The perceptions and misunderstandings of Artificial Intelligence

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Among the many emerging technologies that companies are exploring to gain a competitive edge, artificial intelligence (AI) may be the most enticing because of science fiction, and the most misunderstood because of the excessive hype surrounding it.

While AI may be closer than ever to being used in the real world, there are still many steps to take before it is fully integrated into everyday business, according to CompTIA.

Moheb Moses, co-founder & director, Channel Dynamics, and director, ANZ Channel Community, CompTIA, said, “With disruptive possibilities for both workflow and workforce, AI must be well understood by not only the technology team, but also the business executives making decisions regarding the future of the organisation.”

CompTIA recently released its ‘Emerging business opportunities in AI’ report, which provides context for the state of AI adoption, describing general perceptions around the concept, current implementation status, and hurdles in the way of future success. The key findings include:

AI represents a new way of thinking about software

Most businesses aren’t actively developing their own AI algorithms. Instead, they are adopting products that have AI features built in. Even so, there must be an understanding of how AI differs from previous software applications. Rather than operating on inputs in a deterministic way to produce results, AI programs take in large amounts of data and work in a probabilistic manner. This means that there is a higher degree of uncertainty in the results. AI may produce unique and disruptive insights, but those insights require some amount of validation.

Innovative use cases require strong foundations

Using a broad definition for AI, many existing IT activities could be placed on an AI spectrum, and building on these activities could lead to better automation or stronger data analysis. However, most companies take a modern view of AI, imagining use cases such as personalised customer experience or security incident detection. Along with the introduction of new AI components, companies must also consider the infrastructure needed, the data that will drive the work, and the processes for integrating AI into workflow.

A mix of basic skills and new skills are needed

Considering the scope of potential impact, businesses should make sure that AI projects are handled similar to most technology projects in a digital organisation; as a collaboration between IT and business. When it comes to detailed implementation, companies are often looking for specific skills around troubleshooting or developing AI, however, there are also foundational skills in software development, security, and data management that contribute to AI success.

Strong data management is key to AI operations

The end goal for most AI solutions involves making sense of large amounts of data, so companies face the same challenge in building AI input that they faced in transitioning to a big data strategy. Data silos must be identified, and processes must be built for consistently handling the capture, processing, and visualisation of different data streams.

Seth Robinson, senior director, technology analysis, CompTIA, said, “AI can help with cost savings, but the greater potential lies in opening new doors. Companies that are approaching AI as an IT activity should consider its far-reaching implications and move towards a more collaborative model. AI is a topic that should involve the entire organisation.”