How to Balance Technical Feasibility and Business Value in AI Projects
With the transformation and advancement of AI technology, many businesses are highly interested in it, hoping to enhance their competitiveness by integrating AI. However, companies should not rush the process when adopting AI, particularly when choosing the right AI projects, as making the correct choice is essential. To help businesses better understand how to select appropriate AI projects, we can analyze from two key dimensions: technical feasibility and business value.
Technical feasibility VS Business value
- Projects with high feasibility but low business value are a good starting point for companies’ AI efforts, helping them gain experience and reduce the learning curve.
- Low feasibility and low business value projects should be set aside temporarily to avoid wasting resources.
- Low feasibility but high business value projects represent long-term goals, and companies should approach them gradually, building on existing AI work and creating a long-term plan, which requires careful strategic planning.
- High business value and high feasibility projects are rare and require optimal conditions and timing.
Assessing Technical Feasibility
Assessing technical feasibility requires businesses to have a solid foundation in AI project experience, including knowledge accumulation and engineering practices. Feasibility analysis can start with factors like data, current technological status, and the boundaries of AI’s capabilities. Quick testing is essential, breaking the project into modular components for validation. To reach project goals more efficiently, businesses may consider collaborating with a reliable team, avoiding the high risk of assembling a new team from scratch.
Understanding Business Value
On the business value side, the focus should be on cost savings, efficiency improvements, and revenue-generating potential. Evaluating business value involves multiple perspectives: productivity gains (including personnel efficiency and impacted user count), cost savings, new revenue streams, brand value, and return on investment (ROI). This requires managers to have a deep understanding of the business and industry, alongside a clear grasp of company operations and strategy, which tests their decision-making ability.
Therefore, when considering the introduction of AI models, managers should engage in in-depth discussions with technical leads or professional AI consultants to clarify the project’s positioning. Having experienced numerous AI projects, I would suggest that businesses prioritize cost-saving and efficiency-improving AI projects, as they are generally more controllable with less risk. These kinds of AI projects make it easier to manage progress and measure results. In contrast, revenue-generating projects often require market expansion, which may involve additional expenses. However, for businesses that already have traffic resources, leveraging this advantage to invest in revenue-generating AI projects is also a worthwhile option.
Throughout this process, managers need a sound understanding of AI technology and a clear plan for the business, business model, and overall strategy. The product roadmap and technology roadmap for the project must be clear to ensure progress in the right direction.
In summary, choosing the right AI project is crucial for a business's future development and strategic positioning. Thus, when investing in AI, companies should avoid rushing and ensure thorough consideration and evaluation. If managers have limited understanding of AI, seeking advice from professional AI consultants can accelerate the decision-making process and serve as an important safeguard.
