When everything is possible, it can be nearly impossible to sort the profitable from the merely interesting. Today’s fast-evolving AI models present near limitless potential, but they lend no clues as to which action to take first—let alone which action may most benefit your company. Prompting the model to divulge these insights falls short as broad direction commonly leads to missed or malformed targets.
Like any technology, the secret to reaping generative AI’s benefits is rooted in human business acumen. The first and most crucial step is to seek out ideas where the organization will benefit most, followed by:
- Matching the technology’s strengths to those needs
- Developing a plan to overcome the technology’s shortfalls
- Conducting a cost vs. benefit analysis
- Designing a strategy for implementation, provided the cost analysis deemed it worthy of pursuing.
From the high view, these steps appear straightforward, perhaps even simplistic. In truth, they are not. Strong business acumen must guide all these steps so that the results of each are outcome-driven and expectations are tied to reality.
Drawing from the experience of others within an organization, among competitors, or throughout an industry is also invaluable. Partnering with a solid and highly experienced company, such as Mission Cloud, to help you plan and execute AI projects pinned to desirable AI outcomes is critical, especially in the early days of AI deployments.
Mission Cloud works alongside your company team to fine-tune what the model produces. This is the most efficient means to produce responses according to your organization’s business needs and targeted outcomes.
Tips for identifying high-value AI use cases
First, be specific in everything from the model tuning to the prompt engineering because although AI is a generalist, it performs best when it’s tuned and used as a specialist or team of specialists focused on a specific task.
Here is a short list of top considerations for identifying use cases with high returns.
- Optimization of any process. Focus the AI model on evaluating and improving workflows, processes, and policies. That includes AI development and implementations too. For example, MLOps is essential to optimize AI operations. For another example, AI in FinOps can greatly reduce cloud costs.
- Document processing of any kind. This is a potential goldmine in mined information, key negotiations, contract terms and perils, instantly created or updated content in multiple languages, and more. Use AI applications to manage and leverage all documents from legal discovery processes to self-help repositories, technical documentation, HR documentation, contract management, and any other document work.
- Elevating Customer Experience. Generative AI-based chatbots can do far more than lesser natural language processing chatbots. When coupled with AI agents, these chatbots can eventually perform actions too like automatically repairing a customer’s device or designing a customized insurance or banking plan. This type of use case can often elevate brand loyalty as well.
- Manufacturing. AI models can be extended into the real world by integrating with other technologies such as computer vision, robotics, autonomous AI agents, and mobile apps. Consider AI assistance in product development, production, quality control, packaging, and shipping.
AI applications are endless, but the best all have one thing in common: a solid, measurable, and planned business outcome. Whether you’re just getting started, or ready to scale your foundation model’s infrastructure, Mission Cloud can help transform your business with generative AI.