work “in practice”. Prototype projects can then be scaled up into “real implementation” (i.e., the scope or breadth of the prototype project has increased) with a goal of optimising efficiency (cost vs. benefit) and the effectiveness of the system (the system is performing its intended function).
Your organisation has identified several prototype projects that were successful prototypes, which can now be implemented as “real projects” (as opposed to prototypes), which can then be used as the basis for scaling into new “real implementations” across your organisation.
Through the use of multiple phases of implementation (which includes the deployment of these new prototypes), you will mitigate risks during the implementation of future phases, as your phases will be built upon a strong foundation and will help create a cohesive product across your organisation.
Safeguard Against a Lack of Governance and Accountability
Organizations must not ignore new risks introduced by generative AI. Generative AI outputs can be inaccurate, biased, or inconsistent. Additionally, there are potential issues regarding data privacy and compliance.
As such, governance must be included in the roadmap from the start. This does not mean causing delays in the speed at which innovation occurs, it simply means that there need to be rules in place. Human oversight, clear policy definitions and ongoing monitoring are all critical parts to ensure that AI is utilized in an ethical and safe manner.
Prioritize Adoption
Without employees using the most sophisticated AI tools, no matter how good they are, adoption becomes one of the biggest challenges for AI solutions to be successful. People need to see that AI solutions are tools to enhance their jobs and make them more efficient, rather than threatening them. Training, communication and advocating for the internal use of AIs are also critical in this area. If employees feel empowered by AIs instead of being displaced by AIs, they are much more open to change.
To be successful, a roadmap must treat adoption as a central element and not an afterthought.
Track Results to Sustain Momentum
Track your performance so that you can keep that energy flowing. To sustain your momentum, you need to measure how well you are doing at achieving that energy level. You do not have to get very detailed when measuring; however, you do want your data to provide you with a reference point of success.
Track your success with Artificial Intelligence (AI) by measuring your success through improvements in efficiency, reductions in operational costs, and/or improvements in customer experiences. These data points will give you evidence to justify any future investments to support your ongoing plan for growing your business.
An initiative that has received a lot of initial support can become unsupported very quickly if it is not the subject of continuous measurement.
Get Prepared for a Change in Your Plans
Generative AI is developing rapidly; therefore, you should pay close attention to these advancements as they arise. What is currently effective may become different within a year or less.
Companies that have been most successful continuously evaluate their existing priorities, explore new technologies, and quickly respond to marketplace changes. When establishing or revising an organization’s current roadmap, the organization should consider using the roadmap as a living strategy for its organization rather than viewing the roadmap as a permanent document.
Final Thoughts
Combining all of the ideas above leads to the final thought on moving forward with your organization’s generative artificial intelligence initiatives. It is very simple: Build a generative AI strategy, follow that strategy, and don’t get distracted by what other companies are doing with generative AI until after you have successfully implemented your own generative AI strategy.
Once you have built your own generative AI strategy, you can begin to explore opportunities to leverage generative AI technologies in ways that will create new business value for your organization, your customers and their customers. You will be able to do this by taking detailed notes about what happens as you build out your generative AI strategy and documenting the results of these efforts for future reference.
Building a generative AI strategy is crucial to enabling organizations to become leaders in their application of generative AI technology.
Author Bio
Yuliya Melnik is a technical marketing writer with a strong focus on AI, software development, and innovative digital solutions. She enjoys breaking down complex technical concepts into clear, practical insights for businesses and tech enthusiasts.
