From The Turing Test to Today
It doesn’t take a genius to figure out that Artificial Intelligence (AI) has changed all kinds of industries and workplaces in a number of significant ways. Attitudes towards the use of AI have also shifted, along with the ways in which it has been approached. One of the biggest shifts has been the emphasis on top-down reasoning rather than bottom-up big data. This refers to applications and machines moving towards more intelligence rather than artificiality, increasingly resembling a human approach to problem-solving in the process. It has, in turn, led to more applications of AI than ever, with more opportunities across industries.
AI and Customer Relationship Management
The financial services industry has, unsurprisingly, been able to deploy AI in all kinds of ways. Products such as NexJ’s CRM empower customers to differentiate on service by anticipating customer needs to deliver a tailored customer journey, all enabled by AI.
AI lies at the heart of NexJ’s CRM, allowing it to improve customer experience and revenue outcomes, drive suggested next actions and proactive engagement. Our product also uses rules in marketing, sales, and customer service, as well as to manage processes such as generating customer insights or automating research distribution to clients. Another important cog in our wheel is backward chaining, also referred to as backward reasoning, which is an inference method that works backward from the goal and is used in a number of AI applications. It is, along with forward chaining, one of the most commonly used methods of reasoning with inference rules and logical implications.
The evolution and future of AI
AI began with the top-down model in the 1950s and abandoned it in favor of bottom-up machine learning methods only because technology hadn’t advanced enough to make the former approach possible. Research and computational techniques have now made it possible for a revival of those early approaches.
Today, AI makes workplaces more productive and efficient by capturing data better to discover meaningful insights and deploy those benefits. There is more data-driven decision-making, changes in how data is shared internally and externally, and an increased emphasis on real-time analytics. Also, there are shifts in how employees function as implementers of strategic initiatives based on data parsed by analytic tools.
There are still limitations to data-hungry neural networks, from trouble identifying anomalies to misidentification of objects that have been slightly altered. There are also privacy issues involved when it comes to using large amounts of data that belongs to citizens. There is also the worrying possibility of data being manipulated in unethical ways to suit specific purposes.
The future of AI lies in faster, more efficient systems that will rely on intelligence rather than volumes of data. One of the ways this is being done is through more efficient reasoning, training robots to have a human-like conceptual understanding of the world. Machines are also being taught to navigate the world using common sense, even though this is one of the most difficult tasks for machines.
Where does CRM come in?
The biggest reason why AI has increasingly transformed CRM platforms is that the platforms themselves now do much more than simply function as a repository for customer data. Award-winning CRM solutions such as NexJ effectively empower advisors and financial services employees to engage with customers better. This continuous engagement in real time plays a critical role in delivering service as well as revenue goals, and AI is the foundation of what smarter working environments such as our Intelligent Customer Management platform have been built upon.
Customers are increasingly comfortable with the use of AI, Machine Learning and Deep Learning tools that lead to more effective experiences and interactions with their financial services organizations. They engage with everything from chatbots to voice technology, allowing CRM solutions to introduce more AI into their offerings and workflows.
Managing data more effectively
The quality of data sets is what defines the effectiveness of AI in any CRM solution. It is only by studying patterns of behaviour from data that algorithms can learn and make predictions, thereby cutting down manual processes and boosting efficiency. As customer data becomes richer and more robust, a CRM solution that effectively deploys AI and Machine Learning can drive better insights at lower costs. Machine Learning algorithms can now leverage insights from big data at great speed, empowering advisors with recommendations in real time based on comprehensive views of their customers.
Interestingly, non-traditional sources of data such as call center recordings and posts on social media platforms can also be used to generate deeper insights that, in turn, deepen relationships between advisors and customers.
If you have questions about how AI, Machine Learning or Deep Learning can have a positive impact on your approach to CRM, why not get in touch with our experts today?speaker_notes Post Comments