Council Post: Measuring the success of an AI engagement

Measuring the success of an AI engagement
Image © Measuring the success of an AI engagement

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The world has become a playground for continuous digital disruptions where AI applications have become synonymous with a company’s growth. But the pitfall is that organisations are quick to create AI engagements while lacking the techniques to actually measure their success. Although we know that AI adoption leads to better business outcomes for many organisations, this is claimed without enough evidence of success across various parameters. 

Given we are still in the early stages of AI implementations, measuring their success is still a grey issue. Most organisations are informed on leveraging the technology but not on evaluating it. At best, they measure the impact on ROI and a few other KPIs.

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We have to start measuring impact on varied metrics to see sustainable and holistic AI-led growth in an organisation. This article will discuss the six major metrics and their KPIs for organisation leaders to evaluate their AI engagements against. 

Financial metrics: 

The goal of any business is to make a profit through its products or services. The financial impact is possibly the most important metric to test the success of your AI engagement. 

Financial KPIs consider two aspects; the company’s investments and returns. First, KPIs like cost per acquisition and burn rate gauge the company’s financing or investments. Second, KPIs like revenue growth, revenue per employee, revenue per account, gross profit, net product revenue, cost savings and operational cash flow assess the output to ensure there is the net gain after integrating the AI engagement. 

Customer metrics:

Customers’ feedback on the AI interventions is critical. Therefore, closely tracking customer experience is key to identifying how the technology is being received or how well it is used. 

For instance, if AI intervention replaces customer care professionals with a virtual chatbot that only answers queries through automated responses, consumer interaction may decrease, followed by reduced engagement. Here, reduction in KPIs like customer satisfaction score, consumer interaction ratio and customer engagement rate informs the organisation that the AI engagement is not well received and needs work. 

Other important KPIs cover two sections; retaining customers and seeking new consumers. KPIs such as customer loyalty and monthly active users measure existing customers’ response to the AI interventions. Additionally, customer conversion rate and customer pipeline help study if and how the AI engagement has brought forth new customers. 

Service delivery 

Organisations need to set standards for themselves by ensuring efficient service delivery for the clients. Such service deliveries can be measured through KPIs like ensuring SLA adherence; reduction in quality issues related to data, reports or models; and quicker turn-around time.

Additionally, the integration of AI should lead to standardising organisational processes, making the service culture agile and flexible. This can be measured by identifying the time taken to work on common tasks and studying the number of activities/processes that have been standardised.

Employee metrics 

The AI engagement needs to ensure a better employee experience and overcome employee-related challenges. It is critical to a firm’s development that the employees grow with the company through continuous learning. AI integration should automate repetitive and menial tasks, allowing employees to leverage their time in upskilling their capabilities.

KPIs such as the employee engagement score, new capability development and rate of upskilling can be used to track employees’ experience and development during the course of an AI engagement. This can be measured through employee feedback forms, quantifying certifications an employee accrues over time, the kind of projects they are participating in or eagerness to try new tasks. 

KPIs such as reduction in churn, new hire retention and new hire success rate can be measured to understand the extent to which AI is impacting the workforce. 

Culture

Many organisations build AI models, but their adoption remains a challenge. After building and implementing a model, one must ensure adoption. This entails taking a macro and holistic approach to ensure that the employees apply and adopt AI. 

The widespread use of AI helps increase the speed of decision making. Additionally, the integration of AI should create an entrepreneurial mindset among the employees. Measurement metrics include rate of idea generation, idea to project conversion ratio, creation of intellectual assets like patents or research papers and R&D budget.

Compliance and ethics

While AI can be very rewarding, it can also go wrong in many ways. It can infringe on people’s privacy or provide incorrect solutions if the training dataset is faulty. Hence, companies integrating AI need to monitor its compliance with ethics and sustainability to ensure fairness in the AI engagement. 

For instance, let’s consider an AI-based talent screening system for hiring new employees in a technology organisation. If the model is trained on biased data, it could select candidates based on racist considerations. Therefore, KPIs like data quality and model bias must be measured regularly to ensure the AI model is ethical. 

Other important KPIs to ensure the model is fair and secure include model explainability, efficacy rate, and data privacy. Organisations can ensure these by complying to current data regulations and ethical standards, adhering to the security control and policies, having privacy standards in place and providing employees with the right access to fair mechanisms. 

As India is embracing a digital ecosystem, we must build a sustainable AI growth culture. Identifying and monitoring the success metrics is key to reaching the goal of making India one of the top AI hubs in the world.

AI is extremely advantageous, but it is not a one-way ticket to success. Leaders must maintain their focus on outcomes and tread ahead with holistic awareness.

This article is written by a member of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives in the Data Science and Analytics industry. To check if you are eligible for a membership, please fill the form here.

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Swati Jain
Swati Jain is a seasoned professional with over two decades of analytics and consulting experience across multiple verticals, including financial services, retail, media, logistics, and healthcare. Currently, she is the Vice President, Decision Analytics at EXL Service. She leads a world-class team of data scientists and AI/ML professionals impacting marketing, risk management, and operations of several Fortune 500 organisations. She has been instrumental in developing multiple award-winning innovative AI solutions along the customer journey, propagating newer business models and democratising AI in client organisations.

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