For most of us, as consumers, Artificial Intelligence is synonym of Amazon Alexa, Google Nest or Apple Siri: Smart devices that provide varying levels of expert assistance.
As expert in digital transformation, I am, like many of us, looking at AI and its potential use cases: How can AI support organisations with reducing costs, improving production efficiency, and enhancing the customer experience? I have been fortunate enough to be involved very early in projects involving AI and Natural Language Processing functions. A great experience with a lot of lessons learned!
It is fair to say that numerous initiatives are emerging and vendors are continuously developing role-specific AI-enabled applications. In many cases, they are adding AI capabilities to their Customer Engagement platforms, CRM, and increasingly to their industry verticals, ranging from financial services, manufacturing, retail, to education and healthcare to mention a few.
Nearly all industries show signs of AI implementations today. Just in the recent few years, I have observed many organisations experimenting AI solutions, primarily with language / voice recognition and advanced machine learning.
As an example, retailers are implementing chatbots and virtual assistants for personal shopping and product recommendations as a way of augmenting their online customer experience. At the same time, they are developing other use cases focusing on automated customer service agents and dynamic pricing.
Another example is in the automotive industry, where car manufacturers are investing into self-driving trucks, cars and buses. In doing so, they are connecting AI solutions with huge Big Data and Internet of Things systems. As we see, everything is connected.
In short, a large variety of use cases is possible. AI connected to CRM has the potential to transform business processes to create new offerings, improve the effectiveness and ROI of marketing campaigns, optimise customer service operations, or identify sales approaches for particular customers.
More CRM trends#1 – Everything is cloud
#2 – Voice Interfaces are on the rise!
#3 – CRM systems are increasingly powered with Artificial Intelligence (AI)
It is no secret that too many choices bring indecision.
As a result, top managers often have issues in understanding how to exploit AI. The identification and prioritisation of the most appropriate use cases for an organisation may remain challenging. Often, it ends up in many scattered small initiatives across several business units – not aligned. It is difficult for business leaders to decide on where to invest.
The data quality is not a given either within most firms as we are probably all well aware. Siloed and isolated data is still very much a reality. However, access to the data is crucial for the AI platforms to work effectively.
It is therefore fundamental to have a structured approach when approaching AI.
When I started to work with AI, the initial focus from managers was about technology, but quickly the conversation would derive to digital services and the new value creation.
My point is that AI must be considered as a ‘journey’ and should always be seen in this context. AI maturity progressively grows in the same way as the digital maturity of an organisation grows. It is part of it. AI is driven by digital transformation.
3 steps are especially critical: First, organisations should identify the initial use cases where AI can be leveraged. Secondly, they should develop personas and scenarios of how users would interact with the AI system. Lastly, as in any digital project, they should define hypotheses and clear metrics about how to measure the success.
In conclusion, I would ultimately argue that AI adds a total new dimension to CRM and creates a series of new business opportunities for organisations – Revenue growth, cost reduction, and enhanced customer experience.
For most firms, the main question remains how does AI change the way we can create new value for our customers. Increasingly I will need to have those discussions with my clients. It will surely be on business objectives, AI-enabled operating models and processes, less on the technology itself.