Many thoughts about how to apply AI to create Value-driven Management.
There are a number of ways that AI could be used to support value-driven management. Mainly in analyzing data on customer behavior, businesses can gain insights into the things that are most valuable to their customers.
AI can be used to automate certain tasks, such as inventory management or supply chain optimization, which can help businesses improve efficiency and reduce costs. And optimize marketing and sales efforts by providing insights into the best ways to reach and engage with customers.
But value-driven means to have utility to the customer business!
The idea behind value-driven management is to focus on creating value for the customer rather than just cutting costs or increasing efficiency. By using AI to gain a deeper understanding of customer needs and preferences, businesses can deliver products and services that are more valuable to their customers. It’s all about creating products and services that make a meaningful impact on people’s lives rather than just trying to make a quick buck.
The utility criteria for the customer’s business should link it to business values meaning.
There are a number of criteria that can be used to assess the utility of a product or service to a customer’s business:
- Does it increase revenue or profitability?
- Does it improve the customer experience?
- Does it enable the business to be more agile and responsive to changing market conditions?
- Does it help the business to reduce risk or increase security?
- Does it increase the efficiency or productivity of the business?
But many times, the customers and even the service provider are not sure about the value that fits business needs. An AI -Artificial Intelligence framework analysis to support the decision-makers would be great.
Often, there is a lot of uncertainty and ambiguity around the value of a product or service. That’s why a framework for analyzing and communicating value is so important. Here are some elements that could be included in such a framework:
- Define the value proposition: what unique benefits does the product or service offer to customers?
- Identify the key stakeholders and their needs: who are the decision-makers and what are their priorities?
- Assess the impact of the product or service: how does it contribute to the organization’s goals and objectives?
- Quantify the value: how can the benefits of the product or service be measured and communicated in financial terms?
AI plays a critical role in this framework, in several ways:
- AI can help to analyze and organize large amounts of data to identify patterns and insights that may not be obvious to humans.
- AI can be used to automate the analysis and quantification of the value of a product or service, by using techniques such as machine learning and predictive analytics.
- AI can help to create personalized value propositions for different customer segments, by using data to understand their unique needs and preferences.
Including simulation of an organizational space to experiment with its values that fit customer expectations.
This is what we call a “simulated environment” or a “digital twin.” Here’s how it might work:
- The AI system would create a digital representation of the organization and its processes.
- This digital representation would be a kind of sandbox where the AI could experiment with different values, strategies, and decisions to see how they impact the organization and its customers.
- The AI could run simulations to test various scenarios and measure the outcomes, allowing it to identify the most promising strategies and decisions.
A fulfilling the gap between the organizational process maturity and customer value delivery.
By creating a simulated environment, the AI system could help an organization identify areas where its processes are not aligned with customer needs, and suggest improvements that could close that gap. This could lead to a more efficient and effective organization that is better able to deliver value to its customers.
Each organizational value stream should be composed of teamwork to develop the right features.
The composition of value streams in an organization should consider:
- Define clear goals and objectives for each value stream.
- Identify the key stakeholders and processes involved in each value stream.
- Map out the flow of work and information through the value stream.
- Identify bottlenecks and opportunities for improvement.
- Create cross-functional teams that can work across value streams to deliver value faster.
- Use agile practices like Kanban or Scrum to increase transparency and collaboration.
Indeed, cross-functional teams generate complex backlog dependencies to manage. Then, there are ways to mitigate it. Here are a few techniques:
- Create a single, shared backlog that all teams can access and prioritize together.
- Use tagging or labeling to identify which items belong to which value stream.
- Prioritize items based on their impact on the organization’s goals, rather than just their importance to a particular team.
- Encourage open communication and collaboration between teams to reduce confusion and conflict.
- Regularly review the backlog to ensure that it remains aligned with the organization’s goals and priorities.
An effective prioritization for backlog items list to fit values’ customers needs and also considering that the timeframe for each feature component development is different, the backlog items prioritization can be tricky, especially when there are different timelines for each item. To help with that, consider:
- Use the Weighted Shortest Job First (WSJF) method, which takes into account the value, effort, and risk of each item.
- Consider the “must-haves” vs. “nice-to-haves” to prioritize items that will deliver the most value to customers in the shortest amount of time.
- Use data and analytics to identify the items that will have the biggest impact on customer satisfaction and retention.
- Involve stakeholders in the prioritization process to ensure that their needs are represented.
And design a team topology for this tricky feature development! Here are some tips for designing a team topology that can handle complex feature development:
- Create cross-functional teams that include members with different skills and perspectives, such as developers, designers, and subject matter experts.
- Encourage a flexible and collaborative culture that encourages shared responsibility and shared decision-making.
- Use agile methodologies like Scrum or Kanban to promote iterative development and regular feedback.
- Establish clear lines of communication between teams so that everyone is on the same page and working towards the same goals.
- Ensure that everyone has a clear understanding of their role and responsibilities within the team.
In addition, the learning cycles should be incorporated into the development process to enable the inclusion of lessons from value-meaning validation. The continuous learning is critical for delivering value-driven product/service. The development process should establish:
- Conduct user research throughout the development process to continuously gather feedback and adjust the product accordingly.
- Regularly review customer data and metrics to understand what’s working and what’s not.
- Conduct retrospectives after each sprint to reflect on what went well and what could be improved.
- Encourage a culture of experimentation and prototyping to test new ideas quickly and iterate based on feedback.
- Empower developers to continuously learn and improve their skills through training, coaching, and mentoring.
The real challenge is how to keep lean on process development and focused on the learning cycle to fit time-to-market customer expectations. But think about:
- Use minimum viable products (MVPs) to get feedback early and often and then iterate quickly based on that feedback.
- Prioritize features based on their impact on customer value rather than just their technical complexity.
- Leverage automation and testing tools to speed up the development process.
- Embrace an iterative approach to development rather than trying to get everything perfect on the first try.
- Foster a culture of continuous improvement, where everyone is always looking for ways to optimize processes and deliver value faster.
You have many strategic items to consider, don’t you think? Let’s go back to the central question: how can the utility of the product/service feature be quickly achieved?
Some additional tips for achieving quick utility in product or service feature development are:
- Solve the customer’s pain points first. Identify the top challenges or problems your customers are facing and build features that directly address those.
- Keep the user interface simple and intuitive. Don’t overcomplicate things with unnecessary features or confusing design elements.
- Deliver value in small, incremental updates rather than trying to launch everything at once. This helps you stay flexible and responsive to customer feedback.
- Conduct user testing early and often to ensure that the features you’re building are actually meeting customer needs.
Have an excellent mastering!