Concepts such as explainable and responsible artificial intelligence (AI), continuous intelligence, and the knowledge economy have created an organisational environment where the temptation is to focus more on the technologies than on the people driving it.
But this does not have to be the case, as PBT Group has found.
“Much of this thinking is driven by the evolution of robotic process automation (RPA) and the efficiencies it unlocks for businesses especially in relation to performing real-time data analytics at scale,” says Andreas Bartsch, head of service delivery at PBT Group. “It has become almost too easy to rapidly adopt the technologies without considering the impact on the human resources within the organisation.
“On the other side of this spectrum are organisations who resist these innovations and only rely on the experience of their employees to help drive growth. Embracing computer intelligence and integrating it into the company does not have to be an all-or-nothing approach.”
At a fundamental level, standardising responses to different triggers can help drive the adoption of machine intelligence and consequent automation while still having human oversight. After all, AI does not learn in a vacuum. It relies on the skilled human resources within the organisation to ‘teach’ it. Yes, it can adapt, automate repetitive functions, and analyse data at magnitudes of scale faster than people. And yet without their guidance this evolution of intelligence and automation within the business is not possible.
Deriving value from knowledge
Joe Dreyer, BI consultant at PBT Group, says central to this theme is how companies have already become part of the knowledge economy where intellectual capital is driving consumption, production and innovation.
“Data is fuelling business decisions. But if that data does not make it possible for decision-makers to act on information, then it is ultimately useless. Companies must embrace the engineering and technological aspects of getting the systems, activities, and processes in place of intelligence. But without the human component making sense of the data and trusting the information, none of this really means anything,” says Dreyer.
This requires effective knowledge management, not in the sense of the technology concept of the past but rather the fact that if a company does not have the right information then it cannot make the right decisions.
“The personal experiences, insights, and knowledge of people enable the machine intelligence to automate efficiently and grow the knowledge economy from within the business. Consultants, analysts, and other employees must reskill and upskill as the technology evolves and systems become more advanced. Some of this requires adapting to new ways of working while other elements revolve on how to effectively unlock the potential of machine intelligence,” says Dreyer.
Intelligence done differently
Take continuous intelligence (CI) as an example, says Bartsch.
“This not only provides a more effective way of conducting data analysis, but it does so by reviewing current and past data to suggest actions in response to events. Doing this at a scale not possible through human operators can radically transform the potential of data inside the organisation. Now companies can improve the cycle time of data to derive continuous benefit from it,” believes Bartsch.
Another benefit of relying on technology is the removal of the inherent bias that people have when it comes to their own analysis of the data.
“CI deals with data in a way that people cannot process. By leveraging this technology to replace a significant portion of manual data analysis, companies can repurpose their human resources in other, more strategic ways. This requires a willingness to change from both the organisation and the affected people. However, embracing the capabilities of machine intelligence and combining it with the expertise of humans will be the way of the future,” concludes Bartsch.