AI techniques are beginning to outperform human performance and are making fantastic progress. We are in an AI era wherein if any algorithm starts to perform at a human level, the adoption by others takes off. But, an expensive talent pool and computing make AI implementation a costly affair. However, if the implementation can be increased substantially, it will attain economies of scale and reduce the overall AI implementation cost.
Maximizing effectiveness through AI
The 20th century was the information era in which we saw the evolution of computer and information technology and the introduction of various technologies to build efficiency and efficacy in business processes. While the adoption of technology eliminated many jobs, it also compensated by creating new jobs and roles. For example, in the US, 70% of the population was engaged in farming and allied activities a century back, but today, it has come down to 3%. One of the main reasons for this shift is the high adoption of AI. Another example is in the BFSI sector, where ATMs replaced tellers, and the tellers found new job roles.
The last few years have seen significant use and adoption of AI across sectors. Robotic pharmacy and Qure.ai are some notable cases in hand in the healthcare sector. A robotic pharmacy can read prescriptions and accordingly give medicines without errors. Qure.ai enables multiple diagnostics with high accuracy and promptness. In the mobility and logistics space, Google Maps, built upon extraordinarily intelligent algorithms, is one of the most widely used applications that has made navigation easier and almost error-free like never before. All these are happening because organizations are trying to maximize effectiveness through AI.
Building Smart Business through Digital Transformation
With COVID – 19, conversations around digital transformation are sprinkled all over like a blind man seeing an elephant. The area of digital transformation is more widespread than just buzzwords like IoT, blockchain, etc.
Digital transformation is about building a smarter business by leveraging data at the enterprise level. For making #betterdecisions, one has to connect all the data sources and build a smarter business. A successful digital transformation lies in engaging with stakeholders and customers and improving operational effectiveness, like, increasing productivity, decreasing risk, losses, better forecasting, building precision, agility, reducing errors.
Digital transformation is also about using data to improve decision-making speed and accuracy and secure the future as there are several threats to every good business. It works on customer genomics to arrive at the next best action that a customer can take.
So how can organizations use data to improve the speed and accuracy of decision-making? The below three formulas can help build AI advantages for organizations
What drives results @ scale? R = AI * E2 * D2
AI itself is not enough to drive results. AI is about building smart algorithms, but smart algorithms are only one part of the job.
For making #betterdecisions using AI, organizations need huge engineering investments, including investment in cloud engineering, which is more critical than algorithms.
It is all about bringing together i) smart algorithms which essentially match humans, ii) engineering which seamlessly connects all data pipelines, iii) Design which helps in putting everything together in the cloud to make those decisions in real-time.
Reducing error 1/e = d * c* t
Reducing error is about bringing in more data, more computation, and more techniques. When Siri was adopted, error rates were too high, and now with Alexa, the error rates have been eliminated, and adoption has gone up.
At the organizational level, to reduce error rates, there has to be a culture where mistakes are celebrated. In the world of AI, one can learn only when there is an error.
Organizational effectiveness OE = T *C * G
To drive an organizational culture towards more effectiveness, it is most important to invest in a talent mix of AI, engineering, and machine learning which can help drive results.
It is important to build a culture of meritocracy and a positive error culture, a culture of trust and autonomy that defines transparency in the organization. Further, it is important to build upon a mechanism with strong governance, more so if you’re creating multiple operations and to make sure models are maintained and their lifecycle seen properly.
To learn more about building AI advantages for organizations, watch the below video: