Tag: AI

Categories
Blog

AI and Compliance Training

AI-driven training tools are transforming how organizations deliver compliance programs. By offering personalized, interactive, and role-specific training at scale, AI eliminates many cost and logistical barriers that have historically made tailored training challenging. This evolution improves engagement and reduces compliance risks by equipping employees with relevant, actionable knowledge. Today, I want to explore how AI reshapes compliance training, supplemented with real-world examples of companies leading the charge.

Personalization at Scale

AI analyzes vast amounts of data, an employee’s role, learning history, and performance metrics to create tailored training experiences. This ensures that the content is directly relevant to each employee’s responsibilities. For example, a sales team focusing on international transactions might focus on anti-bribery and corruption rules under the FCPA. A procurement team could receive training on vendor due diligence, export control and sanctions, and conflict-of-interest disclosures. Conversely, a finance staff member might dive into anti-money laundering (AML) and financial controls.

You can integrate AI into your global compliance training programs to tailor content to employees’ roles. Through machine learning, your system can deliver specific modules to individuals, ensuring that high-risk roles receive advanced training while others get streamlined, relevant content. The result will be better alignment between training content and operational realities, boosting engagement and effectiveness.

Just-in-Time Learning

AI enables “just-in-time” learning, delivering content at the precise moment it’s needed. For example, an employee preparing to interact with a foreign government official might receive a refresher module on anti-corruption policies before the meeting. Similarly, an employee about to onboard a vendor might receive training on due diligence best practices. This approach effectively ensures that employees apply their knowledge in real-world scenarios when it matters most. It also minimizes the “forgetting curve” by delivering training in digestible chunks that reinforce memory retention.

This means you can use AI to deliver microlearning modules through your internal compliance training platform. Employees receive targeted reminders about data privacy regulations when working on projects involving personal data, ensuring compliance is seamlessly integrated into daily workflows.

Enhanced Engagement Through Gamification 

AI makes compliance training engaging by incorporating gamified elements like quizzes, leaderboards, and decision-making simulations. These interactive features transform mundane lessons into enjoyable experiences, boosting motivation and retention. Imagine employees participating in a simulated bribery scenario, navigating ethical dilemmas in real time. Such immersive experiences teach policies and foster critical thinking and decision-making skills.

For example, PwC’s Game of Threats™ is a digital game that simulates the speed and complexity of a real-world cyber breach. It is designed to help executives “understand the steps they can take to protect their companies. The game environment creates a realistic experience where both sides, the company and the attacker, are required to make quick, high-impact decisions with minimal information.” You can “coach players through realistic scenarios with different types of threat actors and their preferred methodologies and explain what they can do to better prevent, detect, and respond to an attack.”

Continuous Improvement

AI-powered platforms don’t just deliver training; they learn and adapt. These systems analyze performance metrics, such as quiz scores and engagement rates, to identify areas where employees struggle. Based on this data, the platform refines its content, ensuring that training evolves alongside organizational needs and regulatory changes.

One company implemented AI-driven tools for compliance training that adapt based on user feedback and performance data. If employees consistently fail a particular module, the AI identifies gaps and adjusts the content to address misunderstandings more effectively.

Cost-Effective Solutions for Large Organizations

Scaling traditional training methods across a large global workforce is challenging and expensive. AI simplifies this by automating the customization process, ensuring consistent quality across teams and geographies. It also reduces costs associated with in-person training sessions and printed materials—one large multinational leveraged AI to implement a scalable compliance training platform for its over 150,000 employees. By automating the delivery of role-specific training modules and offering multi-language support, Unilever significantly reduced training costs while maintaining high levels of engagement and effectiveness.

Overcoming Barriers to AI Adoption in Compliance Training

Unfortunately, despite its obvious benefits, some organizations hesitate to adopt AI-driven compliance training due to perceived challenges. Some of these challenges include one or more of the following concerns: The Cost Concern is where the initial investment in AI tools seems way too high. This is even where the long-term savings, through improved training efficiency and reduced compliance risks, far outweigh the upfront expenses. Another concern is around the Technological Complexity. Partnering with experienced vendors or consultants can simplify the implementation process, ensuring seamless integration with existing systems. Finally, there is the ever-present Cultural Resistance. Employees may resist AI-driven training for fear of surveillance or skepticism about its effectiveness. Clear communication about how AI enhances training rather than replacing human oversight can help alleviate these concerns.

