Solving three common AI challenges companies face

Also read: AI, humans and India's role in this tech revolution

Embed AI ethics into leadership development

Train leaders on AI biases they should look for and their implications for ethics, privacy, and compliance. For instance, consider recent cases involving major companies that have deployed an AI recruitment tool. These tools seek to enhance hiring efficiency, but bias in AI models might cause them to favor particular candidates over others. Set up a cross-functional AI governance task force comprising human resources, cybersecurity, and business strategy representatives to conduct intensive checks of AI systems to ensure they are making fair decisions.

Problem No. 2: Deploying AI without sufficient cybersecurity measures

AI can become a considerable company liability without proper cybersecurity measures, potentially leading to various threats, including data poisoning and cyberattacks. What should leaders do?

Solutions:

Prioritize AI cybersecurity

Conduct rigorous cybersecurity risk assessments before deploying any AI initiative, and invest in AI threat detection technology.

Develop AI-specific incident response protocols

Companies need new policies to guarantee data stewardship, model security, and detection of adversarial AI attacks. For example, consider Microsoft’s response to the Midnight Blizzard attack in 2024: After detecting that the Russian state-sponsored hacking group used AI-enhanced techniques to access emails and authentication materials, Microsoft quickly updated its incident response protocols to include AI-specific threat detection and mitigation procedures. These updated protocols focus on:

• Model security, ensuring that AI systems in their cybersecurity stack weren’t vulnerable to adversarial manipulation.
• Data stewardship by tightening access to sensitive logs and communications that could train or feed adversarial models.
• Detection of adversarial AI attacks, such as AI-generated phishing or automated credential stuffing attempts, by integrating AI-enabled monitoring tools with their security operations center.

Deploy zero-trust architectures

Transition from “trust but verify” most information to “verify everything,” leveraging strong data encryption and authentication policies for AI access. JPMorgan took a cautious approach, implementing a cybersecurity program that adopted AI as an imperative but shielded algorithmic trading models and required staff to monitor AI-facilitated transactions for potential deception. AI can operate 24/7 without breaks, using every permutation possible, which increases its ability to detect potential security breaches.

Also read: Global AI and the future of work

Problem No. 3: Investing in tools that can’t scale

The misalignment of core business processes typically results in siloed AI projects, which begin well but don’t scale. Even though AI is an incredible tool and a productivity multiplier, it is only as valuable as the person using it. In every organization, there are believers and skeptics. The skeptics work extremely hard to put the believers leveraging AI in a “corner,” which amplifies the siloed nature of AI use inside many firms.

Solutions:

Incorporate AI into business process automation

Ensure that AI augments current workflows and decision-making processes, rather than disrupting them. Align operational constraints with AI capabilities.

Measure the tool’s return on investment over technical performance

Establish metrics for assessing the degree to which AI enhances efficiency, revenue, and customer satisfaction, rather than focusing too much on algorithmic precision.

Create AI governance playbooks

Establish roles and expectations for AI management, model validation, and bias analysis. Evaluate the security threats and skill imbalances in adopting current AI deployments. For instance, General Motors has effectively implemented AI-driven automation throughout its supply chain, making AI a top business enabler.

Remember, AI integration starts with your people. Leading your organization in AI adoption isn’t only about investing in technology; it’s about prioritizing talent and security infrastructure to ensure a successful integration. Integrating AI into day-to-day tasks requires leaders who can acquire, scale, and build a workforce that uses AI responsibly, rather than allowing the technology to take over.

Is your organization ready to enter the AI era?