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Finding AI Use Cases: The AI Booster Cycle

Applying AI seems like a no brainer to most organizations. Smart use of AI improves efficiency, increases product quality and boosts customer and employee satisfaction. However, almost half of those who have not yet deployed AI report unclear use cases as the primary reason. Well, we feel you, the problem is real. Whether you’re looking to boost productivity or deploy AI to your product or service, it requires knowledge and experience to determine the correct approaches. And in this fast moving field, first-hand experience is in short supply.

In this blog post we’ll introduce our simple steps for finding AI use cases, the ABC of AI deployment; the AI Booster Cycle! There are three steps to follow, one after another, and they form a cycle that feeds itself. After a round or two you’ll know how you should be using AI. It’s effective.

The cycle cuts through unnecessary bureaucracy, preliminary data readiness evaluations and what not. The point is to learn by doing. It doesn’t matter if you don’t have a fancy AI strategy yet, the ABC might as well be it. After all, it addresses an important problem (like a good strategy should do): figuring out how to deploy new transformative technology, and it’s practical to apply (like a good strategy should be).

We argue that a key factor is to educate people at every level of the organization about AI, to let them experiment with different tools and AI methods, and to selectively bring ideas into prototypes. After all, the problems of daily work are different at every level of the organization, and we should aim to maximize the benefits AI can bring. Everyone deserves the boost in productivity, quality and work enjoyment that AI can bring. Let’s see how the ABC works.

The ABC process to find AI use cases and iteratively improve them consists of three steps: Educate, Empower and Experiment. Together, they form a cycle that feeds itself and allows the organization to discover more productive AI use cases over time. Below we detail each step.

AI Booster Cycle

First, a quick note. We use the term AI liberally here, to mean any machine learning approach. In the current jargon this is often associated with generative AI, like large language models and multimodal vision-language and speech-language models. Many high-quality generative models, such as GPT models, are readily available, and make for a good starting point for discovering AI use cases!

The ABC steps

If you are looking to kick-start your first AI project, it’s best to start with education!

Educate: Laying the Groundwork

The first step in our ABC cycle is "Educate". Education is the foundation upon which successful AI deployment is built. It’s not just about teaching the necessary basics of AI and its potential applications, but also setting the stage for how your company shares experiences and knowledge.

  • Understanding AI’s Capabilities: Education is about demystifying AI. This includes explaining the fundamentals of AI technologies and how they can be applied in different scenarios. Knowing what AI can and cannot do is crucial in setting realistic expectations, for example when choosing between retrieval augmented generation (RAG) implementations.

  • Technical Training: Methods like prompting and use of specialist tools require practice to get the gist of it. With expert support you will jump over beginner mistakes!

  • Data Compliance and Usage: It's imperative to understand the legal and ethical boundaries concerning data use. We educate teams on what data can be leveraged in AI models and highlight the importance of privacy and transparency.

  • Unlocking Unique Data Value: Each organization has unique data that can provide competitive advantages. We help you identify this data and explore how it can be used to create value that your competitors may not have.

  • Sharing Success Stories: Learning from others’ experiences boosts confidence and sparks ideas. We share case studies and success stories within the industry to illustrate effective AI use cases and the benefits they bring.

  • Assigning Responsibility: Effective deployment of AI requires clear roles and responsibilities. We help you appoint key individuals within your organization who will lead AI initiatives, take responsibility for data quality and ensure that projects stay on track.

By investing in education, we empower your workforce not just with knowledge, but with the confidence to embrace AI technologies. Building a team with AI expertise or partnering with AI service providers can bridge the knowledge gap, allowing organizations to leverage AI effectively. This sets the stage for the next step in our cycle: Empower.

Empower: Facilitating Hands-On Engagement

The second step in our ABC cycle is "Empower." After educating your team about AI's potential and how to use it responsibly, the next step is to empower them to put their knowledge into action. Here’s how we facilitate this crucial phase:

  • Encouraging Experimentation: We create an environment where trying new things is encouraged and supported. By allocating specific times for experimentation, employees feel they have the space and permission to innovate without the pressure of immediate results.

  • Providing Access to Data: For AI experiments to be meaningful, access to relevant data is essential. We ensure that your teams have the data they need to conduct meaningful experiments, along with the tools to handle and analyze it securely.

  • Tool Exploration: Giving employees the opportunity to experiment with a variety of AI tools enhances their understanding and helps them find the right fit for their needs. Tools like YOKOT.AI, ChatGPT, and GitHub Copilot are examples of technologies we introduce to help bridge the gap between theoretical knowledge and practical application.

  • Prototyping and Testing: Previous prototypes aren’t just shelved—they’re brought out for wider testing within the organization. This not only helps in refining the applications but also in gauging their impact across different scenarios and receiving direct feedback from diverse user groups.

  • Mentorship and Support: Establishing a system of mentorship where more experienced employees and external resources guide those who are less familiar with AI tools can foster a more inclusive and supportive learning environment. This accelerates learning and increases confidence among team members.

This step of empowerment is where theoretical knowledge meets practical application. By providing the resources and freedom to experiment, employees can explore the potential of AI and begin to see tangible outcomes from their initiatives. This hands-on experience is crucial for building confidence and sparking ideas for integrating AI into existing products, services and workflows! These ideas naturally lead into the experimentation phase.

Experiment: Prototyping and Real-World Integration

The final step in our ABC cycle is "Experiment." This phase is where your organization’s AI endeavors start to materialize into tangible prototypes and integrated solutions. Experienced partners such as Softlandia are valuable in this phase. Here’s how we guide this critical stage:

  • Prototype Development: Encouraging the creation of novel prototypes allows teams to turn their ideas into real, testable applications. This hands-on approach helps in understanding how AI can solve actual problems within the organization.

  • AI Integration: We integrate AI into existing workflows or tools. This helps in demonstrating how seamlessly AI can enhance the efficiency and effectiveness of current processes, providing immediate value.

  • Accuracy Assessment: Gathering data is crucial not just for training AI models but also for evaluating their accuracy and effectiveness in real-world scenarios. This step ensures that the AI solutions are performing as expected and are viable for wider deployment. The evaluation data will be your ground truth when new models and algorithms are released!

  • Measuring Success and Benefits: It's essential to quantify the success of AI implementations. We establish metrics such as performance improvements, time savings, and user satisfaction to objectively assess the impact of AI prototypes.

  • Selecting Winning Prototypes: The most successful prototypes are those whose results are measurable and clearly beneficial. We emphasize moving forward with prototypes that show the most promise in terms of usability and impact.

  • From Prototype to Production: Softlandia not only develops prototypes but also assists in scaling successful ones to full production. This involves refining the technology, ensuring scalability, and integrating it into your operational framework.

The Experiment phase is crucial as it not only tests the practical applications of AI but also sets the stage for scaling successful projects across the organization. By focusing on prototypes that integrate well and deliver clear benefits, the cycle of Educate, Empower, Experiment can begin anew with further refined and ambitious projects, lessons learned and tools to empower the organization!

So, there you have it—the AI Booster Cycle, ABC of AI deployment: Educate, Empower, and Experiment. It's all about taking clear, actionable steps towards integrating AI in a way that’s practical and impactful for your organization. Continual education and training in AI will enable you to stay competitive in this rapidly evolving landscape.

Ready to get started or just looking to chat more about how AI can fit into your operations? Reach out to us at Softlandia. Let’s make AI work for you, not just as a buzzword but as a real engine for growth and innovation.