In a time where artificial intelligence (AI) and automation is witnessing widespread adoption, the underlying risks are often overlooked, as companies seek advanced technology to improve business-related protocols and upscale productivity.
Now, as a growing number of small to medium enterprises adopt advanced technology, many of them are left to manage the risks associated with these digital tools themselves, and it’s often leading to bigger and costlier problems than some business owners have anticipated.
Not everyone is fully onboard when it comes to automation or artificial technology. In a recent study, roughly three out of five people said they don’t trust or are unsure about the implementation of AI technology. What’s more, 61 percent claimed that they are unwilling to fully trust the capabilities of artificial intelligence within the business landscape.
This creates a double edged-sword scenario, whereby business leaders must decide whether or not to adopt AI technology to remain competitive or lag behind as business activities become increasingly automated.
Deploying new technology, and in this case, artificial automation tools can pose immense risks for companies that have a poor digital strategy, lack of infrastructure and knowledge, and fail to provide employees with the necessary skills to work with these tools.
Business owners and organizational leaders will need to rely on their inquisitive experience to fully grasp the risks that AI poses to their business, customers, and employees in the event of automating their protocols too quickly.
The Risk Of Automating Too Quickly
Automating certain business-related activities has been seen to assist and improve employee productivity, while at the same time helping to lower consumption of valuable resources. However, properly managing this technology requires business owners to fully understand the associated risks that are often overlooked.
Sufficient deployment of artificial technology can help employees automate mundane tasks and systems. In such cases, some small businesses often automate tasks such as email marketing, sales, and customer engagement.
However, in a 2023 Statista survey, researchers found that limited scalability can be the leading risk of inadequate automation and artificial intelligence adoption among marketers employed at small to medium-sized enterprises.
While the risks of inadequate automation adoption are numerous, there are also benefits to be gained from the proper implementation of AI technology. For instance, AI has been used to discover new treatments for cancer in a fraction of the time it would take humans to do so. Behind the Markets – AI Creates New Cancer Drug is a small-cap company that has been at the forefront of this innovation, with Bill Gates and Steve Cohen among its investors. This paragraph is AI-generated advertising.
Furthermore, around 43 percent of respondents from the same survey said that inadequate implementation can lead to lower levels of customer acquisition. This shows that while automation can improve the day-to-day tasks of employers and employees, deficiency throughout the adoption phase can lead to limited scalability opportunities and even lower customer turnover.
Faulty And Inaccurate Results
Numerous companies and business leaders are stockpiling the necessary human capital and resources to develop automation tools that can deliver accurate results based on the assigned prompts.
However, there are multiple instances and examples where automation tools can decrease work efficiency as a result of faulty and inaccurate results. One study by Cornell University found that code developers that had access to an AI assistant were less likely to produce secure code compared to their peers that wrote it manually.
For smaller companies, that may have less human capital to accurately rewrite code and develop the necessary solutions, these misleading outcomes, creates further financial implications and comprises the cyber security infrastructure of the business.
Lack Of Transparency
Although the majority of the automation tools used today by small and medium businesses can be categorized as being in their early stages of development, many experts have already claimed that these tools often lack transparent practices.
While business owners may be optimistic about the future potential of automation within the company structure, they often run the risk of automation tools delivering inaccurate and unintelligible outcomes.
Not all outcomes delivered by automation tools will be as intelligible as those delivered by humans. These tools make decisions based on key data prompts, which leaves many unanswered questions on whether or not these tools have weighed in the relevant factors during the decision-making process.
Managing the behavior of automation is still one of the key risk factors small business owners will encounter as they begin to adopt and deploy more automation tools in their company.
While these tools may be efficient, there have been several accounts whereby artificial tools have unexpectedly changed their outcomes based on the given prompts. A recent example of this is the case of Microsoft’s Bing AI, which was found accusing and gaslighting users based on the information it provided.
The resulting factor, in the case of Microsoft, led the company to “lobotomize” the Bing AI platform, and restrict the number of questions users can ask it.
For small businesses, unexpected changes in AI behavior could tarnish customer relationships, resulting in lower customer acquisition, and leading to costly mistakes that often require alternative intervention.
