AI is one of the most powerful tools to accelerate the digital transformation of enterprises. With the availability of a plethora of technologies and outcomes driven by AI, business leaders should have a clear picture of which AI suits their business goals best.
AI applications for business
While the goals of academic AI research are specific, (strong AI, cognitive simulation & applied AI), enterprises can focus their attention on the applications of AI to understand how AI can drive business outcomes. In a broad sense, AI supports a few important business goals: improving efficiency, growing revenue through discovery and insights, and improving customer satisfaction.
In a recent study of over 150 enterprise technology projects, The Harvard Business Review reported that most of the technologies fell into the following buckets-process automation, cognitive insight, and cognitive engagement.
This is the most popular technology used in enterprises to automate back-office tasks. Robotic Process Automation (RPA) is easy to implement and offers significant ROI. RPA robots can perform tasks that require acquiring inputs from multiple IT systems. Some of these tasks include transferring data from internal systems such as email, to systems of record. In the financial sector, tasks that require updating of records in multiple systems such as replacing lost credit cards can be automated via RPA.
Organizations use machine learning-driven analytics to make informed decisions on predicting what customers are likely to buy, dynamic pricing, detecting credit fraud in real time, algorithmic trading, to name a few. Machine-learning models are trained on massive sets of data and the models perform better over time as new data comes in. Here you will find applications of Deep Learning as well as Natural Language Processing (NLP). For example, an auditing firm can use cognitive insight technologies to rapidly scan documents to extract terms to help auditors evaluate contracts and audits faster and better.
One crucial business goal is improving customer experience (CX) along with increasing engagement and raising conversion rates. The use of conversational agents like chatbots and digital assistants that are driven by NLP aids in meeting this goal. These conversational agents vastly improve customer experience by guiding customers to the relevant content faster. Here recommendation engines can add to the CX by providing personalized products, services and content to customers based on their buying behaviors, stated preferences and affinity groups. In the HBR study, it was reported that the organizations largely used cognitive engagement technologies to improve communications with employees, as seen in internal sites created for answering employee queries on most HR, onboarding and benefits-related topics.
Now it is possible for companies to use all three categories of AI business goals (process automation, cognitive insight & engagement) for complex projects that aid the digital transformation of the business. As business leaders become more aware of the kinds of AI technologies available, they will gain a better understanding of how their goals can be met with AI.