How do you meet AI and BI stakeholder needs?

By identifying your stakeholders, you can understand their needs, preferences, and concerns, and communicate and collaborate with them effectively throughout the project lifecycle. Stakeholders can be internal or external to your organization, and they can have different levels of involvement and impact on your project. For example, some common stakeholders for AI and BI projects are: Project sponsors , Project managers, Business users, Data owners, Data scientists, External partners, External customers, Regulators etc.

Let's first clarify! Stakeholders need their business problems solved and *don't have AI or BI needs*. Once you define the business goals, identify stakeholders and frame their problems even *before* you bring technology into the picture. If the challenge needs descriptive or diagnostic insights, a BI approach might solve it. Else, explore advanced analytics, including AI techniques. Picking stakeholders with AI or BI projects in mind is a recipe for failure.

Stakeholders might not have direct BI or AI needs. They have business needs and profit goals but don’t always have the answers or procedures to achieve those goals. There is when they use tools like BI or AI: BI which help analyze and visualize trends and patterns of their most important KPIs and data; and AI, that infer solutions from these trends and even provide recommendations on how to proceed to achieve business goals. At the end, stakeholders are the ones who take the final decision, generally mixing the outcome of these tools and their own instincts and feeling, built by experience.

Recognizing your stakeholders, especially end users, is pivotal in securing their trust and support for the initiative or product you're advancing. Their endorsement is closely tied to its eventual adoption and overall success. Engaging them early and valuing their feedback ensures the product or initiative resonates with their needs and expectations, thus boosting its chances of long-term viability and impact.

We need to debunk the myth that AI or BI is the end goal. This is one of primary reasons why both AI and BI efforts do not bring in desired results. AI and BI are the means to an end, which is enabling business to perform better. We need to work backwards from business objectives of an organization. If we have a clear understanding of what business goals we need to achieve, we can align how AI and BI can help us. Setting up a BI dashboard, or building a generative AI project would not have any takers if it doesn't help teams perform better in some fashion.

metrics ( can be business, efficiency, service, etc) are critical to be identified at the onset of use-case before you start trying to solve for. This requires in my view asking questions with the domain experts. Understanding the metric along with is trend and linkages to other metrics are also key. Understand the linked data for this metric with baseline definitions is key. These must be clearly documented and agree upon.

Define clear objectives aligned to business goals and stakeholder requirements. For example, the objective for the customer churn prediction model is to reduce customer attrition by 15% within 6 months. Relevant KPIs and metrics should be established to track progress. Realistic targets should be set based on baselines and benchmarking, and metrics should be monitored and reviewed on an ongoing basis. This focus on measurable goals, stakeholder-aligned metrics, and regular progress reviews will drive value from AI and BI investments.

On of the key step to meet stakeholder need is to clearly define objectives and metrics. This means identifying what you want to achieve with the AI and BI solution, how you will measure its success, and how it aligns with the overall business goals and strategy. Objectives and metrics should be SMART: specific, measurable, achievable, relevant, and time-bound. They should also reflect the needs and expectations of the different stakeholder groups, such as customers, employees, managers, investors, regulators, etc. E.g., if your objective is to improve customer satisfaction and retention with AI and BI, you might use metrics such as net promoter score, churn rate, customer lifetime value, and customer feedback.

Defining objectives and metrics for AI/BI projects is crucial for accountability and progress tracking. However, to be fully effective they require flexibility to adapt to changes while continuing to guide efforts towards stakeholders' diverse needs. A balanced approach considers both quantitative and qualitative aspects to incentivize ethical, responsible solutions and drive long-term value over short-term wins.

Articulating clear objectives and metrics is fundamental for the success of AI and BI endeavours. Objectives pinpoint the tangible outcomes you seek, whether it's a boost in sales or enhanced user satisfaction. On the other hand, metrics act as gauges to monitor these outcomes, offering insights into aspects like overall revenue or operational efficiency. It's paramount to ensure these objectives and metrics not only resonate with stakeholder expectations but are also lucid and achievable. Establishing a starting point for each metric is essential, alongside setting ambitious yet attainable targets. Regular monitoring ensures the project remains on course and aids in making necessary adjustments in real-time.

The first step in communication channels between AI and BI stakeholders is to remove the jargon. The jargon limits the conversations and collaborative efforts as each of them are experts in their respective fields. The mission, vision and objectives should be easy to understand and the goals should be crisp and subdivided. The common feature between AI and BI is data, as they thrive on it. The focus should be different perspectives of data and its usage.

As technology partners, it is very important for us to learn and speak the language of our stakeholders. Know what we are trying to achieve together: ? Revenue growth by finding new customers and markets in sales ? Cost savings through reduced cycle times and improved yields in manufacturing and supply chain ? Increased efficiency in day-to-day business processes At the same time, we should be able to communicate technical challenges and trade-offs in terms of impact to business outcomes. In my experience, learning to effectively communicate is a critical skill for AI and BI partners; one that can raise the bar and increase trust.