The Future of Compliance Training: AI as a Strategic Advantage

AI-driven compliance training is more than just a technological upgrade; it is a strategic advantage that organizations can use in various ways. It can mitigate compliance risks by delivering tailored, engaging, and timely training. AI reduces the likelihood of compliance violations and associated penalties. It can build and foster trust between compliance and your customer base, which is corporate employees. Employees who feel supported with relevant, engaging training are more likely to embrace compliance as part of their workplace culture. Finally, it will allow you to stay ahead of the compliance curve in training and potentially the Department of Justice (DOJ). AI ensures training evolves alongside regulatory changes, keeping organizations proactive rather than reactive.

The message is clear: Investing in AI-driven compliance training is not just about ticking boxes; it is rather about building a resilient, ethical organization that thrives in today’s complex regulatory environment. If your company has not yet embraced the AI revolution in compliance training, now is the time to explore the possibilities. With the right tools and a commitment to meaningful employee engagement, you can transform compliance from a checkbox exercise into a powerful driver of business success.

Categories
Blog

AI, Process Management, and Compliance

Integrating artificial intelligence (AI) and advanced analytics with robust process management principles can unlock new levels of efficiency and innovation. Mars Wrigley, the global confectionery leader, offers an instructive case study. In an article in the Harvard Business Review entitled, How to Marry Process Management and AI Thomas H. Davenport and Thomas C. Redman wrote that through its strategic deployment of AI to digitize its supply chain and manage operations, Mars Wrigley demonstrates how a systematic approach to process management can achieve significant improvements in operational performance, customer satisfaction, and sustainability.

Mars Wrigley’s success story holds valuable lessons for compliance professionals about aligning technology, data, and governance to enhance compliance frameworks and drive value across organizations.

Digitization and AI: The New Frontier for Process Management

Mars Wrigley began its journey by building a digital twin of its production line and feeding real-time operational data into machine-learning models. The results were striking. The company received predictive insights that reduced overfilling, minimized waste, and optimized supply chain processes. They partnered with vendors like Aera Technology for data visualization and preventive maintenance and with Kinaxis to balance supply and demand, automate invoices, and increase truck utilization by 15%.

This underscores a critical point from a compliance standpoint: Technology can only enhance compliance when processes are well-defined, integrated, and aligned with organizational goals. Compliance officers must recognize the potential of AI to streamline compliance monitoring, enhance risk detection, and reduce manual inefficiencies.

For example, consider AI tools that monitor high-risk transactions or flag anomalies in employee expense reports. When implemented in a robust compliance framework, these tools improve detection rates and allow compliance teams to focus on strategic initiatives rather than routine checks.

The Role of Process Management in Compliance

Process management is about understanding how tasks fit together to create a specific outcome and then optimizing those sequences. Put another way, it is about operationalizing compliance. Whether addressing department-level activities or end-to-end processes, process management principles can yield transformative results when applied to compliance. What are some of the ways process management can do so?

In areas as basic as error reduction, well-managed processes minimize compliance failures by reducing error rates and increasing consistency. A traditional compliance department area is cross-functional coordination with other corporate departments. Effective compliance requires breaking down silos, whether between legal, finance, HR, or operations, and aligning departments toward common objectives.

This approach can also positively impact corporate culture by increasing stakeholder buy-in and employee engagement. Process management often conflicts with hierarchical management structures. In compliance, this tension may manifest when reconciling DOJ mandates with operational priorities in your organization. Persuading stakeholders to prioritize compliance demands strong leadership and effective change management.

AI and Process Management: A Compliance Blueprint

AI supports specific subprocesses within larger workflows, but true transformation occurs when organizations integrate these capabilities across end-to-end processes. For compliance professionals, this is a roadmap for embedding AI into compliance programs.

Step 1: Establish Ownership

Every effective compliance initiative begins with clear accountability. A defined ownership structure underpinned Mars Wrigley’s digital twin success. Compliance programs require similar clarity. Appointing a “compliance process owner” ensures cross-functional alignment, while department-level compliance champions can coordinate implementation.

Step 2: Map and Redesign Processes

Mapping current compliance processes is essential for identifying inefficiencies. Process mining tools, which analyze enterprise system logs to identify bottlenecks, can uncover hidden risks. For instance, tracking the due diligence lifecycle in third-party onboarding can reveal inefficiencies, such as delays in background checks or missed follow-ups.

