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What business decision-makers face when buying AI

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Artificial intelligence (AI) is widely viewed as a technological innovation that may leave no economic sector untouched and is considered to be revolutionizing most business operations. The excitement ae understandable since the technology can provide a competitive edge, increase efficiency, generate cost reductions, and enable the creation of new products and services. However, AI is a complex technology, and many companies remain anxious about some of the challenges of its adoption, such as data bias and discrimination, data integrity, and job cuts, just to mention a few. Moreover, deploying AI can impact many business functions and disrupt existing operational and IT processes. Companies’ natural response is often to stay cautious about purchasing AI solutions. Therefore, the chance perceptions in your head of those making these buying decisions matter a great deal. We would like to share some observations of such risks from our work in the financial service industry.

Who is responsible if the technology doesn’t work?

Companies, especially the large ones, generally have their in-house innovation teams faced with assessing new technologies and making recommendations about their viability and fit with the business. There are numerous aspects that these teams will consider when assessing an AI solution. Perhaps despite the traditional wisdom, financial services companies do certainly not have to be concerned with various oft-mentioned risks, such as those linked to bias and discrimination. Why? They use AI solutions to automate non-customer-facing activities such as document processing. Instead, they’re more prone to concentrate on technological issues. Consequently, having an advocate for the technology who will require full responsibility for the purchase can be difficult.

So much of the successful adoption of an AI solution goes beyond the pure technical merits of the perfect solution. It requires integration within existing IT and operational processes: will the AI model work once live data is fed into the device? Will the IT infrastructure be able to handle the AI solution? Will the in-house engineering and data science teams be able to manage and maintain the technology in the long run? There is always the danger that AI technology doesn’t perform as intended or expected. The uncertainties around the technology’s success within the company are excessive. They might be unwilling to bear the total weight of sponsoring the technology and risk their job should its deployment fail for reasons outside their control.

Are you sure the technology would act as intended?

Business leaders are the ones who ultimately make capital expenditure decisions. In many organizations, particularly those under public scrutiny or in the people sector, having the technology right initially is essential. Failure to have the technology to supply what was promised can be very damaging to the business enterprise leaders, who are visible as incompetent, or worse, as having misused public funds, potentially ultimately causing reputational or even legal damages.

With this type of technically complex solution as AI, the leadership’s decision depends heavily on the info provided to them by their technical teams, particularly data scientists. They should be knowledgeable of the newest solution and in a position to communicate its benefits and challenges clearly to non-technically versed business leaders. Vendors have a vital role to play here to ensure that the info scientists fully understand the technology and have detailed details about its business case.

In terms of purchasing AI is worried, issues such as talent shortage to implement and support the technology, budget considerations, integration with the existing IT infrastructure, and a practical business case are apt to be top of mind. However, there remain many uncertainties in the long run for the info and leadership teams, such as perhaps the technology vendor will still exist in five years for you to provide the required tech support team, how scalable the proposed solution is given the state and nature of the existing IT system, who is going to maintain the onboard technology going forward and what this maintenance involves. Looking from the vantage point, unless the decision-makers can somehow be 110% confident and comfortable with the technology about to be introduced, there always exists the temptation of dropping the AI project altogether. In short, no gain, no pain.

Past research and studies have warned us against the risks of AI technologies. Yet, in the business context, it’s often not the technology itself but rather the uncertainties surrounding adopting the technology that matters. The perceived risks developed by these uncertainties can be very real – realistic enough to discourage the uptake of AI for a company. Finding ways to mitigate these risks should be considered a priority of any business wishing to use AI to generate value.

 

 

Brian Santiago

Businesses are urged to get to know their supply chain when hiring.

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