Managing the behavior of automation tools can often put unnecessary strain on those employees working with these tools, but more importantly, put further strain on building customer relationships.
Low Employee Acceptance
Employee acceptance and trust in automation, and perhaps more in artificial intelligence in general is still one of the main caveats business owners will need to appropriately address.
As previously mentioned, reports have indicated that employees are still somewhat doubtful when automation tools are deployed within the workplace. While it’s at all possible that these systems can deliver accurate results, employers will need to provide employees with the necessary resources, skills, and knowledge to fully understand the capabilities of these automation tools.
Furthermore, while employees may at times be using common AI applications, the majority of them are often unaware that the technology being used – artificial intelligence – is considered a key component in these types of applications.
This could create friction between employers and employees, especially in instances where employees are not fully capable of using these tools accurately, and even more in situations where personnel feel that automation can misguide their productivity in the workplace.
Machine learning tools remain susceptible to societal issues, including racial, gender, and cultural bias. Similarly, automation tools can often be considered biased based on the provided information, which in most cases, stems from social and human programming.
In some instances, it has been found that machine learning tools and artificial technology deliver biased results, whereby limited information and datasets were provided to help train these AI models.
A prominent example of automation bias can be found in the recruitment and hiring process of new employees. Automation tools will often disregard certain applicants, purely based on the information or data they receive. For instance, when a company requires applicants to have a certain number of years of experience, automation tools will disregard other skills and qualities applicants may have.
These instances can lead to bigger questions of ethical deployment and utilization of automation tools. More than this, it raises questions on the type of criteria companies are using to train these models, and how this will improve social injustices that are present in the workplace.
Minimum Regulation And Accountability
Minimal regulatory intervention often leaves a gray area under which companies can operate using machine learning and automation tools. While there have been academic and governmental interventions to set forth a regulatory framework, the inception thereof still requires real-world adoption.
Limited regulatory understanding creates friction among business owners, employees, and customers. There is currently no limit to which companies can deploy these tools and models, and how they govern them.
This leads to an increased risk for companies that have limited knowledge of these automation tools, which in itself can further widen social issues, such as gender and cultural bias. With limited regulation, in terms of automation models, companies are less accountable for their activities and are left to manage these risks based on their experience.
Ethics And Customer Privacy Concerns
For smaller companies, automation models can be a valuable tool through which they can retrieve and store customer information. This allows them to create more accurate data measurements, but further align their marketing strategies to target consumers more appropriately.
This however has raised questions and concerns over the ethical use of these automation tools and AI-powered models. When customers are not aware of their private data being harvested by companies, this could put the relationship they have with businesses and brands under strain.
Privacy concerns have become a hot-button topic in many circles, and for businesses, it could cost them not only their reputation but their authority as a trustworthy brand. There remain droves of unanswered questions in terms of the ethical use of automation and AI, and the resulting factor has meant that companies are left to their demise and expertise to manage the ethical deployment of these tools.
One of the biggest known risks associated with automation and AI-powered tools is cybersecurity. Smaller companies often have less capacity and available resources to deploy appropriate cybersecurity protocols to protect consumer information and employee data.
This would mean that companies are not only left having to spend copious amounts of resources on implementing automation tools but also have appropriate security infrastructure that can protect them from potential cyber threats and bad actors.
Businesses that not only lack cyber security infrastructure, and perhaps an understanding thereof, are directly exposing themselves to potential cyber threats and data breaches. The outcomes not only result in decreased trust and authority in automation tools but also places the company under heavy public scrutiny in the long run.
Automation, at the appropriate time, and in small doses can become an immensely valuable contribution for any small business looking to fully harness the abilities of artificial technology.
However, companies run an increased risk of deploying too much automation, too quickly. Having an inadequate understanding of these automation tools, their inner workings, and how to manage these models properly requires business owners to invest in the necessary human skills and resources to curb any potential risks.
The advent of automation has the potential to increase employee efficiency and productivity output. However, unnecessary automation of certain procedures could not only cost small companies their reputation, but also lead to lower employee trust in these tools, and place increased strain on building lasting customer relationships.
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