Practice what you preach: Data centricity and democratization
Artificial intelligence (AI) and human intelligence have always been intrinsically linked. AI was born from studies of the inner workings of the human brain. Although he previously sought to explain how the brain works, he is now tasked with increasing and improving human capacities. Tasks that require repetitive and mundane input, or those that come from almost incomprehensible datasets, are exactly the kind of resolution AI has been introduced to. Adopting AI for such tasks prevents employees from wasting the energy and resources needed to mimic a computer and instead allows them to tackle issues that require a touch of humanity, such as computer-based skills. empathy such as creative design or critical thinking.
There is a false dichotomy that there is a battle between humans and machines. The reality of the relationship is much more symbiotic, with AI acting as a way to further facilitate human intelligence rather than replace it. The best analogy for this is to think of how we use a calculator. The device is user-driven to solve specific problems, but calculating those problems is not the end goal of the process – it takes a human to interpret the results of the calculation and put the information to work.
By increasing human intelligence in this way, tasks that were once considered too time consuming and labor intensive can be done away with. This translates into minimal human input, producing results at a level of complexity far beyond what an individual or team could hope to accomplish. It is this combination of human and artificial intelligence that is really great. The applications of this approach are almost endless, but to truly unlock their potential, organizations must remove the complexity of using AI and accessing data-driven information.
Let machines be machines and people be people
The goal of any business is to allocate and manage resources that deliver the desired outcome, whether from a product or a service for its customers. However, the global landscape for these products and services is much more complicated than ever. The rapid digital transformation has raised the bar for business and consumer expectations, creating an environment where speed, accuracy, reliability and 24/7 availability are no longer best-in-class features; they are expected as standard. Thus, highlighting the need for digital automation to keep pace with this demanding business environment.
It is important to contextualize the application of AI for different industries, as there are limitless combinations of industries and implementations. Electronics, for example, uses robotic process automation (RPA) in the manufacturing process of electrical devices. Computer vision (CV) is also used in the quality assurance process and in fraud detection, identifying emerging issues long before they become problematic.
In these examples, the demands dictated by the market exceed human capabilities. This does not mean that humans are left out of the process, it is up to the human to understand the context around the problem and design the larger system that meets the desired outcome. The user of these tools simply moves from “doer” to problem solver, using their analytical and reasoning skills to improve the quality of their results.
Keep it simple
The myth that machines are here to replace us firmly dispelled, why is there still an air of ambivalence or mistrust around AI? It’s the same reason why some companies are all engaged in digital transformation while others timidly pursue the competition with short-term fixes. This reason is the complexity of the problem.
To get the most out of AI, regardless of your industry or relevant implementation, the simple truth is that the technology has to be usable. Not just by those with greater technical maturity or advanced data science skills, but literally by anyone in your organization. The dashboard UI must be understandable. Machine learning software should be usable, data management systems should be accessible by a variety of people with different levels of technical ability and different preferences for viewing information. The great irony of this being that keeping it simple is a very complicated business.
As the technology around AI improves, more frameworks, models and platforms will become widely available. Enabling teams to identify, test, and adopt solutions based on their needs means there’s no need to start from scratch when you can leverage the work of open source. Equally important are advances in AI technology. AI management applications are increasingly advanced, with significantly improved user interfaces allowing a greater degree of intuitiveness and a lower barrier to entry. Interestingly, AI is increasingly intersecting with other cutting-edge technologies such as augmented reality (AR), thus broadening the appeal of AI applications for those in conventionally non-technical roles.
AI is for data-centered
Organizations must embrace data centricity and democratization, which puts data in the hands of their people. By enabling employees to use available data, companies create a progressive improvement feedback loop, improving the data analysis skills of their employees by improving data collection procedures. Understanding this, we know that the main priority of any successful AI implementation is to define the problem being addressed in such a way that the actions or processes that one seeks to improve are highlighted.
The success of this approach depends on the accessibility of AI to all business functions and not just IT. Democratizing this technology is essential to integrate it into enterprise-wide workflows. This is a critical step in getting the most out of the technology and getting the ROI from implementing AI in the first place.
That’s not to say that ROI should be the only goal we use to measure the success of AI integrations. More flexible benefits such as using data as a reflective asset for continuous improvement and streamlining enterprise-wide workflows can yield invaluable long-term benefits. The real value, however, is giving control to your employees. Give them the tools, the ability and the license to work collaboratively, not in competition, with AI. Only then will artificial and human intelligence coexist in harmony, leading to better commercial production and a smarter future.
Ian Jeffs, Managing Director, Infrastructure Solutions Group, Lenovo UK and Ireland