customer transaction
Applying AI to decision making can improves customer experiences for both the customer and the employees helping them PHOTO: Jason Briscoe

Many define artificial intelligence (AI) as machines that can think — and perhaps even act. As businesses and consumers make decisions every day, AI-infused customer experience solutions that include decision-making components can radically affect how, when, and why customers actively engage.

Many brands still rely on gut feel and a cookie cutter approach to govern the majority of their day-to-day sales and customer service decisions, though. They lack the ability to be proactive in automating simple decision-making or providing customer-facing employees with genuine insights to guide their more complex decisions. 

Embracing the Potential of AI 

Often, it’s because brands’ analytics aren’t applied or operational. The very fabric of their systems is rigid — designed for reliability rather than robust adjustability.

AI’s new possibilities have dramatically changed the landscape. Using AI, today’s machines can make a wide variety of unassisted decisions, while augmenting countless others. Yet, the vast majority of companies still operate with decision systems that are sorely outdated, rarely add value and are unable to keep step with technological advances, competitive pressures, dynamic markets or modern consumer expectations.

Add the Power of AI to Your Decision-Making

But where should companies begin as they seek to build AI-friendly decision-making capabilities? And how can your company begin to incorporate the potential of AI to improve its customer experiences? 

Here are four suggested strategies that will move your organization toward significant success, radically improving how you leverage AI to interact with your customers and improve their experiences:

1. Incorporate AI into Your Decision-Making Support Systems

Back in the '90s, programmers built Decision Support Systems with the goal of providing valuable data to managers making decisions. Usually in the form of weekly or monthly reports, these systems provided some assistance, but couldn’t produce data and insights relevant to rapidly changing business dynamics.  

By contrast, AI allows for the creation of decision management systems that don’t just support decision-making but actually make decisions. Using artificial intelligence and machine learning, such systems can adapt to changing marketplace conditions and fine-tune themselves toward more optimal decisions and outcomes.

2. Let Your AI Decision Making System Orchestrate and Arbitrate Experiences

Serving your customers involves trade-offs: Should I refund this customers’ purchase? Should I offer this customer a discount? Should I route this customer to my best agent? If you’re a large business, you face these decisions thousands, if not millions, of times a day. The outcome of each decision may not be apparent that day or the next, but the cumulative result of those decisions matters immensely to the final score.

Incorporating AI into your decision management system allows for making cross-functional decisions at scale and in real-time. Think of it as an automated layer of management, directed and monitored by real people, but carefully empowered to take instantaneous omnichannel actions — and even learn from them. 

Using a customer engagement hub with an AI brain and a decision-making module lets you configure sub-strategies that each play a specific role in decision arbitration. For example, as a customer experience professional, you task one strategy with assessing the best action for service outcomes. Another assesses the best product recommendations, while yet another takes on gauging customer risk, and so forth. 

Then, the magic happens. When a customer engages with your brand, each strategy performs its role, calling on all its historical knowledge of that customer. Enhanced with real-time contextual information, each sub-strategy returns its best assessments for that individual. 

A master strategy serves as the final mediator, weighing the output of each sub-area and invoking its arbitration rules to deliver final decisions. The system can then send those decisions directly to your customers through a virtual assistant such as a chatbot or provide prompts to an agent conversing live. 

3. Put Decision Management at the Center of Your AI Investment Strategy

Earlier this year, Forbes published an article entitled, "Top 10 Hot Artificial Intelligence (AI) Technologies" which summarized Forrester’s Q1 2017 Tech Radar on AI (fee charged). In the report, Forrester identified decision management as the AI technology most strongly poised to deliver value over the next five to 10 years.

Forrester covered many cutting-edge AI innovations, including deep learning and swarm intelligence, yet decision management came out on top of the value curve based on the thinking that, to extract value out of AI technologies, businesses must transform insights into actions. 

4. Do Your Homework and Get Started 

Unlike the never-ending, built-from-scratch data warehouse, CRM and Enterprise Resource Planning projects of yesteryear, today we enjoy modern iterative development methods, and prepackaged solutions that reduce project risk and provide faster time to value. An AI-based customer engagement effort should be no different.  

The key is selecting vendors with proof of successful implementations, flexible and open interfaces, relevant pre-built Integration Packs and an agile approach to both software and services delivery. Especially if you are a large enterprise, you’ll need more than one vendor, so carefully choose the key partners — particularly the decision management experts — who will play central, long-lasting roles. 

Closely inspect their software, experience, and reputation to make sure its future proof. Challenge them to marshal evidence of successful outcomes, interoperability, scalability and risk mitigation. Then, get going before your competitors outflank you.