What does it take to make a good recipe? A good chef or the appropriate quantity of ingredients, or both. I wish making the machine learning project a success was that easy. It will be one day.
Even when you have sufficient data infrastructure and have established a culture for change management, an accurate model is in place, but projects fail. Alas! Though they play a critical role in defining the success of the AI/ML project, what and where are we doing wrong? We might be overlooking other ingredients that can influence AI/ML conquest.
Who plays the role of a chef? A data scientist or a domain expert?
To picture the holistic and sustainable AI-led growth, there is a requirement we evaluate its impact on various metrics. Pillars that help us gauge the success of the AI/ML project.
1. Setting up your steps for AI/ML project success.
- Identify your objectives and use case: An indefinite business problem that leads to an achievable business objective is necessary. Starting with incremental steps would help unclear business goals. In spite of looking for the solution, organizations must begin with a well-defined business problem that will clarify whether looking for an AI solution is a must or not. Setting up for moon shots will not benefit, but setting small goals to build trust in AI/ML model and scale incrementally will reap tangible benefits. We cannot argue over the fact that ML is an experimental field where it goes through a trial-and-error process. Looking at AI to help solve existing problems or improve current processes rather than as an opportunity for new business avenues. A clear picture of your goal enables you to measure accomplishment accordingly.
- Comprehension of data standards: The projects to work efficiently and provide acceptable results rely on the right data for the problem at hand. It should be clean, formatted, and organized to train the model well. So after selecting a use case, it is followed selecting a valid dataset for the same.
- Launching pilots: Begin with the projects that lead to a quick win and help you boost your confidence in the technology—making a small team that will work under a restricted time frame towards an achievable goal. A purpose-driven path will help a team to assess performance. A team where domain experts and data scientists work together will help align projects toward organizational priorities. The success of this project is essential as it will help in forging trust in AI/ML. Even if this initial win does not provide significant value in the short term, its success contributes to persuading other corporate executives to invest in AI/ML for future initiatives.
- Choosing a leader and building relationships:
Establishing cross-functional teams where data scientists and domain experts work together is imperative. A domain expert helps roadmap a problem to embedding it into production, where data scientists work on it to build a data-driven model. Domain experts better comprehend concerns and their influencing factors, helping data scientists strategize solutions.
2. Evaluating Success.
Your project’s success is not complete until you successfully evaluate its performance. There is a chance that an accurate model will not achieve the desired business goal. Business performance and model performance play different roles in determining success. Business performance depends on model performance and other variables.
A good model performance doesn’t guarantee good business performance.
Domain experts can best judge the model performance as only they can decipher if objectives are mapped to the outcomes.
Business performance indicators are often known as trailing KPIs, as they must be defined prior to using an AI solution. Metric evaluation is vital in establishing whether or not goals are met. These KPIs fluctuate depending on the business model. After installing the AI solution, a product manager will utilize product operational KPIs to measure performance, such as financial metrics(revenue growth, cost per acquisition, operational cash flow, and so on), Customer metrics( customer retention rate, customer satisfaction, customer engagement ratio, etc.), service delivery( improved turn around time, reduction in quality issues, efficiency, etc.). There are other metrics like employee metrics (churn rate, new hire success rate, rate of upskilling, etc.), culture (adoption rate of AI models, speed of taking decisions, etc.), ethics and compliance ( data privacy standards, model interpretability, model bias, etc.).Several metrics are linked with a business’s goal, but only those indicators are identified as KPIs, which are fundamental and critical in establishing the target.
Model performance: At times, business performance metrics are not enough to measure the effectiveness of AI solutions. For this, model performance metrics have their role to play. These technical indicators vary depending on the algorithm used. For various metrics, we can gauge the properties like how well an algorithm can perform classification justifying its accuracy (confusion matrix, precision, recall, etc.), the ability of an algorithm to generalize, and the deviation of prediction from reality (RMSE, MAE, etc.).An example to explain model performance-enhanced email usage cannot be enough to test the efficiency of spam filters until appropriate isolation is performed in some way.
3. Communicating Success:
After accomplishing milestones in your AI/ML solution, it’s time to let others in your company know about it. Communicating success will help other business leaders and their teams choose roadmaps to AI solutions. Setting a small benchmark helps an organization achieve broader goals. AI will acquire traction when many stakeholders can witness results. Success is entailed by comparing current business stances over the past. In terms of value communicated as revenue, growth, time, capital savings, and return on investment. These attributes are worth sharing as they will define the real business impact of AI/ML applications.
A wrap up:
Success in AI/ML implementation always relies on your initial steps’ strength. They serve as the foundation for at least mastering AI in a vertical industry sector and giving an edge over your competitors. Choosing the right use case, executing it, and evaluating it is imperative for measuring success. So, if your organization has yet to invest in artificial intelligence, now is the time.
Adopting AI has become more accessible and manageable in recent times. With the advent of no-code AI tools, technical barriers have been lifted for domain experts, allowing them to utilize their expertise to deliver better results in their respective fields. These ready-made tools enable a focused, time-bound team to achieve its goals in a close timeframe. They can experiment and collaborate to attain seamless outcomes at an affordable cost. Given the scarcity of data science talent, upgrading our expertise and gaining a competitive edge is wise. Domain experts may not be data scientists, but they deeply understand business processes. With a basic AI understanding, they can easily experiment with AI using these tools without delving into its complexities.
You don’t need to know how a lamp works to turn it on.
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