Practising what you preach: data centricity and democratisation

Although it previously sought to explain how the brain works, AI is now tasked with augmenting and enhancing human capabilities. Tasks that require repetitive, mundane input, or those that pull from almost incomprehensively vast data sets, are exactly the kind AI was introduced to solve. Embracing AI for such tasks stops employees wasting the energy and resources necessary to imitate a computer and instead lets them tackle problems that need a touch of humanity, such as empathy-based skills like creative design or critical thinking.

There is a false dichotomy that there is a human versus machine battle at play. The reality of the relationship is much more symbiotic, AI acts as a means to further facilitate human intelligence rather than replace it. The best analogy for this is to think of the way that we use a calculator. The device is directed by the user to solve specific problems, but the calculation of these 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 use.

By augmenting human intelligence in this manner, tasks that were once considered too time consuming and labour intensive can be stripped back. This results in minimal human input, producing results at a level of intricacy well beyond what an individual or team could hope to accomplish. It is this combination of human and artificial intelligence which is truly formidable. The applications of this approach are almost endless, but to truly unlock their potential, organisations must remove the complexity of utilising AI and accessing data driven insights.

Let machines be machines and people be people

The goal of any business is to assign and manage resources that provide a desired outcome, whether from a product or through a service for its customers. However, the global landscape for these products and services is far more complicated than ever before. Rapid digital transformation has raised the bar for both business and consumer expectation, creating an environment where speed, accuracy, reliability and 24/7 availability are no longer best-in-class features; they’re expected as standard. Thus, highlighting the need for digital automation to keep up in this demanding business environment.

It’s important to contextualise the application of AI for different industries, as there are boundless combinations of industries and implementations. Electronics, for example, use Robotic Process Automation (RPA) in the manufacturing process of electrical devices. T Computer Vision (CV) is also used in the QA process and in fraud detection, identifying nascent issues long before they become problematic.

In these examples, the requirements as dictated by the market are beyond human capabilities. This doesn’t mean that humans are excluded from the process, it’s for the human to understand the context around the problem and design the wider system that meets the intended outcome. The user of these tools simply shifts from the ‘doer’ to the problem-solver, using their analytical and reasoning skills to improve the quality of their output.

Keep things simple

With the myth that machines are here to replace us firmly dispelled, why is there still an air of ambivalence or mistrust around AI? It is the same reason that some companies are all in on digital transformation while some tepidly chase the competition applying short term fixes. That reason is the complexity of the problem.

To get the most out of AI, regardless of your industry or the relevant implementation, the simple truth is that the technology must be usable. Not just by those with greater technical maturity or advanced data science skills, but by literally anyone in your organisation. The UI of the dashboards must be understandable. The machine learning software must be usable, the data management systems must be accessible by a variety of people with different levels of technical ability and different preferences of visualising the information. The great irony of this being that keeping things simple is a very complicated matter.

As the technology around AI improves, more frameworks, models and platforms will be widely available. Allowing 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. The advancements in AI technology are also just as important. AI management applications are becoming more advanced, with much improved UIs allowing for a greater degree of intuitiveness and a lower barrier for entry. Excitingly, AI increasingly intersects with other advanced technology such as Augmented Reality (AR) broadening the appeal of AI applications to those in conventionally non-technical roles.

AI is for the Data-Centred

Organisations should embrace data centricity and democratisation, which puts data into the hands of their workforce. By letting employees use the data at hand, companies create a feedback loop of incremental improvement, enhancing the data analytics skills of their employees by improving the data collection procedures. Understanding this, we know the main priority of any successful AI implementation is to frame the problem being addressed in such a way that the actions or processes which are sought to be improved come into focus.

The success of this approach depends on whether AI is accessible to all business functions not just IT. Democratising this technology is central to integrating it into companywide workflows. This is an essential step to getting the most out of the technology and getting the return on investment of implementing AI in the first place.

This isn’t to say that ROI should be the sole lens that we use to measure the success of AI integrations. The softer benefits such as using data as a reflexive asset for ongoing improvement and the streamlining of companywide workflows can produce invaluable benefits in the longer term. The real value, though, is putting your people in control. Give them the tools, the ability, and the license to work in collaboration, not competition, with AI. Only then can artificial and human intelligence coexist in harmony, leading to enhanced business output and a smarter future.