Many businesses are just beginning their AI journey. The big question is where AI would deliver value for the business. To implement AI using a pragmatic approach, return on investment (ROI) and implementation complexity are two key factors to consider. These two factors address top-of-mind questions business leaders have when identifying quick-wins versus longer-term investments.
Based on industry data and enterprise outcomes on AI deployment, the following shows use cases categorized by ROI (return on investment) and implementation complexity. ROI is the expected impact within 6-12 months. Complexity is ranked based on data readiness, systems integration, governance and skill requirements.
High ROI, Low Complexity
A good starting point for AI deployment is for use cases that deliver value quickly. When good data is readily available, and implementation does not require much effort to establish or refine ethical guidelines and privacy controls, rollout can take place within a short period of time.
Customer service chatbot implementation is a common example. Using readily available data from FAQs (frequently asked questions) and historical customer interaction logs, AI-powered chatbots are trained to handle routine inquiries eloquently. Customers are happy with the faster response time. The business reduces cost per contact. Chatbots are available 24/7 to support customers in all industries, freeing up agents to deal with more complex tasks.
Early success helps to build knowledge and confidence in AI. It is an excellent way to generate excitement and build momentum to expand AI deployment.
High ROI, Moderate Complexity
This group requires data consolidation from different systems such as ERP, logistics and external sources. Success hinges on data quality.
Sound supply chain demand forecasting and inventory optimization boost the bottom-line. Inventory comes with a carrying cost. Demand forecast accuracy minimizes excess inventory which leads to significant savings for the business. For retailers, it minimizes stockouts. AI can predict demand more accurately and optimize inventory turnover using historical sales, inventory data, marketing campaigns, and seasonality.
When done well, businesses can achieve significant ROI. With progressive experience built on improving data quality, it enables the business identify areas where it can leverage data and AI for optimal benefits.
Moderate ROI, Moderate Complexity
Deployments in this group require integration of systems, structured data with good governance. Challenges such as data quality, skill gaps, and buy-in are main barriers to scaling.
AI-powered financial forecasting and variance analysis improve productivity. A KPMG survey of 2,900 organizations across 23 countries showed that 71% of surveyed organizations use or pilot AI in finance. Improvements in forecasting cycle time and accuracy help leaders make more informed decisions faster and reduce analyst time in performing the work.
Success depends on well defined data structures and integrations of systems. Learnings serve to build data foundations and cross-functional alignment, preparing the organization to deploy AI for more analytics-based activities.
Despite the potential benefits AI has to offer, it is not a panacea to all business problems. Alternatives such as rule-based robotic process automation is a better fit for manufacturing or transactional processes that require precision. Availability and accessibility of relevant and reliable data are critical for successful AI adoption. AI changes job roles but it is far from replacing human. Human intelligence is still a necessity to critique and refine AI output. This means investment in upskilling talent is a must for building internal competency for scaling AI deployment.
If you find this useful, you might like Essential Skills for Working with Generative AI.
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