Share the project vision, goals, scope, and expectations with all relevant stakeholders, as well as solicit their feedback, input, and concerns. Communication and collaboration entail establishing clear roles and responsibilities, reporting and escalation mechanisms, and decision-making processes among the project team and the stakeholders. For example, a product manager may use a stakeholder analysis matrix to identify the key stakeholders, their interests, influence, and communication preferences, and then design a communication plan that specifies the frequency, format, and content of the information to be shared with each stakeholder group.

Thoughtful communication and selective collaboration are important for AI/BI success by fostering stakeholder buy-in through transparency on initiatives and participation where expertise provides value. However, engagement must be tactfully managed to avoid overloading participants or losing focus. Tailored, two-way exchanges build understanding and steer solutions, while targeted collaboration taps insights to strengthen outcomes. Together, balanced stakeholder interactions provide guidance crucial for responsible, accepted AI/BI that meets strategic objectives.

Understand Stakeholder Requirements: Begin by comprehensively understanding the needs and expectations of both AI and BI stakeholders. Clear Communication Channels: Establish transparent communication channels to facilitate ongoing dialogue and feedback between the two groups. Integration of AI and BI Solutions: Ensure seamless integration of AI and BI solutions to meet the specific requirements and preferences of each stakeholder. Regular Updates and Training: Provide regular updates on progress and conduct training sessions to keep stakeholders informed and engaged. Feedback Mechanism: Implement a robust feedback mechanism to gather insights from both AI and BI stakeholders, fostering alignment with their evolving needs.

While demonstrating value is the final step here, the prep must begin even before you kickoff the project. Your project proposal must include an estimate of value the initiative will deliver (top/bottom line, branding...). This will not just help you secure budget, but get alignment with senior stakeholders on outcomes and how it will be measured.

Deliver the product and demonstrate the value using dashboards, reports, or visualizations that highlight the key insights, outcomes, and benefits of the solution, such as improved efficiency, accuracy, revenue, or customer satisfaction. For example, if the solution is a chatbot that automates customer service, the team can demonstrate how it reduces response time, increases customer satisfaction, and saves costs. The team can also collect and analyze data on the chatbot's usage, accuracy, and feedback, and use it to improve the solution and identify new opportunities.

Successfully delivering value in AI and BI initiatives is pivotal. It's not just about deploying technology; it's about the tangible and intangible benefits these bring. AI and BI solutions must be reliable, precise, and scalable while ensuring security. Demonstrating value involves more than metrics; it's about the transformative narrative. It's essential to highlight how these solutions impact business or enhance user experience. Celebrating wins, coupled with transparently addressing challenges, fosters stakeholder trust. As AI and BI become integral in today's dynamic landscape, showcasing their genuine value becomes paramount for sustained growth and acceptance.

One fundamental truth stands out: value is the heart and soul of BI and AI solutions. It’s not an afterthought; it’s the driving force from day one. Defining tangible and intangible benefits, aligning with objectives, and leveraging the role of a business analyst to connect it all is pivotal. From stakeholders’ identification to documented success criteria, it’s about proving the initial point with metrics defined at the outset. Value is the engine that powers these solutions, and recognizing its centrality is key to their success.

In my experience, delivery and demonstrations can be a great time to get teams and stakeholders excited about future possibilities. In consulting, these on-point, time saving, adaptable AI and BI solutions are highly sought after when impactful. If you demonstrate achievements and high customer reliability, you will likely expand the demand for your solutions into other teams in the organization.

AI and BI stakeholders both have different needs. Important metrics for AI(Artificial Intelligence) stakeholders are typically business process optimization, resource independence, and smart automation for complex tasks. However, for BI(Business Intelligence) Stakeholders primary concerns are Transparency on Business KPIs, Actionable Insights, Smart Visualizations, Org. Data Governance, Granularity, Quality and Accuracy. In order to meet the needs of these stakeholders - - At first, build their persona and map out the metrics that concern them. - Then set goals and benchmarks for each of these KPIs. - Eventually track performance improvements pre and post-product enhancements.

These are aligned from the vision perspective, things can so south when strategy and plan puts into action. Though they are lot of common feature having a clear plan, risk mitigation, feedback enablement and accountability are critical in addition to her above. Above all consistency and incremental win will be sustainable on long run.

To meet AI and BI stakeholder needs: 1. Identify stakeholders and their needs. 2. Align solutions with business goals. 3. Choose suitable tools and tech. 4. Develop and implement solutions. 5. Monitor and evaluate performance. Example: Executives get dashboards showing business impact, managers access real-time insights, data scientists have powerful AI tools, analysts create reports, and end users get user-friendly BI solutions. This ensures solutions meet all stakeholders' needs.

Identify specific requirements: Understand the distinct needs of both AI (Artificial Intelligence) and BI (Business Intelligence) stakeholders. Align goals: Ensure that AI and BI initiatives are aligned with the broader organizational objectives. Foster collaboration: Encourage communication and collaboration between AI and BI teams to optimize synergies. Scalability and flexibility: Design solutions that are scalable and flexible to accommodate evolving stakeholder needs. Continuous feedback: Establish mechanisms for ongoing feedback to adapt AI and BI strategies in response to changing requirements.