In the erstwhile Industrial era of the 70-80s, organization size was a very important factor for growth. How large the company is made a huge difference to their fortunes and growth prospects.
In the now Digital age, size has seized to remain that critical. Instead, agility has emerged as a key determinant of success. How fast an enterprise can learn and respond to market changes in a highly digital economy, pretty much decides the momentum of gains it stands to make. Agility is not just about speed, but also about dexterity. It is described as nimble proficiency for quick learning, pivoting and scaling sharply, once it is proven successful.
The moments of truth in consumer journeys could present fleeting opportunities, which when mined in real time, could offer a great competitive advantage. Alternately, the advantage could be lost forever, if missed at the opportune moment.
It is very promising that brands are adopting automation tools and agile methodologies at a rapid pace. To an estimate by a leading SAS provider, two-thirds of customer engagement will be via digital devices and smart machines, by 2030, replacing human intervention.
Artificial Intelligence & Machine learning
AI is said to effectively replace humans in jobs, which machines can do faster, more accurately and at a fraction of the cost. The applications of AI for industry present tremendous opportunities for improving customer resolutions, response time and better experience, in real-time. It is being adopted at breakneck speed across tech-driven businesses that are disrupting erstwhile operating models.
When dealing with an enormous quantum of structured and unstructured data, AI enables a machine to simulate human behaviour by rapidly expanding the rate of computation of complex problems. With AI, intelligent systems can perform any complex task like a human, but at far greater speed and in a fraction of the time.
The most common application of AI that everyone is familiar with are chatbots, which are programs in language learning, reasoning, self-correction and response that interact with customers much as humans would.
But that is not even the tip of the iceberg. Wherever there is a large volume of data, AI has a use case in speeding up the extraction of learning through ultra-fast computations. AI is being deployed for processing data at key customer touch-points to analyze, personalize and offer triggers, customized offers, and propositions that are humanly not possible, enabling better customer engagement, conversions and revenue optimization through relevant offers.
A large quantum of Customer feedback is being collated by brands, from all brand touch points and the World Wide Web. Where conducting manual analyses and derivation of insights & actions would take days, that has now been replaced by the use of AI for sentiment analysis. The system can intelligently extract key learning and propose top next best action steps that the team must undertake, and resolve consumer issues, that too in real-time
The goal of Machine Learning is to allow machines to automatically learn & progressively get smarter from past data without explicit programming each time, to provide more updated and accurate output, on the fly. Machines are “trained” to learn a specific task & to detect patterns and trends to automatically update the results. Even minor shifts in consumer behaviour get monitored and responded to, without human intervention
Google search algorithms have become smarter over time, and even NLP bots that develop deeper language & response skills over time, as they compute & analyze more data, are examples of ML applications in use extensively.
Brands deploy AI/ML-based Retention and revenue models that once built, become smarter over time as they continue to “learn” from more data and thus evolve the quality of output from the models – like enhancing cross-sell revenue, lowering churn based on customer segmentation and profiling. Such programs give increased conversion statistics over time, as they can capture minute nuances and use them to predict likely consumer behaviour. The dexterity to build them and refresh them on-going provides the brands with a head-start
AI applied to repetitive and otherwise time-consuming processes, with the explicit purpose of achieving the same goal much faster and cheaper, creates a robotic process automation (RPA) chain. Since RPA technology is deployed for specific business and IT use cases at scale, they help enormously reduce the time for processing, improve efficiency and reduce costs dramatically.
The most common applications are for back-office operations like workflow, email desk automation, financial accounting, and bank reconciliations. These can help in vastly improving CX through the faster turnaround of resolutions, which are otherwise slow due to dependence on these time-consuming manual processes and human dependence, which may be error-prone.
An enterprise can work towards an entirely paperless back office by deploying RPA to its largest repetitive tasks such as data collection, reporting, on-boarding, managing orders, payroll processing and more.
A combination of Robotic Process Automation (RPA) and artificial intelligence (AI) technologies together empower rapid end-to-end business process automation and accelerate digital transformation, helping brands to achieve their goal of enhancing CX – much better and faster.