Artificial intelligence is already reshaping industries worldwide. However, AI’s rapid ascent has overwhelmed the many business leaders struggling to manage and implement its capabilities effectively within their companies.
To thrive in this new era, executives must first address three common challenges organizations face with AI integration. Here’s a look at those three obstacles and some advice for overcoming them.
Problem No. 1: Failing to develop internal AI talent
Many organizations are overly focused on recruiting external AI experts while neglecting to train and upskill their current employees. This gap can create a two-tiered workforce: one that knows and understands how to work with AI and another that lags behind.
Solutions:
Call for widespread AI literacy
Develop tiered AI training programs for all employees to make the workforce aware of the benefits and risks of relying on AI. For example, while AI can help an employee refine the wording of a report, the employee should be aware that AI can introduce inaccuracies. So, the employee needs to double-check any data it derives from AI by digging deeper for the original sources of information. AI risk training for all executives, not just technology leaders, should be mandatory.
Go beyond one-off training sessions
Shift away from isolated training and move toward continuous AI learning streams that are part of everyday workflows. Implement reverse mentoring, with AI specialists coaching business executives on the best ways to use the technology. Consider CEO Satya Nadella’s approach at Microsoft, which focused on hiring data scientists and reskilling the entire employee base to think and act with AI in mind. By reskilling employees, the culture will gradually shift toward increasing cognitive capacity, making everyone better equipped to operate in this dynamic VUCA (volatile, uncertain, complex, and ambiguous) environment. Infusing AI literacy into leadership development will help close the digital skills gap.
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.
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?
Image created by HBSWK with photograph from AdobeStock and assets created with AdobeFirefly.
