How do you track AI project success?

To ensure AI project success, set SMART goals in line with business needs and stakeholder requirements. For instance, when developing an AI chatbot, aim to boost customer satisfaction, reduce response times, and increase sales conversions. Before implementation, establish baseline measurements for accurate impact assessment. Ensure ethical and regulatory compliance. Maintain transparent stakeholder communication with regular progress updates. After the project timeframe, conduct an assessment to evaluate SMART goal achievement and value delivery. In AI projects, success relies on setting, measuring, and adapting goals for desired outcomes. Your dedication to this process is pivotal for overall success.

Beyond aligning with business objectives or using SMART criteria, I believe it's vital to consider the adaptability of these goals. The AI landscape is rapidly evolving, and what may seem a priority today might be obsolete tomorrow. So, for our AI projects to truly succeed, our goals should be flexible enough to evolve with technological advancements and market shifts. Being proactive in this approach ensures our AI solutions remain relevant and effective in a continually changing environment.

Tracking AI project success demands clear, SMART goals linked to business objectives, customer needs, and ethics. Metrics like customer satisfaction, response time, and sales conversions gauge success. AI enhances project management via progress monitoring, project readiness identification, and task automation. Assess opportunities scope and apply agile practices. Prove AI's business value through measurable revenue, cost, quality, and satisfaction outcomes. These practices enhance AI project tracking and boost value for organizations and society.

🤔 How do you track AI project success? It really comes down to this question ... How do you track success in ANY project, not just an AI project. Yes, setting SMART goals is a good idea but more importantly .. start with the problem you are solving. Before starting a new AI project, ensure you and your team are crystal clear on what problem you are set to solve in the market. Ensure the problem you identified is clearly articulated in (ideally) a project charter - read online how to create one. In your project charter, you also want to clearly outline what is in scope AND what is out of scope. Additionally, you want to have multiple success metrics and consequential metrics (look up the definition online). 👍 Like to support this

SMART is SMART, but you can be SMARTER than that, by adjusting your objectives to be as specific as possible, if necessary giving extra weight to one measure over another. But for sure, the primary objective has to be enhanced user/customer satisfaction. All SMART elements should optimize this objective.

The choice of metrics depends on what your AI project is meant to achieve. For example, if it's about making predictions, you might use metrics like accuracy or precision. If it's about making users happy, you could use something called Net Promoter Score (NPS). But be careful, each metric has its own strengths and weaknesses. So, you need to understand them and choose the right one for your project. Also, make sure the metrics you choose don't accidentally treat people unfairly or unfairly favor some groups. This is important to make sure your AI project is ethical and fair.

Again, the metrics should be focussed on obtaining the optimal user/customer satisfaction and experience. In this regard, value-added and timeliness are of prime importance.

In any project, selecting the right metric is crucial to meet the specific business requirements. Recall, for instance, plays a vital role in identifying all relevant instances within a dataset, especially in contexts where sensitive information, like medical diagnoses or fraud detection, is involved. The F1 Score, on the other hand, strikes a balance between precision and recall, proving highly valuable when dealing with datasets that exhibit imbalances.

For AI projects to be successful, it is important to connect model performance metrics to business KPIs. The purpose is to generate business value, rather than simply building models with the highest performance. Applied AI focuses on designing solutions to 'optimize the entire user journey or business workflow' by integrating multiple point solutions. The success is governed by factors more closely tied to the business KPI

Choose AI project metrics Once you have your AI project goals, you need to choose the appropriate metrics to measure them. Metrics are quantitative indicators that help you evaluate the performance, quality, and impact of your AI solution. Depending on your goals, you might use metrics such as accuracy, precision, recall, F1-score, ROC curve, AUC, MAE, MSE, RMSE, NPS, ROI, or others. You should also consider the trade-offs and limitations of different metrics, and how they reflect your AI project values and ethics.

While I deeply appreciate the advantages of agile methods in managing AI projects, I've also been pondering the importance of fostering a culture of continuous learning within our teams. AI, by nature, is about teaching machines to learn. Similarly, our project teams need an environment where they are encouraged to learn from every iteration, mistake, and success. Integrating methods like knowledge-sharing sessions, or even regular team reflections can greatly enhance our project's adaptability and innovation potential.