Redesign efforts should prioritize risk-prone areas, leveraging AI tools to streamline activities like transaction monitoring, policy distribution, and whistleblower case tracking.

Step 3: Define Metrics and Set Targets

Compliance performance must be measurable. Metrics such as incident resolution times, training completion rates, and risk assessment quality should guide process improvements. AI enables real-time metrics monitoring, providing insights that compliance officers can act on immediately. Mars Wrigley’s use of analytics to improve truck utilization offers a parallel for compliance: by tracking resource allocation, compliance teams can reduce unnecessary costs while ensuring optimal coverage of risk areas.

Step 4: Leverage Technology and Data

AI tools such as robotic process automation (RPA) and natural language processing (NLP) are increasingly used in compliance programs to automate routine tasks. RPA can streamline repetitive activities like generating regulatory reports. NLP can analyze large volumes of text, such as contracts or policies, to identify risks or inconsistencies.

Compliance professionals must also advocate for standardized data practices. As Mars Wrigley’s case illustrates, data silos impede process efficiency. In compliance, inconsistent data can obscure risks, making standardized data governance a cornerstone of effective compliance.

Step 5: Foster a Culture of Continuous Improvement

AI and process management are not “set it-and-forget it” solutions. As Mars Wrigley demonstrated, continuous monitoring and iterative improvements are critical for sustaining gains. This means regularly reviewing and updating AI tools for compliance professionals to address emerging risks and regulatory changes.

Lessons for Compliance Professionals

Mars Wrigley’s journey highlights several key takeaways for compliance leaders:

  1. Invest in AI Thoughtfully. Technology is not a silver bullet. Its effectiveness depends on how well it integrates with and supports compliance processes.
  2. Adopt a Holistic View of Compliance. Compliance risks rarely confine themselves to one department. Breaking down silos through cross-functional process management improves visibility and reduces risk.
  3. Prioritize Data Governance. High-quality, standardized data is essential for both AI and compliance. Without it, even the best tools cannot deliver meaningful insights.
  4. Embrace Change Management. As with Mars Wrigley’s digital transformation, compliance process improvements require buy-in from leadership and employees.

The Compliance Call to Action

Compliance has been reactive for too long, focusing on addressing failures rather than preventing them. Integrating AI into process management offers an opportunity to shift that paradigm. By combining the best of technology and process management, compliance programs can reduce risk and enhance business value.

Mars Wrigley’s success story reminds us that the tools and strategies to transform compliance are available—but the onus is on compliance professionals to lead the charge. Whether through smarter risk management, better stakeholder engagement, or innovative technology adoption, the path forward is clear: process management and AI are not just operational tools; they are the future of compliance.

Now is the time to act. By adopting process management principles and leveraging AI, compliance leaders can build programs that are not only effective but also resilient, sustainable, and aligned with organizational goals. The question is no longer whether compliance should embrace these tools but how quickly they can integrate them into their processes.

By learning from companies like Mars Wrigley, compliance professionals can reimagine their programs, aligning them with the business’s needs while staying ahead of regulatory requirements.

Categories
SBR - Authors' Podcast

SBR – Author’s Podcast – Exploring the Future of Work, Ethics, and Compliance with Kelly Monahan, Part 2

Welcome to the Sunday Book Review, The Authors Podcast! Host Tom Fox visits with authors in the compliance arena and beyond in this Podcast Series. Today, Tom is joined by his good friend and colleague, Earnie Broughton (Earnie from Boerne), to visit with Dr. Kelly Monahan, co-author of the soon-to-be-released book Essential: How Distributed Teams, Generative AI, and Global Shifts are Creating a New Human-Powered Leader.  (Co-authored with Dr. Christie Smith) We three had such good fun that we went on for nearly an hour, so we have broken up the interview into two podcasts. If you have not checked out our first episode, you can do so by clicking here.

In Part 2, we deeply dive into effective communication tools for conveying corporate values to diverse workplace groups, emphasizing tailored training and gamification. Kelly highlights the importance of engaging, behavior-reinforcing communications through storytelling and public recognition systems. Emphasizing intrinsic motivation over financial incentives, Kelly draws on behavioral economics and the importance of fostering an environment of curiosity and context awareness for leadership roles. The discussion also addresses the nuances of generational differences in the workforce and the importance of diversity, equity, inclusion (DEI), and ESG initiatives for long-term organizational sustainability. Compliance professionals are encouraged to stay ahead of AI developments and promote positive behaviors to align with evolving business and ethical standards.

