Is There a Reason Why A.I. Hasn’t Gone Viral Yet?

It is often claimed in technology-focused blogs and portals that artificial intelligence (AI) is utterly transforming all sectors of the economy. Many accounts discuss how AI is revolutionising a vast range of industries, from manufacturing to retail to farming to space exploration. It appears that AI is having a significant impact on virtually all areas of our lives.

The chorus of praise for this achievement has been loud and wide-reaching, with many declaring it the greatest technical achievement of our era. It is hard to disagree – the power of this technology when applied to the real world is both exciting and awe-inspiring, a reminder that we are perched on the brink of a future that could dramatically reshape the way we live.

If we take a step back and assess these articles objectively, we can see that they utilise numerous adjectives. Are businesses truly investing heavily in Artificial Intelligence (AI), or are we overestimating the situation? How much of a genuine AI revolution are we currently witnessing, or is this more of an aspiration than a reality?

A number of papers and studies have been conducted to answer this question. IBM’s survey has reported that only 34% of organisations in the United States, the European Union, and China have adopted Artificial Intelligence (AI) initiatives. Gartner’s research has suggested that most businesses have four active AI initiatives. Furthermore, NewVantage Partners’ poll has indicated that just 37.8 percent of Chief Executive Officers (CEOs) and other high-level executives have successfully implemented a data-driven business culture.

It is clear from these figures that artificial intelligence is not yet having a revolutionary impact on the business world. Rather, it is being cautiously tested in isolated instances. This raises the question of why this is so, despite the high levels of public interest and investment in the field. Why has artificial intelligence not yet taken over the world, despite its potential and the resources being poured into it? It appears that this is a discussion that could continue indefinitely.

The Challenge of Integration

It is easy to presume that financial constraints are the primary reason why tech giants such as Google, Facebook, IBM, and others are pushing the boundaries of Artificial Intelligence (AI) development. However, this would be an oversimplification of the situation. The transformation to an AI-based culture is complex and the majority of companies are not yet equipped to take on this challenge. While financial resources may be a contributing factor in the development of AI, it is not the only factor.

When it comes to implementing Artificial Intelligence (AI) technology, there are a number of factors to take into consideration. One of these is the unmet expectations of AI by company owners and decision-makers; they may expect the process to be as straightforward as purchasing a smartphone and being able to use it right away. Unfortunately, the reality of harnessing AI is far more complex than this.

The integration of AI-based solutions into an organisation’s existing digital infrastructure is often a complex process. It is important to consider all the digital programmes a business is currently using to ensure a successful AI solution. This could include applications such as accounting software and customer relationship management systems (CRMs), as well as any programmes deployed in marketing, sales, or human resources. To ensure a smooth implementation, the AI solution must be able to integrate seamlessly with existing programmes, so that they all work together in harmony.

If you have ever been involved in the process of introducing new software into an existing system, you will be familiar with the difficulties that can arise. It can be a complex task to link new software to existing systems, or to amalgamate closed-source applications with open-source programs, or to integrate offline solutions with mobile ones.

It is a mistake to limit the use of Artificial Intelligence (AI) initiatives to areas such as chatbots, marketing automation and supply chain management algorithms. This restricts businesses from unlocking the true power of AI across all areas of their organisation. Automating simple tasks like social media updates and vendor payments is certainly a benefit of AI, but much more must be done to realise a true AI-driven transformation.

Most contemporary companies that invest in Artificial Intelligence (AI) do so in a cautious manner, recognising that deviating from the accepted path could be a major undertaking that necessitates considerable resources. As a result, the majority of individuals only perceive the potential of AI in the abstract, and the transformation is postponed.

The Problem of Expertise

It is an optimistic vision to believe that a corporation could embrace a completely integrated Artificial Intelligence (AI) system, where data from a range of sources throughout the organisation is used as input. In theory, this could allow AI to reach its full potential. However, in practice, it is not as straightforward. The successful implementation of AI requires a critical stage of training the AI model correctly, which can present its own challenges.

Despite the availability of Artificial Intelligence (AI) as a Service (AIaaS), it is not a product that can be simply bought and used instantly. In addition to the difficulty of incorporating the algorithms into your existing digital system, there is also the challenge of training the algorithms to understand your data and processes in a way that is specific to your business. To put it another way, the AI solution must have a thorough understanding of the workings of your business and to do this, you will need to provide the type of data that would be most beneficial.

It is clear that a substantial amount of effort is required to create an efficient Artificial Intelligence (AI) approach within any organisation. This includes the vital task of collecting, cleansing and structuring data so that it can be utilised by AI algorithms. As such, the involvement of data professionals is essential, who can tailor both the data and the AI solution to meet the particular needs of the business. Unfortunately, there is a shortage of such talent, meaning that the requirement for data scientists is increasing and, as a result, the deployment of AI is being hindered at many businesses.

The challenge of sourcing the necessary expertise to successfully implement an Artificial Intelligence (AI) strategy is still very much a problem. One option to overcome this is to either recruit data scientists in-house or to outsource the work to a specialist organisation, such as Works. However, for the AI system to be tailored to the company’s specific requirements, employees must be involved in the process to ensure the algorithms are adapted and modified to best serve the business. Data scientists can provide invaluable assistance with data collection and model training, but it is employees who can ensure the AI system is developed with the company’s needs in mind.

The process of implementing Artificial Intelligence (AI) should involve the staff who will be using the AI software. Before AI systems can be trusted to provide useful insights, it is essential that they are validated by experts in the field. This means that the most experienced and capable personnel in the company need to work closely with engineers to develop an algorithm that will be of real benefit to the organisation.

Many organisations are put in an uncomfortable situation due to this. If companies want to successfully implement AI, it is crucial to take their best employees away from their usual tasks in order to train the AI solution. This could cause a significant disruption to the company’s usual operations. Nevertheless, if those workers aren’t involved, the AI integration may be a wasted effort, as the insights it offers may not be useful or applicable.

Set the Sparks Flying

The lack of specialised personnel, in-depth industry knowledge, available time, and financial resources are all contributing to the slow uptake of Artificial Intelligence (AI) among smaller and medium-sized businesses. It is clear that most businesses do not have the necessary resources to implement a true AI strategy, which is why the adoption of AI is not taking place at a faster rate.

It is not necessarily the case that we are destined to remain at the current level of Artificial Intelligence (AI). There is a great deal of potential in the AI of today, even if it is not yet as sophisticated as we would like it to be. The investments we make in AI solutions now may form the basis of the more robust AI infrastructure of the future. Therefore, it is prudent to begin taking account of AI now, and start introducing it into businesses at a steady pace, regardless of the scale of the project – as most business owners recognise that ignoring AI is not an option.

It is an unavoidable fact that organisations will face challenges when attempting to integrate artificial intelligence into their operations. This is especially true for organisations which are not naturally data-driven or have a mostly digital infrastructure, as they will require additional effort to comprehend how AI can be deployed in their particular field. However, there is a greater risk in not taking any action at all. Fortunately, it is still possible to bridge the gap.

The Artificial Intelligence revolution has not yet taken place, and it appears that it will not be happening in the near future. However, it is prudent to be prepared for the eventuality of AI becoming a reality and living up to the widespread expectation that it will eventually take over the world. The time for action is now.

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