Performance achievement is of prime importance, and this is best measured using a real-time digital dashboard, with agility and iteration very important to staying on track..

To effectively adopt AI project methods, it's essential to follow a structured approach. Begin with meticulous project planning, defining objectives, scope, and allocating resources appropriately. Collect and preprocess data, ensuring its quality and addressing privacy concerns. Perform feature engineering and select suitable machine learning or deep learning algorithms, followed by data splitting for training and evaluation. Train the model, fine-tune parameters, and rigorously evaluate its performance on validation and test sets. Deploy the model with scalability and reliability in mind. Address ethical considerations. This systematic approach ensures that AI projects align with objectives and deliver sustained value.

To effectively monitor AI project success, it's crucial to utilise appropriate methods and tools for gathering, examining, and conveying your metrics. Many AI projects lean on agile methodologies, like Scrum or Kanban, favouring regular feedback and adaptability. These methods help manage the unpredictability inherent in AI projects, ensuring quicker, more efficient value delivery. Tools aiding agile management in AI might encompass data pipelines, dashboards, testing, and regular reviews.

When I consider monitoring the progress of AI projects, I can't help but think about the human element involved. Beyond the milestones and KPIs, it's essential to gauge the well-being and morale of the project team. The quality of their work-life balance, their alignment with the project's vision, and their overall satisfaction can be strong indicators of long-term project health. After all, even with the most advanced AI technologies, it's the people who drive a project to success, and their well-being can be a critical, yet often overlooked, metric.

Again, a real-time digital dashboard, as mentioned above, is the best way to monitor and assess project progress. And some of the KPIs should be to measure and assess future performance as you progress.

For effective AI project management, continuous monitoring of progress and performance is key. This allows timely identification and rectification of issues or capitalising on emerging opportunities. By consistently overseeing progress, you can reaffirm assumptions and outcomes, ensuring the AI aligns with expectations. Monitoring techniques encompass setting project milestones, leveraging KPIs, task tracking, diligent data logging, and periodic model audits.

I also feel that we should consider the longevity and scalability of our AI solutions. It's not just about how well an AI solution functions at the point of completion, but how sustainable it is in the long run. Can it adapt to new data sets? Can it scale with increasing demands? As I see it, true success lies in creating AI solutions that aren't just effective today but remain robust and relevant in the face of future challenges and growth.

The evaluation needs to be undertaken within the business and societal contexts. So for example, we need a clear assessment on how the AI solution contribute to the following: business bottom line, revenue increase, costs reduction, customer srrvice improvement, risks mitigation, environmental and social impacts.

To gauge AI project success, it's crucial to assess outcomes and discern the impact. This involves determining the efficacy of the AI solution in addressing the problem and achieving the set goals. It aids in gathering insights for refining processes and strategising future enhancements. Evaluative measures include utilising surveys, conducting interviews, focus groups, and case studies, and employing techniques like A/B testing and MLOps.

In reflecting on the measures of AI project success, I feel that a crucial dimension is represented by the ethical implications. While metrics, methods, and evaluations are essential, we must always remember that AI technologies can have profound societal impacts. It's not just about achieving our goals but ensuring that our AI solutions respect fundamental human rights, mitigate biases, and promote fairness. In essence, success isn't solely defined by numbers or feedback but by the positive and responsible mark we leave on society.

In my opinion,the success of AI project metrics must include metric for bias. Any AI project might go wrong and tend to make biased decisions. We must ensure bias ratio to be part of any AI project success. Another important is tracking human involvement or feedback metric which is crucial for AI project success

Tracking the success of AI projects has seen a notable pivot. Traditional AI projects often hinge on extended timelines, requiring years to amass sufficient training data and time to train models. Success is often gauged by data accumulation speed, model accuracy over time, and the expertise of data scientists involved. In contrast, LLMs, empowered by few-shot learning, require significantly less training data. As such, the timeline compresses, and the nature of expertise shifts. Rather than data scientists, LLM projects often lean on LLM API and prompt engineers.Success metrics for LLM projects might focus on efficiency of API integrations, the effectiveness of prompts, and the model's adaptability with minimal data.

You need future oriented KPIs to measure ongoing project success, as well as operational KPIs to measure performance along the project roadmap.