(Part-1) of the Series- AI for Fast Customer Decisions & Resolutions
AI is going to be the next normal, in the field of business. And it’s a fact that businesses who adopt AI in some or the other way will be ahead of their competitors.
But how can you actually use AI to revolutionizing CX? By using it for data processing and analytics, predictive analysis, real-time monitoring, machine learning and a lot more.
In this post, we will understand different ways in which businesses can incorporate AI in their day-to-day operations, to better their customer experience.
Why is it tough to integrate AI in businesses?
Most business environments are fairly complex, where decisions and specially customer resolutions are dependent on a multitude of variables which can sometimes even be conflicting. A number of policy matters and rule engines along with cost matrices may be applicable, with need for approval structures & hierarchies and so on - which make the process complex and time consuming.
In a customer centric environment, for instance in a call center, front office or branch office, where consumers reach out with their concerns, looking for quick resolutions, this becomes a challenge. Such situations often result in front office staff having to seek extra time to revert with an answer or a solution, which may be non-standard and need to be customized to the specific customer need. Clearly this doesn’t augur well from a CX standpoint, leaving the customer feeling dis-satisfied.
This is possible since AI has the ability to think and act in real time, based on data structures and learning patterns.
AI can revolutionize CX through data processing and analysis
Data contains information that enables us to make better decisions. Processing is the means by which this information is extracted and action can be taken. Humans and AI are both data processors with very different capabilities.
It’s not just about automation. AI algorithms can rapidly analyze vast amounts of data, extracting relevant insights and patterns that would be impractical for humans to process manually. This speed is crucial in complex scenarios where quick decision-making is required.
By leaving the processing of large amounts of data to machines, we limit errors related to transcription, but also to judgments. Reducing human intervention also increases the security of data protection
Predictive Analytics
AI can predict potential issues or opportunities by analyzing historical data. This helps businesses anticipate problems and address them proactively, rather than reactively, resulting in faster resolutions and better decision-making
Machine Learning for Decision Support
AI can assist in complex decision-making processes by providing data-driven recommendations. For instance, in finance, AI algorithms can help identify investment opportunities or risks quickly and accurately
Real-time Monitoring
AI systems can continuously monitor and assess complex systems and environments. For example, in cybersecurity, AI can detect and respond to threats in real-time, preventing potential security breaches
Process Optimization
Optimization decisions are those where the AI finds the best option given a set of objectives and constraints. AI-driven process optimization identifies bottlenecks, inefficiencies, or areas of improvement in complex business processes.
By automating these improvements, AI contributes to faster and more effective operations
Risk Assessment
Whilst AI enables better and more efficient data-driven decision making. However, risks can also lurk. AI-based decisions must be legal, transparent, and subject to human oversight and review.
Legal reform is still needed in order to make these types of decisions more accountable and in order to protect people from any potential harm after these decisions are taken. In order to fully unlock the value contained in their data, companies must integrate AI into their workflows, and to sometimes replace humans.
In industries like insurance or lending, AI can rapidly assess and underwrite risks based on a wide range of data points, expediting decision-making and reducing the time it takes to provide services
Supply Chain Optimization
AI assists in optimizing supply chains by predicting demand, identifying potential disruptions, and recommending efficient routes. This results in faster and more reliable delivery times.
Next-generation data-driven AI is a game changer for consumer-centric businesses. If you are a key decision-maker, you will appreciate that the speed and accuracy of your decisions are critical to the customer experience you are able to offer and eventually also to your business success
CX Leaders must learn to use AI/ ML, in order to improve their decision-making skills if they want to stay competitive in today’s economy. It is important to make decisions based on the right data and at the right time. Efficiently combining human intelligence with data-driven AI ultimately maximizes the potential of the business and customer delight!
Conclusion
CX is an ongoing and evolving process; it is not defined and executed only once. Keep your company abreast of CX benchmarks and adopt a consumer cantered innovative CX approach.
If you think this post was helpful, then check out the upcoming CX trends. So, that your innovation department can innovate around them to elevate your business.
Also, if you need any help regarding your business’ customer experience connect with us anytime at anjali@c-xcel.com or call us at +91 9599496661.
You can also listen to us on our C-Xcel podcast!
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