If telecom companies truly operated with complete efficiency (maximum gain, minimum waste) and effectiveness (consistently doing the right things to operate and grow their businesses), we would never see failed acquisitions, mergers that failed to deliver intended benefits, marketing programs that failed to boost accounts or reduce churn, or technology initiatives that swiftly delivered the promised value.
Telecom firms--like all others--suffer from “friction.” As a practical matter, when all business processes become more frictionless, it should lead to outcomes such as higher lead-to-customer conversion rates, lower churn and higher account retention, plus higher renewal rates, as well as enhanced productivity (the ability to produce and sell more while reducing the cost of doing so).
To reduce friction, many agree that better insight is required of operations, customer experiences and expectations, supply chain and partner behavior. And almost everybody agrees at some level that artificial intelligence, machine learning or deep learning will underpin the efforts to glean much more insight.
But it often is hard to imagine how artificial intelligence can be implemented, as a practical matter, since AI is a capability, not a product; a learning system, not a discrete set of attributes.
One does not go “buy AI off the shelf,” so to speak. So it might be better to cast AI as a tool for reducing unwanted friction, where the theoretical scenario is:
100-percent efficiency and knowledge of buyer demands, preferences, tastes
complete understanding, in real time, of the state of a firm’s supply chain
as-good-as-can-be-expected employee productivity, based on knowledge of actual behavior
full effectiveness of all information technology systems, devices and software
real-time knowledge of any legal or regulatory compliance issues
Robust feedback loops and intelligence gathering that aids in the development process for new products and features
In practice, no organization operates that way, all the time. There are inefficiencies in all operations processes, capital allocation, employee alignment with organizational objectives, understanding of customer demand changes and supply chain processes.
Fragmented customer profiles, departmental silos, inefficient workflows, shadow IT, slow feature deployment and redundant processes are examples of friction. And friction matters because it gets in the way of customer and user experience, not to mention sales and profits.
Friction also exists whenever a firm or organization has to work with other stakeholders outside the firm boundaries, including customers, partners, suppliers or regulatory and political entities.
Frictionless systems aim, at a high level, to deliver insight, allowing an organization to accomplish its desired outcomes in both an effective and efficient way. And it is hard to imagine effective friction-reducing knowledge systems not using artificial intelligence, deep learning or machine learning.
So maybe we should speak less about AI as a technology and more about how AI enhances core business processes to remove friction.
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