How To Optimize Your AI And Data Approach?

In a recent interview Craig Le Clair, VP at Forrester, stated, "Automation is increasingly critical to every organization's ability to win, serve, and retain its customers."

That particular quote got stuck in my head for a few days while researching the feasibility of digital employees and cognitive AI services for our customers and implementation within our own company.

Boiling it down and reading between the lines, it all comes down to fundamental convergence of insights, information, and decision making.

In today's complex and disrupted business environments, any effective decision-making must be connected, be used in context, and continuously focus on outcomes.

In essence, it means rethinking the role of data and analytics in effective decision making and exposing it in ways people change to use it.

The current digital acceleration rate is often higher than enterprises, governments, and individuals can maintain. Many of them can no longer discern the music from the ever-increasing noise and fall back in a more conservative stance, resulting in faulty decisions.

One cannot ignore the evidence viewable in the market that a significant portion of companies is still taking a defensive stance, keep a near-term view, and miss opportunities in the market to maximize breakthrough. 

If one adds that the predictions companies such as Gartner made the past years, the pandemic has created a momentum in digitalization that is borderline magical. The world has before our eyes changed from Business Transformation with support from technology to a technology-driven business transformation.

Betting on growth businesses, in particular, has proven to be a powerful engine. I firmly believe that innovation and design thinking constitute a powerful launchpad in existing services and new services. It requires a radically different culture to stay digitally relevant and understand the convergence of technologies we see today. 

Cognitive AI

Cognitive AI, including conversational AI systems, and Machine learning, have emerged as a vital enabler of the essential innovation drive. Whatever governments, enterprises, and individuals do in the technology space do these days, at its core, it is all about data, information, and insight. 

Data without true meaning is worthless, and therefore applying a more semantic model makes it information, insight, and knowledge. It is this insight that leads to better decisions. 

Much has been written about the potential of AI and data mining to redefine business processes through the automation of physical, transactional, and higher cognitive tasks. 

The whole AI stack is, understandably, generating quite some nervousness for many employees. It remains crucial, however, that many of these technologies are there to augment employees by, for example, adding digital employees in the workforce or support making better decisions.  

Data, Insight, and Knowledge 

True value comes from using the proper building block convergence around data and insight to support business decisions accurately. 

In a recent survey, Gartner stated that 65% of decisions made are more complex and require more stakeholders or choices than two years ago. The current state of decision-making is therefore unsustainable. To reengineer decisions in a way that deals with higher complexity and uncertainty, good decision-making is to be more connected, contextual, and continuous.

Making it real and scale requires a true Data Culture and a clear focus on the decision aspect.

In my opinion, there are three key technologies and methods that can give this approach a real boost for many enterprises. They are hyperautomation, data fabrics, and digital twins. By combining and converging these three, as an example, advanced cognitive assistants can enable predictive decision-making using real-time analytics.

Hyperautomation is the concept involving the use of an ecosystem of advanced automation technologies to augment human intelligence. The aim is to further automated business processes so that more agile organizations and business units can capitalize on insights for more efficient decision-making. It combines AI, ML, conversational platforms, event-driven software, process discovery, and automation tools to bring a more holistic approach.

A data fabric utilizes continuous analytics over existing and exposed metadata assets to support the design, further deployment, and utilization of integrated and reusable data across many environments, including hybrid and multi-cloud platforms. A data fabric leverages human and machine capabilities and can enormously enhance any data engineering and data mining approach in many dispersed data sets in an ecosystem.

A digital twin is a structured virtual representation of an object or system that spans covers the product or service lifecycle. It is updated from real-time data and uses simulation, machine learning, and reasoning to help decision-making. It is, therefore, very adept at running what-if scenarios that are essential when testing the underpinning assumptions of decisions. Intelligent twins can maintain powerful simulation capabilities, and with a solid data foundation or fabric in place, they enable a robust innovation process.

Conclusion 

It is straightforward to see that there is still a long road ahead as the technology landscape evolution keeps accelerating. Adapting to this change in pace requires business and governmental leaders to reevaluate their priorities and strategies at all levels in their organizations. 

In the end, it contributes to the overall business resilience. This resilience is the ability of a business and any other organization to resist, adapt, and reimagine the approach in the face of internal and external shocks.