How do you prove the business value of AI delivered by agile teams?

3 clear values 1.Objectivity 2.SPEED 3.Actions objectivity is like RADAR of the AI ship.. to stay on course SPEED is obvious ..if the model is right the speed is acheived as everything fits in a predcitive franework Actions.. is the atomic simple actions at 5 ft level down from 20000 ft aggreagation.. making thing.. data driven abd bottoms up

As a SaaS founder, I've learned the key is tying AI value criteria directly to specific business goals. For example, at my last startup we used AI to optimize trial-to-paid conversions. Our goal was increasing conversion rates by 15% in a year, which we measured through A/B tests. This aligned perfectly with our growth objectives. We also targeted a 20% reduction in customer acquisition costs by using AI to automate lead generation. We benchmarked against historical CAC data to track progress. The most important thing is quantifying your targets - like reduced churn or higher NPS scores. That way you can prove over time how much value AI is creating through concrete metrics tied to business priorities.

After implementing an AI solution, showcasing its value requires tracking KPIs tailored to your objectives. These metrics may include increased revenue from new customer segments, improved customer retention rates, and decreased operational expenses. Quality enhancements, like reduced error rates and improved product performance, can also indicate the AI's impact. Monitoring customer satisfaction scores or Net Promoter Score (NPS) provides insights into enhanced user experiences. Regular analysis of these KPIs demonstrates how the AI aligns with business goals and strategy, underlining its contribution to achieving defined value criteria.

Think of value criteria like a GPS for your AI journey. If you're planning a road trip (your AI project), you wouldn't just hop in the car without a destination in mind. These criteria are your guideposts, ensuring you're headed in the right direction and getting the results that matter most to your business. Just like you might prioritize visiting a certain city on your trip, prioritize the most impactful criteria for your project's success.

Defining value criteria is a foundational step in aligning AI projects with business objectives. the continuous need for reassessment and flexibility in these criteria. Markets change, customer needs evolve, and new opportunities arise. Aligning AI projects with adaptable value metrics ensures they remain relevant, achieving both immediate and long-term strategic goals, and enabling a business to stay ahead in the fast-paced technological landscape.

In simpler terms, think of value indicators as the "report card" for your AI project. Just like you'd want to check your grades to see how you're doing in school, these indicators show how well your AI is meeting your goals. If your AI was a car, these would be your speedometer, fuel gauge, and customer reviews combined – letting you know if you're on the right track or if tweaks are needed.

Businesses that strategically integrate AI should consider developing a robust feedback loop that includes data-driven metrics and human insights. This approach ensures that projects are not only meeting initial expectations but are also responsive to unanticipated user needs and market shifts. The agility in tracking and responding to these indicators is what often differentiates an evolving, responsive project from a stagnant one.

Value indicators are the North Star for any AI project. These metrics, whether tangible or intangible, offer insight into how well a project is aligning with its core objectives and the value it is creating for its stakeholders. The benefit of quantitative KPIs is that they provide an objective measure of performance. Metrics like accuracy, precision, recall, or speed are intrinsic to the function of many AI solutions. Conversion rates, retention rates, and engagement metrics can further highlight the impact of an AI solution. Often, the success of a system is not just in its numbers but in the experiences it facilitates. User feedback, testimonials, and reviews provide nuanced insights that might be missed by purely numerical metrics.

Tracking value indicators is not just about identifying metrics; it's about aligning them with the real-world impact and needs of the business. Utilizing both quantitative (e.g.accuracy, speed) and qualitative measures (e.g.customer feedback) provides a comprehensive view of an AI project's effectiveness. But beyond mere tracking, the key lies in turning these indicators into actionable insights. Analyzing trends, correlating different data points, and forecasting future outcomes can create a feedback loop that guides decision-making. This doesn't only align the project with the strategic goals but also promotes a culture of continuous improvement and learning, enabling teams to adapt to changing req. & maximizing the ROI of AI initiatives.

1st have a solid agile team in: Project Manager / Leader (POC, defines stakeholders objectives, keeps team on deliverables timeline), scrum masters (coach), developers & architects (design, algoritms, evaluations). 2nd Design the MLOps workflow / pipeline starting from defining the business problem & listing stakeholders KPIs. 3rd create a UI for each stage of the MLOps workflow (ie in a dashboard) so stakeholders can visualize the process (eg link to structured Git Repo) 4th have project scope and process detailed in app deployment landing page with summaries from each agile member outlining successes and limitation for each phase and list evaluation metrics (eg F1, accuracy, etc). 5th share end user analytics inc bounce rate & feedback

Think of value delivery as showing off your AI's "before and after" transformation. Just like a home renovation show where they highlight the old kitchen and then unveil the new one, you need to vividly present how your AI project turned a problem into a success story. A simple bar chart, a captivating demo, or a relatable customer story can all be your "big reveal" moment. Remember, it's all about making your audience go, "Wow, I see the difference!"

While an AI project might be technically brilliant and achieve its internal KPIs, its real success is gauged by the tangible benefits it brings to its stakeholders and users. Technical metrics, while essential, may not always resonate with non-technical stakeholders. It's pivotal to translate these into tangible benefits, like improved efficiency, cost savings, or enhanced user experiences. Storytelling makes the communication memorable and relatable. It creates a narrative that is more than just data, focusing on challenges faced, solutions implemented, and the transformative impact of the AI project.

