Artificial Intelligence (AI) which promises a long list of business benefits is the epicenter of every business leader’s attention. From understanding customers better than they know themselves to optimizing supply chains and even offering personalized recommendations, AI offers endless possibilities.
But not every AI-adopting business succeeds. A primary reason is that there is no proven framework for successful AI adoption. But entities with analytics in their culture would have an upper hand. Here’s why:
AI is largely data-dependent. Data fuels the predictions of any AI and Machine Learning (ML) system, analytics being its immediate predecessor. After all, analytics is all about diving deep into data to deduce patterns and insights.
The data-centric and fact-based approach of analytics would enable a business to know why a particular incident happened, what could happen next and how to prepare for it, well in advance. It helps filter the noise out of your data and handpick signals that hint about what is oncoming.
Jog with Analytics before sprinting with AI!
A C-Suite executive would agree on the difficulty in digesting numerical metrics depicted in bar graphs and pie charts. Perhaps this difficulty in number-crunching is motivating many to leapfrog over traditional business intelligence and analytical systems to AI systems that boast of cognitive capabilities.
However, a recent study by Forrester has found that while 58% of enterprises are researching AI, only 12% of them actually have AI systems at work. Widespread adoption of AI is yet to become mainstream as there are too many stumbling blocks – the chief of them being the scarce availability of accurate, labeled and large volume datasets that the AI system can feed on to learn and develop cognitive insights. It is here that analytics positions itself as a worthy predecessor or a warm-up level for AI adoption.
How Analytics contributes to AI adoption
Analytics requires enterprises to have data maturity. And data maturity is attained by compiling data from diverse sources, picking the right data storage infrastructure and governing it properly to ensure accuracy and completeness. Dell was able to convince its stakeholders to accept its predictions by demonstrating its data maturity spread across four stages.
At each stage of data maturity, the organization becomes more adept at integrating a data analytics culture into their organization. Such high levels of data maturity enables senior management to invest in more sophisticated and autonomous analytic systems like AI and ML.
McKinsey’s Artificial Intelligence: The next digital frontier study found that “Representatives of firms that have successfully deployed an AI technology at scale tended to rate C-suite support nearly twice as high as those of companies that had not adopted any AI technology.”
So, to get the approval solid proof of success is necessary. Analytics provides the right use case for that.
As a company advances in data maturity, it will pass through several stages of analytics maturity which will prepare it for long-term AI adoption. Brent Dykes breaks down the analytics maturity of an enterprise into five stages:
- Descriptive Analytics which deals with static reports & dashboards
- Diagnostic Analytics which conducts inquiries
- Predictive Analytics which forecasts the immediate future events
- Prescriptive Analytics which suggests possible scenarios
- Cognitive Analytics which is AI in its crude form
Thus it is evident that no enterprise should attempt to leap over directly to an AI system without having a strong analytical base. So here are a few ways analytics facilitates quicker AI adoption:
▪ Sets up the right infrastructure
It helps an organization build infrastructure for data collection, storage, visualization, governance and analytics.
▪ Ensures data consistency
Analytics brings structured and unstructured data from diverse sources in a single repository where it can be classified, labeled and even made to specific order for quick analysis. This facilitates data ingestion for the AI system.
▪ Helps fine–tune processes
Implementing an analytical system leads to knowing the data weak points that need reinforcement within the organization. This leads to fine-tuning of data collection and analysis processes. Ultimately this would facilitate quicker deployment of AI systems with adherence to best practices.
The Way Forward
In retrospective, analytics has, in many ways, set the ball running for the rapid adoption of AI and ML systems. It is no surprise that businesses intend to use AI for specific data analytics centric utilities like forecasting demand and sales, targeted sales and marketing, and optimized manufacturing utilities, among many others.
Without the foundation of analytics, a business cannot afford to venture deep into AI. The upside of taking the analytics route is that it will put your business on the faster lane to digital transformation.