Key highlights:

  • Effective Communication Tools for Corporate Values
  • Future of Leadership in the Age of AI
  • Suspending Self-Interest and Cultivating Curiosity
  • Importance of Context in Ethical Decision-Making
  • Generational Differences in the Workforce
  • Role of Ethics and Compliance Professionals

Resources:

The Essential Website

Pre-Order Essential: How Distributed Teams, Generative AI, and Global Shifts are Creating a New Human-Powered Leader on Amazon.com

Kelly Monahan on LinkedIn

Earnie Boughton On LinkedIn

Tom Fox

Instagram

Facebook

YouTube

Twitter

LinkedIn

Categories
Blog

Kaizen 2.0: Leveraging AI for Continuous Improvement in Compliance

In the late 1940s, engineer Taiichi Ohno introduced the world to the Toyota Production System, an operational approach rooted in the Japanese principle of Kaizen or, as we call it today, continuous improvement. By prioritizing incremental enhancements and engaging employees at all levels, Toyota transformed manufacturing with concepts like worker empowerment, just-in-time manufacturing, root-cause analysis, and total quality management. The result? Toyota became the largest automaker in the world and a gold standard for process excellence. All this and much more was found in a recent Harvard Business Review article, The Secret to Successful AI-Driven Process Redesign, by H. James Wilson and Paul R. Daugherty.

I use their article as a starting point to explore where Kaizen meets the transformative power of artificial intelligence (AI) in the compliance realm. Kaizen 2.0 empowers employees with AI tools to make data-driven decisions, streamline processes, and elevate organizational performance in this new era. For compliance professionals, the principles behind this transformation offer a powerful roadmap for managing risk, embedding compliance into your business processes, and creating resilient risk management structures.

From Kaizen to Kaizen 2.0: The Role of AI in Compliance 

At its core, Kaizen is about empowering employees to improve processes continuously. Kaizen 2.0 amplifies this with AI, making advanced tools accessible to non-technical employees and enabling them to synthesize complex data for actionable insights. For compliance teams, this means using AI not to replace human judgment but to enhance it, whether by automating routine tasks, detecting risks, or uncovering inefficiencies.

Mercedes-Benz provides an interesting example. The company’s MO360 Data Platform democratizes data access across its global production network, enabling employees at every level to make data-driven decisions. A frontline worker can query AI about assembly-line bottlenecks or supply chain delays and receive actionable real-time recommendations. Imagine a compliance professional leveraging similar tools to identify patterns in third-party transactions or track policy adherence across business units.

This democratization of information underscores a key lesson for compliance professionals. AI tools are most effective when they empower teams rather than replace them. By augmenting human expertise, compliance programs can scale their impact while fostering a culture of accountability and engagement.

AI-Driven Tools: Unlocking New Compliance Opportunities 

Incorporating AI into compliance frameworks opens the door to new possibilities. Consider the following applications for the compliance function.

  • Root-Cause Analysis

Root-cause analysis can become more powerful with AI. Generative AI tools can analyze vast amounts of data to pinpoint the underlying root causes of compliance failures. For example, training AI on high-quality data can reduce false positives in transaction monitoring, allowing teams to focus on genuine risks. Using AI in the root-cause process could allow a compliance professional to determine the root cause of every compliance failure, whether simply a hiccup or a major system failure.

  • Just-in-Time Compliance

Borrowing from Toyota’s just-in-time manufacturing, compliance teams can use AI to implement “just-in-time compliance.” AI tools can monitor real-time transactions, communications, or activities, flagging issues as they occur rather than after the fact. This proactive approach aligns with regulators’ increasing focus on continuous monitoring. Also, consider how you could send a personalized compliance message to an employee who is about to travel to a high-risk country or engage in a high-risk activity.

  • Employee Empowerment

AI-enabled compliance platforms can empower employees across the organization to identify and address risks. This offers a great opportunity to move a compliance tool directly to the first line of defense. A generative AI tool could help employees draft accurate disclosures, navigate complex policies, or report concerns anonymously and securely. By embedding compliance tools into day-to-day workflows, organizations can create a proactive compliance culture and make the process more efficient.

