What do you do if you want to measure the impact of your AI work effectively?

For any AI project, I've found it extremely important to have a set of input metrics and output metrics. Input metrics are what you can reliably change using technical levers like Data, Model Tuning, Changing Hyperparameters, etc. Examples of input metrics are model Accuracy, precision, recall, etc. Output metrics are what we track as business owners. These include Cost, Revenue, Profitability, etc. When taking up a project, there needs to be a strong correlation between these metrics, and that must be proven through data. This way, you can reliably measure your input metrics, and you know exactly how much is that moving the output metrics. Output metrics are nothing but impact.

When starting an AI project, it's crucial to first decide what 'success' means. This means setting SMART goals—specific, measurable, achievable, relevant, and time-bound. For example, if your AI is meant to speed up a process, a SMART goal could be to reduce the time it takes by 20% within the next year. This clear goal helps you know what to focus on and measure, making sure your project stays on track and really improves things in the way you want.

Of Course, it is important to set SMART goals when deciding on an AI project. However, even before this, I would suggest one exercise which is taking a view of Feasibility versus Value. In other words, giving markets out of 10, how easy will this candidate project(s) be to undertake (i.e. feasibility) versus what advantage will AI in this area provide (i.e. value)

Measuring our AI impact effectively is crucial for real progress! I always start with clear KPIs and user feedback. It's about seeing how our tech improves lives, not just the numbers. What strategies have you found effective for gauging AI's real-world impact?

Sugiro que selecione meticulosamente as métricas que refletem diretamente as metas estabelecidas para o seu projeto de IA. Eu costumo dividir as métricas em técnicas, como precisão de algoritmos e tempo de resposta, e centradas no usuário, como satisfação e taxa de adoção. Essa abordagem dual permite não apenas avaliar a eficácia técnica do sistema, mas também entender seu impacto real na experiência do usuário, garantindo uma visão completa do valor agregado pela IA.

Choosing the right metrics is essential to see how well your AI project is doing. If your goal is to make a process faster, you should look at metrics like how long the process takes now compared to before. It's also good to check both technical things, like how precise your AI is, and user-focused aspects, like how happy people are with it. This way, you get a full picture of the impact your AI is having, ensuring it's not just technically sound but also truly useful to people using it.

I choose metrics that align with project objectives, capturing both quantitative and qualitative aspects of performance. These metrics are relevant, actionable, and easily trackable, allowing for regular monitoring and adjustment to drive continuous improvement and optimize impact.

Choosing the right metrics is fundamental in project management, particularly in engineering where precision and efficiency are paramount. For instance, during the development of a new aerospace component, we prioritized metrics like tolerance accuracy and material fatigue alongside project delivery timelines. This dual focus ensured not only that the engineering specifications were met but also that the project adhered to stringent timelines, crucial in the aerospace industry. Balancing these technical and time-based metrics was key to our success. Curious to hear how others prioritize and balance metrics in their projects.

There are lots of metrics you can apply and I'm sure whatever array you choose are appropriate - you know your own business better than other one else. Just be careful, when consolidating results across an array of metrics that you don't double count the benefits. If you register the value in one area based on one metric, don't add to your totalled value score the results from another metric on the same area.

When designing accessible solutions, remember to also involve and engage people who don't know anything about your solution or product beforehand. They can give you valuable insights and data on your blind spots and if there's any difficult to understand jargon or features.

After setting your goals and deciding on the metrics, the next step is to collect the data needed to see how well your AI is doing. This means setting up ways to record important information, like how users interact with your AI, what results it gives, and how often it makes mistakes. It's really important to make sure that when you collect this data, you do it in a way that keeps user information safe and follows any rules about handling data. This helps you understand your AI's performance without compromising on ethics or legality.

Numbers are important, but don't forget the human element! Supplement your data collection with user stories. This could involve gathering user feedback through surveys or interviews. Ask users about their experience with the AI system. Did it meet their expectations? Were they surprised or confused by its outputs? Capturing user stories alongside data helps you understand how the AI is impacting people in real-world scenarios. This can reveal usability issues or areas where the AI can be further personalized to better serve users.