The value of an effective feedback loop cannot be understated. Capable communication between AI developers and key stakeholders allows for efficient and tailored value delivery in an iterative way.

Demonstrating value delivery goes beyond merely showcasing results; it's about connecting those results to the unique needs and expectations of the stakeholders, customers, and users. Contextualizing the benefits of the AI project, be it through reports, dashboards, or case studies, adds a human touch to the data-driven story. For example, instead of merely presenting numbers, one might share a narrative that illustrates how an AI-driven solution solved a critical problem, enriched user experience, or drove innovation within a specific context. Tailoring this demonstration to the audience's industry, role, or pain points can make it more relatable and compelling.

One aspect is to conduct different sets of evaluations: - Model with a subset of historic data (proof of concept) - Model with new data (pilot in production) - Model in production (feedback loop) Don’t just look at the numbers, though. Talk to your users/ business stakeholders what behavior they are seeing and why — and how your mode drivers value (or could be improved).

Just like when baking a cake, you can't assume the amount of sugar without tasting. Similarly, with AI projects, it's crucial to "taste test" our assumptions by validating them with real data. Think of it as a trial run. Before going full-scale, test a smaller version of your project to ensure it meets the mark. Feedback from these tests can be a goldmine for improvement. Don't just guess; always validate!

A good way to validate assumptions is by developing a Proof-of-Concept (PoC). - If the PoC is good enough, you can then deploy it into production. - Or take the learnings and build a better version from scratch. - Or abandon if it haven't returned the results you expected.

An example of value creation for AI projects being optimised is bringing AI experts and data/ML scientists into your product teams to drive the inclusion of safe, transparent and reliable AI into your product roadmaps.

Optimizing value with #AI in the business landscape requires strategic alignment with organizational goals, robust data integration, and benchmarking against industry standards. By leveraging AI for efficiency, personalization, and automation, and employing continuous measurement and benchmarking of ROI through key performance indicators, organizations ensure alignment with best practices. This approach solidifies AI as a vital corporate asset, contributing to innovation, growth, and a competitive edge, while adhering to ethical considerations and scalability. Developing industry standards is a must to ensure consistent performance, quality, and ethical implementation across AI-driven initiatives.

Engage the ACT framework to navigate the AI landscape. Align your project with stakeholders, customers, and users. Foster Clarity by harmonizing your AI projects or efforts with your business context and goals. Transparency ensures value alignment, weaving AI seamlessly into your organization's vision, values, and culture. Embrace this approach to create AI projects that not only respond to market dynamics but also enhance customer experiences, fostering success in the ever-evolving digital terrain.

Risk assessment Understanding the value add of AI based solutions and alignment in terms of risks and anticipating points of failure can help in preventing surprises and aligning expectations. A careful examination of the risk appetite can also help teams decide whether to build in-house vs buy or use AI as a service in order to reach the end result.

Stakeholders often do not appreciate or over estimate the value AI systems can bring to the table. You should be able to manage stakeholder expectations and focus on delivering what was agreed upon. However, a PoC often goes a long way to estimate the capability of an AI system before an agreement on delivery takes place.

Consider leveraging access to data, standards, unique product-specific or wider domain competencies and insights, to connect with other actors in value co-creation. In this way you may turn a painstaking inward business development process into a collaborative, open innovation ecosystem, where competitive resources, knowledge and risks are shared to deliver results. To name a few of the benefits, this offers an ability to pivot, gain learning continuity, to discover new uses for existing competencies and products, build foresight and strategies which were not reachable before, to collaboratively bridge innovation chasms, expand market access and value margins, and improve your own KPIs while generating collective impacts for the industry.

Proving the business value of AI delivered by agile teams involves establishing key performance indicators (KPIs) aligned with business objectives, such as improved efficiency, cost reduction, or enhanced customer experience. By tracking these KPIs before and after AI implementation, and attributing observed improvements to specific AI-driven changes, you can effectively demonstrate the tangible impact of AI on the organization's bottom line and make a compelling case for its value. Additionally, gathering qualitative feedback from stakeholders about how AI-driven solutions address their pain points can provide valuable insights into the broader benefits of the agile AI approach.

Agile development including user empathy is key to the successful infusion of AI into the products. let's consider a customer relationship management (CRM) tool. By applying agile development principles and user empathy, you can infuse AI to enhance the user experience. For instance, the CRM could use AI to analyze customer interactions and provide personalized recommendations for follow-up actions based on the sentiment and context of those interactions. This not only improves user engagement but also streamlines the sales process, demonstrating the effective combination of agile development, user empathy, and AI infusion.

The "added value" metric is a useful measure to demonstrate the value of AI. For the value to be meaningful it needs first to be aligned with both the business strategy and the AI strategy. Also, don't forget that the added value has to take into account several aspects including: - Revenue generation - Margin improvements - Cost reduction - Customer service improvements - Risks mitigation - Competitive advantage

When thinking about the value of AI solutions, it's often tempting to think in terms of savings for the company. However, in my experience it's often more effective to instead cast the value in terms of increased revenue, since the top focus for most businesses is typically growing revenue.