Reshaping Risk Management: Lessons from Kaizen 2.0 

One of the most transformative aspects of Kaizen 2.0 is how it redefines risk management. Merck uses generative AI to improve quality control in drug inspection processes in the pharmaceutical industry. By creating synthetic defect-image data, AI reduces false rejects by over 50%, cutting waste and enhancing efficiency.

Compliance professionals can take inspiration from this approach by leveraging AI to address data quality issues. For instance, AI-powered tools can identify inconsistencies in due diligence data, streamline third-party risk assessments, and ensure consistent policy application across global operations.

Similarly, companies like Colgate-Palmolive and Nestlé are using AI to drive innovation in product development. For compliance teams, these advancements signal the potential for AI to transform regulatory reporting, training, and monitoring by making these processes more adaptive and aligned with business goals.

Overcoming Challenges: Ensuring Human-Centric AI Adoption 

While AI offers immense potential, successful adoption requires careful planning and execution. Compliance professionals must address the following challenges:

  1. Employee Training and Engagement. Like Mercedes-Benz’s Turn2Learn initiative, compliance teams should invest in training employees in AI in compliance programs. Educating staff on using AI tools effectively ensures they can take part in compliance initiatives and take ownership of risk management.
  2. Data Quality and Integration. High-quality data is the foundation of effective AI tools. Compliance leaders must champion data governance initiatives to eliminate silos, standardize data formats, and ensure accuracy. This has been on the Department of Justice’s (DOJ) mind since 2020 and was reiterated in the 2024 Evaluation of Corporate Compliance Programs.
  3. Ethical AI Usage. Compliance teams must lead efforts to ensure AI tools are used ethically and transparently. This includes validating AI outputs, addressing biases, and maintaining accountability for decisions informed by AI.

The Future of Kaizen 2.0 in Compliance

The convergence of AI, digital twins, and autonomous agents will redefine process management in compliance. Autonomous agents powered by generative AI can independently execute tasks, adapt strategies, and continuously improve their performance. This means a shift from routine oversight to strategic leadership for compliance professionals.

Walmart uses autonomous agents for inventory management. Compliance teams could deploy similar agents to monitor real-time regulatory changes, update policies, and notify stakeholders of critical updates.

Looking ahead, digital twins, which are virtual models of real-world systems, could revolutionize compliance training and testing. A digital twin of an organization’s compliance framework could simulate the impact of regulatory changes, test the effectiveness of controls, and identify vulnerabilities before they become liabilities.

A Call to Action for Compliance Professionals

The principles of Kaizen 2.0 offer a roadmap for transforming compliance programs. By embracing AI and empowering employees, compliance leaders can foster a culture of continuous improvement that meets DOJ requirements and drives business success. Three key steps help the compliance professional begin.

The first is to identify opportunities for AI integration in both your compliance program and overall compliance function. You should begin by mapping compliance processes and identifying areas where AI can add value, such as risk monitoring, policy management, or training. Next is engagement with employees by fostering a culture of collaboration by involving employees in AI-driven compliance initiatives. Provide training and resources to help them contribute to continuous improvement.  The final step is to monitor and continuously improve. Establish clear metrics for compliance performance and use AI to monitor progress. Review and refine processes to ensure they remain effective and aligned with business goals. Update, refine, and improve as the data becomes available to you.

Compliance professionals have a unique opportunity to lead our organizations into the future. By leveraging Kaizen 2.0 principles and AI tools, we can create compliance programs that are effective, resilient, adaptive, and aligned with organizational values. Let’s make continuous improvement the cornerstone of a fully operationalized compliance program and demonstrate to your organization that effective compliance leads to more efficient processes, which leads to greater ROI and profitability.

Categories
Blog

Overcoming AI Resistance for Corporate Compliance Professionals

Artificial intelligence (AI) presents a paradox for corporate leaders. On one hand, its potential is undeniable: in a 2023 Gartner survey, 79% of corporate strategists deemed AI, automation, and analytics critical to their success. Yet, only 20% actively use AI in their daily activities. The gap between intention and action speaks volumes, especially in compliance, where AI offers unprecedented opportunities to manage risk, enhance efficiency, and ensure adherence to regulations. In a recent Harvard Business Review Article entitled Why People Resist Embracing AI, Julian De Freitas reviewed this issue and provided some ways to think through how to respond.