Once your goals and metrics are set, the next critical step is data collection to evaluate your AI system’s performance. Implement robust logging mechanisms in your AI application to record essential data like user interactions, system outputs, and error rates. It’s crucial to gather this information responsibly, ensuring that your data collection practices respect user privacy and comply with data protection laws. This approach not only supports the integrity of your assessments but also builds trust in your AI solutions.

I collect data from relevant sources aligned with project objectives, ensuring adherence to privacy and ethical guidelines. By systematically aggregating data from internal and external sources, I enable comprehensive analysis and evaluation, facilitating informed decision-making and continuous improvement.

To effectively measure the impact of your AI work, it's important to take a strategic approach and use meaningful metrics. First analyze the data you collect to evaluate the impact of your work on AI. Compare success metrics to your goals and evaluate whether your work has achieved the desired results or whether changes or improvements are needed. Additionally, compare the results of your work with benchmarks or industry standards to evaluate the performance and effectiveness of the AI system compared to competitors or reference models. By using meaningful metrics, you can effectively measure the impact of your AI work.

Effective impact analysis is the key to responsible AI development - it empowers us to create systems that truly serve the needs of people. I always tend to -Went beyond just technical benchmarks - focused on user engagement and unintended impacts -Utilized statistical analysis and visualization to identify patterns and trends -Conducted qualitative research for deeper user insights -Took a holistic approach to inform improvements and ensure beneficial outcomes -Continuously monitored and evaluated the system's performance and impact

As part of analyzing results, do not forget who you will be presenting your findings to, for example the project sponsor: Project sponsors are primarily interested in the project's return on investment (ROI). During the analysis phase, translate your findings into clear, actionable insights that connect directly to the project's goals and business objectives. For example, demonstrate how a reduction in error rates translates to cost savings or how improved user engagement with the AI system leads to increased customer satisfaction. By presenting data in a way that highlights the project's overall business value, you can ensure continued support and resources from the sponsor.

Start with Exploratory Data Analysis to understand the data well. Most likely, you will understand what the accuracy of your system is likely to be. You must slice and plot your data in multiple ways to do this. If there are too many variables, choose the ones that are showing maximum correlation with the dependent variable.

- 📊 Setting clear, measurable goals for what you expect to achieve with your AI projects. This includes defining specific metrics that will allow you to assess the impact of your work, such as efficiency improvements, cost savings, or enhanced user satisfaction. - 🔄 It's essential to continuously collect and analyze data related to the performance of your AI systems. - 🛠 Adopt an iterative development approach. Make small, manageable changes based on your performance analysis and monitor how these adjustments affect the overall system. - Engage with end-users and stakeholders regularly 💬. Their feedback is invaluable for understanding the real-world impacts of your AI solutions.

The analysis is not a one-time event. The insights you glean should guide adjustments to optimize your AI system's impact. This might involve fine-tuning algorithms, refining user interfaces based on user feedback, or adjusting data collection methods to address bias. Communicate these adjustments clearly to stakeholders, especially the project sponsor, explaining how they address the identified issues and contribute to the project's overall goals. By embracing a growth mindset and fostering a culture of continuous improvement, you ensure your AI project stays relevant, effective, and delivers long-term value.

After analyzing your data, you might find that some changes are needed to make your AI work better. This could mean adjusting the algorithms, making the user interface easier to use, or changing how data is processed. It's important to keep checking and improving your approach regularly. This ensures that your AI not only keeps up with what users need but also stays effective and relevant as those needs change.

Analyze feedback from end-users and stakeholders to identify areas for improvement. Identify any unexpected outcomes or challenges encountered during implementation. Adapt the AI algorithms or models based on the analysis to enhance performance. Implement iterative testing to assess the effectiveness of adjustments made.

