How to Build AI Product Strategy and Team?
Table of Contents
Although AI services are now widely available, businesses must determine if incorporating AI is a strategic match for their product offerings. The benefits of AI must be balanced against its drawbacks, which include the necessity for ongoing development and large expenditures in infrastructure and knowledge. Creating an AI product is a continuous process that will only partially be finished.
Does AI Make Sense for Your Product?
Make sure AI satisfies essential requirements before moving further with your project by answering "yes" to the following essential questions:
- Is there enough good data available? Artificial Intelligence is reliant on enormous volumes of heterogeneous data that are safe, comply with privacy laws, are accessible, and are representative of real-world situations.
- Is the issue too complicated for AI to handle? AI saves time and money best when it comes to challenges that are too complex for straightforward rule-based solutions.
- Does the issue change over time? Unlike static problems better suited to classical methods, AI thrives in dynamic contexts where variables and circumstances change often.
- Can you settle for less-than-ideal results? Since AI relies on probability, some degree of error is unavoidable. AI may not be the ideal solution if perfect accuracy is required.
- Does the answer need a large scale? AI is excellent for jobs like variable element prediction and large-scale, complicated dataset management because it can handle projects projected to develop quickly and exponentially more data.
Describe the intended use of the product.
The product vision is a statement of intent that guides the development of the product, improves teamwork, and builds resilience in the face of adversity.
When creating a product vision, think about how the success of your product will make the world a better place. Though romantic, this visionary question may inspire your team and consumers with great strength.
Create a Strategy for Your Product.
Prioritize identifying the product's goal and target audience before diving into AI details. This may be achieved by using an Agile management strategy that draws inspiration from lean startup ideas. Lean startups strongly emphasize customer involvement and follow the "build-measure-learn" cycle, in which every development is tested with consumers to get insightful feedback that informs the iterative process of improving the product.
To ensure continuous progress, this loop is repeated throughout the discovery, validation, and scalability phases of product strategy planning. All three phases are interconnected. Then you need to analyze the market, the customer base, and the growth rate.
Discovery Phase
Research is used to identify consumer segmentation, use cases, and business models, define and prioritize challenges, and hypothesize solutions during the discovery phase of product strategy. Creating MVP (Minimum Viable Product) statements that include the target user, the issue being addressed, the suggested solution and a success statistic is the last step in this process. Through the "build-measure-learn" cycle, feedback is utilized to improve these MVPs and reduce the number to a few viable choices.
For instance, the following may be included in an MVP statement if the goal is to increase stalling sales for an airline route:
- Adding senior citizen concierge services can boost sales by 5%.
- Increase business users' mileage points by 20% to increase online sales by 5%.
- Offering families free checked baggage to increase sales by 5%.
Validation Phase
Minimum viable tests (MVT) are carried out during the validation phase to verify the viability of MVP hypotheses. An MVT assesses an MVP's underlying assumptions by observing how users engage with a prototype. This measure avoids overspending on concepts that won't work.
First, prioritize the MVPs according to their buildability, consumer attractiveness, growth, and revenue potential. Next, basic prototypes with little functionality are created to enable customer testing and collect data on essential KPIs. For instance, a simple chatbot or landing page may be enough to gather data to explore whether older folks would be willing to pay for concierge services.
A build-measure-learn cycle is created by this technique, which enables rapid prototype creation, user-based measurement, and learning to improve the product idea further.
Scaling Phase
After your MVP has been validated by minimal viable testing, concentrate on customer development efforts such as attracting, keeping, and expanding your client base during the scaling phase. The tactics will change according to the product's size, maturity, and strategic importance for your business.
For example, a startup could use client acquisition strategies like feature additions or price optimization. Still, an established business would try to upsell current customers to raise their lifetime value. The following phases include investigating additional features, revenue models, and team expansion plans based on a concierge AI chatbot for seniors. A build-measure-learn cycle allows for iterative evolution from hypothesis to strategy.
Create an AI Plan for Your MVP.
- Clearly define the AI challenge, including the use cases, objectives, and target audience.
- Select a Data Strategy: Find out what data is already accessible, note any gaps, and determine the best way to get and arrange the required data.
- Develop a technology and infrastructure strategy. Consider scalability, portability, and secure data access in various contexts.
- Develop an Organizational Strategy and Skillset: To facilitate growth and development, assemble a team of domain experts, engineers, architects, designers, data scientists, and business analysts.
Product Team for AI
Domain experts, engineers, product designers, data scientists, and business analysts are all essential for creating a product that customers will enjoy, and they should all be on the Ideal AI Product Team. As the project grows, you may need to add more members to your team and use agile techniques like Scrum or Kanban to ensure smooth operations and growth.
Are you prepared to use strategy to increase the effect of your product? Our team of product strategy consultants can assist you at every stage of creating and expanding your AI product strategy services. Together, let's turn your ideas into profitable goods. To begin influencing the future of your product, get in touch with us right now.