Despite its promise, AI adoption is hindered by human skepticism. Concerns range from fears of job loss to distrust in AI’s capacity for ethical decision-making. For compliance professionals, understanding and addressing these barriers is vital for leveraging AI to strengthen compliance programs and drive corporate integrity. In this blog post, I want to explore these challenges and how compliance leaders can overcome them. I have adapted Freitas’ article for the compliance professional.

The Five Barriers to AI Adoption in Compliance

  • AI’s Opacity: The “Black Box” Problem

Many employees resist AI because it operates as an inscrutable “black box,” offering conclusions without clear explanations. This lack of transparency can be a deal-breaker for compliance teams, as accountability is paramount in regulatory environments. How can an algorithm flag a suspicious transaction or identify potential bribery risks without explaining its rationale?

Compliance leaders should prioritize AI tools that offer clear, comparative explanations to overcome this barrier. For instance, instead of stating that a third-party transaction was flagged as high risk, the system should explain why, perhaps because of discrepancies in invoice patterns or connections to sanctioned entities. Such insights enhance trust and empower teams to make informed decisions.

Start small. Introducing simpler AI models before scaling to more complex ones can build confidence. Much like Miroglio Fashion’s approach to demand forecasting, a pilot program allows teams to familiarize themselves with AI and see its benefits before adopting more advanced systems.

  • AI Is Perceived as Emotionless

Compliance often involves navigating complex, human-centric issues, such as whistleblower reports, triage, Institutional Justice/Fairness, or ethical dilemmas. Many employees doubt AI’s ability to handle such subjective tasks, viewing it as emotionless and rigid. While AI can process vast amounts of data, can it understand the nuances of a whistleblower’s complaint or the subtleties of cultural differences in compliance?

Here, framing matters. Compliance leaders should emphasize AI’s ability to provide objective insights while leaving subjective decision-making to human professionals. For instance, AI can flag patterns in expense reports suggesting potential fraud, but the decision to investigate remains with compliance officers.

Anthropomorphizing AI tools can also make them more relatable. Tools like Amazon Alexa, with humanlike names and voices, have shown that users are more willing to interact with AI when it feels approachable. However, tread carefully in sensitive contexts, such as investigations, where a less personalized AI may feel less intrusive. Always remember the Human-in-the-Loop.

  • AI’s Perceived Rigidity

A common misconception about AI is that it cannot adapt or evolve. For compliance professionals, this rigidity could mean AI systems are seen as inflexible, unable to account for unique organizational contexts or evolving regulatory landscapes.

To address this, emphasize AI’s learning capabilities. Tools that improve over time, such as those that adapt to new fraud schemes or regulatory updates, mainly through large language models, can demonstrate AI’s ability to evolve alongside the business. Netflix’s content recommendations, for example, continuously improve based on user behavior. Compliance systems should follow suit, showcasing how AI refines its processes to meet organizational needs better.

At the same time, compliance leaders must balance flexibility with predictability. Highly adaptable AI systems can introduce risks if they deviate too far from expected outcomes. Regular monitoring and safeguards are critical to ensure the system operates within defined ethical and regulatory boundaries.

  • Fear of Loss of Control

AI’s autonomy often feels threatening, particularly in compliance, where human judgment is paramount. Employees may worry that AI will override their expertise or act independently in ways that could jeopardize compliance efforts. For example, an AI tool autonomously approving transactions without human review might lead to unchecked risks.

The solution? Implement human-in-the-loop systems, where AI supports decision-making rather than replaces it. Nest’s smart thermostat, which allows users to switch between manual control and automation, is an excellent analogy. In compliance, this could mean using AI to flag risks while leaving final decisions to compliance officers. Such hybrid models restore employees’ sense of agency while ensuring AI enhances rather than undermines human oversight.

  • Preference for Human Interaction

Compliance is inherently relational. Building trust, navigating cultural differences, and addressing employee concerns require human empathy—qualities many believe AI lacks. Resistance to AI often stems from the belief that humans are better equipped to handle nuanced interpersonal issues.

While AI cannot replicate human empathy, it can support human efforts. For example, generative AI can analyze patterns in hotline reports to identify systemic issues, allowing compliance officers to focus on building relationships and fostering a speak-up culture. Framing AI as a tool that amplifies human capabilities rather than replacing them can help reduce resistance.

