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Alarming AI Risks and Roadblocks for Businesses to Consider

By Anushree Bhattacharya - April 04, 2023 6 Mins Read

Alarming AI Risks and Roadblocks for Businesses to Consider

As Artificial Intelligence (AI) is shaping digital businesses, leaders must have an organized approach to prioritizing and identifying risks for AI. Doing so can further help organizations to target mitigation efforts effectively.

As AI deployment is becoming central to organizations’ future operational success, the technology leads organizations toward the significant risks of that AI carries. The AI risks range from bias and discriminatory risks to operational and IT risks, from business disruptions to security breaches and many other hidden threats that evolve as AI advances. Businesses should understand that this technology is still nascent, so many risks are unidentified. Such risks may put firms into trouble.

However, business leaders must be aware of the significant risks that AI businesses may face when implementing it into systems and operations. Here are some prime areas of AI risks that arise with implementing the technology.

AI Bias

AI systems are an asset to businesses if data are aligned according to the algorithms. If data contains biases, it could make the AI system biased. Such misalignment may lead to adverse outcomes, which can have legal and reputational consequences for businesses.

Another source of evolving risks for AI is the algorithms used to train the models. Algorithms can go wrong if algorithm gaps exist or due to wrong commands. This may lead to harmful business processes associated with AI systems at large.

Businesses can prevent AI-led risks by encouraging leaders to ensure that their data is diverse and that the algorithms are designed to minimize bias. For this, organizations must be accountable for the data quality they use, model, and governance methods. Regular assessment of the performance of AI systems allows IT teams to identify and address AI biases that may occur and mitigate them with proper solutions.

Also Read: Revolutionizing AI Adoption: How Managed AI is Empowering Businesses

Lack of Transparency

AI systems have become more sophisticated with advanced features. As businesses leverage the advancements in AI, they may also face complexities. Companies can find AI complexities that may make it challenging for teams to understand and interpret gaps in functionalities. This lack of transparency can lead to several AI risks and limitations for businesses to proceed with AI-based operations.

One of the main risks of AI for businesses is making the right decision when required. Many AI systems, particularly those using deep learning techniques, are struggling to understand the outcome, and this may mislead business objectives. Such a situation accentuates wrong decision-making and may lead to managerial incompetence.

For instance, businesses use AI systems to cut costs and meet compliance regulations. So, if the technology does not function efficiently, the organization may face costly financial consequences in terms of compensation, remedial actions, and penalties.

Lack of Training and Monitoring Data Can Sabotage AI Systems

AI highlights another significant risk when leveraging it has to be taught right to work it directly. Many organizations leverage AI for various business operations. However, they lack training and data monitoring, creating inefficiencies in the system. Businesses today are using AI in large quantities for building chatbots for multiple purposes.

Chatbots are well executed with data and algorithms to learn business processes. So if the data and algorithms are poorly maintained and monitored, AI systems may understand adverse behavior. Tracking data for AI-based chatbots is necessary to prevent adverse business effects.

In addition, if organizations lack data training and monitoring, AI systems may fail to forecast accurate customer demand and other business requirements leading to incomplete information, thus affecting their decision-making standards.

Non-Availability of Data

One of the primary barriers that come with AI systems is the availability of data. Often AI systems may fail to provide the data required. And businesses may lack the ability to fulfill a particular function.

This may hinder business processes and may delay delivering commitments due to a lack of data. In such cases, businesses should have a clear strategy for sourcing the data with AI by keeping a backup of data collected from AI systems.

Security Risks with AI

In complex advanced AI models, evolving vulnerabilities may create significant risks. Vulnerabilities such as system hacking and data breaching can pose new security challenges along with promising AI security approaches. Existing AI frameworks often include minimum security standards, which businesses may fail to understand.

In addition, AI systems may also be at risk points when login credentials are shared across organizations, leading to system discrepancy and may result in system malfunctions. Businesses should restrict access of AI systems to authorized teams, and employ multifactor authentication (MFA) systems to limit access to only relevant employees.

Essential Considerations for Mitigating the Risks of AI

AI delivers numerous business benefits. However, complying with regulations to use AI is necessary for businesses to get the most out of this promising technology and mitigate significant risks.

Create Organizational Standards for Applying AI

The fundamental way to mitigate the risks of AI is to create organizational standards for applying it. Deciding how to use AI ethically must be on priority for organizations before deploying AI systems. Organizations can develop processes for monitoring AI algorithms by compiling high-quality algorithms and establishing standards to determine the modification of algorithms.

Adequate Governance

When applying AI applications, business leaders must put adequate governance policies to adhere to, when AI systems are in use. Teams must govern AI models, considering them as an essential corporate asset.

They need proper assessment with adequate tools and mechanisms to help teams audit, report efficiently, and look for algorithm gaps or roadblocks. Many companies attempt to extend their model risk processes, which is a great start, but consistent processes and automation are critical to managing AI models to mitigate risks for AI.

Also Read: Explainable Artificial Intelligence (XAI) and Its Impact

Orchestrate Automated Solutions

AI system risks need more comprehensive monitoring focused on providing automated solutions for system stability, model expiration dates, and ethical fairness. AI models require frequent monitoring based on shifts and data migration, ongoing execution of business needs, and risk thresholds. The models must orchestrate automated solutions that may function around the clock and need human intervention for significant issues.

Key Takeaways for Businesses to Concentrate

Business leaders must keep teams active to focus on the dangers of artificial intelligence, whether leaders plan to deploy new setups or for existing AI functionalities. This may be a practical approach for leaders to figure out ways to employ the technology for noble purposes.

Businesses must prioritize AI risk management plans and measure AI models and technology advance with updates. Managing the risks of AI must be a continuous process to align with AI systems.

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Anushree Bhattacharya

Anushree Bhattacharya is a Senior Editor with Ondot Media, where she covers stories on B2B business strategy, thought leadership, and corporate technology culture. She is a quality-oriented professional writer with eight years of experience. She has been curating content for the B2B industry, and her writing style is inclined toward how businesses want to perceive information about emerging digital transformations and technology developments. Anushree blends the best information on trending digital transformations, technology-driven stories, and SEO-optimized content. Anushree is proficient in technology journalism and curates information-driven stories about enterprise tech for EnterpriseTalk publication.

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