Alyve's Generative AI innovation sprint model
From curiosity to value-add with generative AI
At Alyve, we believe that the key to success lies in embracing tools like ChatGPT and fostering a culture of constant innovation and experimentation.
We encourage our teams to ask questions that can transform how their organisation operates. This culture of curiosity is critical when working with generative AI, where the potential applications are as vast.
The Alyve AI Innovation Sprint model
The Alyve AI Innovation Sprint model is a structured way to get the most out of generative AI, quickly.
Alyve’s AI Innovation Sprint approach
Day 1: Define the problem and set the goal
Start by defining the problem that your AI solution will address. Develop a shared understanding of the working context, including the problem, the business, the customer, the value proposition, and how you will determine success. Identify your most significant risks and make plans to mitigate them. The decision-maker should facilitate agreement around a long-term goal and ensure alignment across the team.
Day 2: Understand the customer and map the journey
Spend the second day understanding your customer. Identify their needs, preferences, and challenges. Map out their journey and identify how AI can enhance their experience at each touchpoint. Remember, the goal is to solve a problem for the customer, not just to implement AI for the sake of it.
Day 3: Ideate and design solutions
On the third day, brainstorm potential AI solutions to the problem. Remember to keep the customer at the centre of your ideation process—design preliminary models of your solution, considering the capabilities of AI tools like ChatGPT.
Day 4: Develop and test prototypes
Day four is all about turning ideas into tangible prototypes. Develop prototypes of your AI solution and test them in a controlled environment. Gather initial feedback and data to understand how well the solution works and where improvements can be made.
Day 5: Review, learn, and plan the next steps
Review the results of your tests on the final day of the sprint. What worked well? Where were the challenges? What did you learn about your customer, problem, and solution? Use these insights to refine your AI solution and plan the next steps. Remember, the sprint is just the beginning. The real work comes in implementing, iterating, and scaling your AI solution.
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With over two decades of experience in digital transformation consulting, Alyve responds to the unique demands of organisations with sensitive, ethical, and compliant AI strategy solutions.
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Example: A large healthcare organisation looking to improve patient experience and streamline administrative tasks using AI.
Day 1: Define the Problem and Set the Goal
The team identifies that administrative tasks are taking up a significant amount of healthcare professionals' time, detracting from patient care. The goal is to use AI to automate administrative tasks, freeing up healthcare professionals to spend more time with patients.
Day 2: Understand the Customer and Map the Journey
The team identifies two main customer groups: healthcare professionals and patients. They map out the journey for both groups, identifying touchpoints where administrative tasks create bottlenecks or frustrations.
Day 3: Ideate and Design Solutions
The team brainstorms potential AI solutions. They consider a ChatGPT-based AI that can handle appointment scheduling, prescription refills, and basic patient inquiries. They also consider an AI tool that can help with documentation and record-keeping.
Day 4: Develop and Test Prototypes
The team develops a prototype of the ChatGPT-based AI and tests it with a small group of healthcare professionals and patients. They gather feedback on the AI's performance and usefulness.
Day 5: Review, Learn, and Plan Next Steps
The team reviews the feedback and data from the tests. They find that the AI was able to handle many administrative tasks effectively, but some users found it difficult to interact with. The team plans to refine the AI's user interface and test it again. They also plan to present their findings to the organisation's leadership, with a recommendation to implement the AI on a larger scale.
This is a simplified example, but it illustrates how an AI Innovation Sprint might work in a real-world scenario. The key is to stay focused on the problem and the customer, and to use the sprint as a structured process for developing and testing an AI solution.
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