The last step is to share what you've learned about your AI's performance. Creating clear and straightforward reports that everyone can understand, whether they're tech-savvy or not, is crucial. In these reports, it's important to talk about both what's working well and what could be better. This openness helps everyone understand how the AI is doing and what impact it's really having, making it easier for everyone involved to make informed decisions about the future of the project.

Summarize key findings in a concise manner. Use visual aids like graphs or charts to present data effectively. Craft a clear and straightforward narrative to explain the results. Tailor the communication to the audience, avoiding technical jargon. Provide actionable insights and recommendations based on the findings.

𖡻 Ao invés de simplesmente apresentar relatórios estáticos, transforme as descobertas em ‘interactive and immersive narratives’. 𖡻 Por exemplo, se sua IA otimizou o fluxo de trabalho de uma fábrica, crie um tour virtual pela fábrica que demonstra as melhorias. Os usuários poderiam interagir com diferentes cenários, visualizar dados em tempo real e compreender o impacto das decisões da IA. Para isso, considere: ⇛ Colaborar com empresas ou profissionais experientes na criação de ambientes virtuais imersivos. ⇛ Utilizar narrativas envolventes para contextualizar os dados e tornar as descobertas mais impactantes. ⇛ Explorar opções de hardware e software que sejam acessíveis para um público amplo. #ai #ia

Communicate findings visually so that the audience "get" the message with just a glance. If you have to explain the findings, then it is not very impactful. Say briefly how you measured it and number of measurements made in what period. Be ready to answer if you are challenged on anything you are presenting. Feel proud of your work - that will reflect directly in your confidence when you present. End the presentation with a "Call to Action". What do you want the audience to do after the presentation? Good luck!

In the beginning, we started with connecting the input metrics like accuracy, precision, recall, to output metrics like costs, revenue, profitability. Now once the input metrics are achieved, it is important to communicate to all the stakeholders how that is actually moving the output metrics. Have dashboards ready that show how the output metrics are moving. Do a deep dive 1 week, 1 month, and 3 months after implementation to understand what three in ground impact has been. Use this data to communicate effectively to all the stakeholders how the project has impacted their bottom line.

If want to measure the impact of your AI work effectively you should include sustainable AI & ethical AI practices you achieved with your AI project: - Are you using Sustainable AI: AI consumes lots of energy for compute and ensuring your AI project is scoped to use the right resources which are environmentally responsible, ethically designed, and socially beneficial is important. Do you need to use a cloud solution or you can use a local datacente or an AIPC to solve the need? Always consider the best option for sustainability. Foster long-term positive impact without compromising on your project outcomes. - Are you using Ethical AI practices: Prioritize fairness, transparency, accountability, and respect for user privacy and rights.

Impact could be measured by three key pillars: Application, setting and end-users. For application, the metrics should be chosen depending on the literature and business needs. In your specific setting - whether it's academic or industrial - consider factors like publications, patents and their impact on the community. And to measure impact for the end-users, think about metrics that reflect real-world outcomes, like time saved, accuracy, number of users benefiting from your solution etc.

In addition to the above metrics, businesses should establish mechanisms for ongoing monitoring of AI performance and impact. This allows you to detect issues in real-time, intervene early, and continuously optimize AI models and systems for better results.

Considere a importância de feedback contínuo dos usuários para iterar e melhorar o sistema de IA. 📝 Ferramentas Úteis SurveyMonkey: Ótimo para coletar feedback dos usuários através de pesquisas. JIRA: Utilize para rastrear e gerenciar feedback e ajustes no projeto. 💡 Além de analisar dados e métricas, ouvir diretamente dos usuários pode revelar aspectos do seu sistema que apenas os dados não mostram. Priorize essa interação para refinamentos contínuos.

When measuring the impact of your AI work, consider other factors too. Look at things like user feedback, expert opinions, and real-world outcomes. Also, think about any unintended consequences or ethical concerns. By considering these factors, you can get a more complete picture of your AI's impact and make better decisions about how to improve it.