Strategies for Driving AI Adoption in Compliance

  1. Start with Transparency. Be upfront about what AI can and cannot do. Educate employees on how AI systems work, their limitations, and the safeguards to prevent misuse. Transparency builds trust and encourages collaboration.
  2. Focus on Small Wins. Demonstrating tangible benefits through pilot programs can win over skeptics. For instance, AI can automate low-risk tasks like policy distribution or routine transaction monitoring. Success in these areas can pave the way for broader adoption.
  3. Prioritize Training and Support. AI adoption requires investment in employee training. Equip teams with the skills to use AI tools effectively and provide ongoing support to address questions or concerns. Mercedes-Benz’s Turn2Learn initiative offers extensive AI training and is a model worth emulating.
  4. Align AI with Ethical Standards. Compliance professionals must ensure AI systems align with the organization’s values and ethical standards. Regular audits, bias checks, and transparent reporting can reassure stakeholders that AI is being used responsibly.
  5. Measure and Iterate. Establish clear metrics to evaluate AI’s impact on compliance processes. Use these insights to refine the system, addressing pain points and enhancing effectiveness.

AI in Compliance: A Strategic Imperative 

AI’s potential to revolutionize compliance is immense. From automating routine tasks to identifying emerging risks, it can make programs more efficient, proactive, and resilient. However, realizing this potential requires more than technology; it demands a cultural shift.

Compliance leaders must champion AI adoption by addressing psychological barriers and demonstrating its value. Organizations can harness AI to strengthen compliance and drive business success by prioritizing transparency, fostering trust, and empowering employees. As the Gartner survey reminds us, AI is not just a tool for the future—it’s a strategic imperative for today. The question isn’t whether to adopt AI but how to do so in a way that aligns with organizational goals and values. For compliance professionals, the path forward is clear: embrace AI, empower your teams, and lead the charge toward a more efficient, ethical, and innovative compliance landscape.

Categories
Daily Compliance News

Daily Compliance News: January 14, 2025 – The RTO Compliance Edition

Welcome to the Daily Compliance News. Each day, Tom Fox, the Voice of Compliance, brings you compliance-related stories to start your day. Sit back, enjoy a cup of morning coffee, and listen in to the Daily Compliance News—all from the Compliance Podcast Network. Each day, we consider four stories from the business world: compliance, ethics, risk management, leadership, or general interest for the compliance professional.

Top stories include:

  • Using AI as an excuse for ‘cost avoidance.’ (WSJ)
  • Crypto’s compliance conundrum. (CoinDesk)
  • Has corporate purpose lost its purpose? (FT)
  • Return To Office compliance. (Bloomberg)

For more information on the Ethico Toolkit for Middle Managers, available at no charge, click here.

Check out The FCPA Survival Guide on Amazon.com.

Categories
SBR - Authors' Podcast

SBR – Author’s Podcast – Exploring the Future of Work, Ethics, and Compliance with Kelly Monahan, Part 1

Welcome to the SBR – Author’s Podcast! Host Tom Fox visits with authors in the compliance arena and beyond in this Podcast Series. Today, Tom is joined by his good friend and colleague, Earnie Broughton (Earnie from Boerne), to visit with Dr. Kelly Monahan, co-author of the soon-to-be-released book Essential: How Distributed Teams, Generative AI, and Global Shifts Are Creating a New Human-Powered Leadership (Co-authored with Dr. Christie Smith) We three had such good fun that we went on for nearly an hour, so we have broken up the interview into two podcasts.

In today’s Part 1, Kelly delves into her academic and professional journey and how her experiences have shaped her focus on the intersection of technology and human development. The discussion centers on three macro trends affecting the future of work: generative AI, remote and hybrid work models, and the rise of the alternative workforce. Kelly elaborates on the ‘gray collar’ concept of workers, emphasizing the merging of physical labor with technology. She also highlights the importance of power skills, formerly known as soft skills, in navigating these transformations successfully.

Key highlights:

  • The Future of Work: Trends and Insights
  • AI and Its Impact on the Workforce
  • The Rise of the Gray Collar Workforce
  • Freelancers and Corporate Culture
  • Leadership Mindset and Workforce Engagement

Resources:

The Essential Website

Pre-Order: Essential: How Distributed Teams, Generative AI, and Global Shifts Are Creating a New Human-Powered Leadership on Amazon.com

Kelly Monahan on LinkedIn

Earnie Boughton on LinkedIn

Tom Fox

Instagram

Facebook

YouTube

Twitter

LinkedIn

Categories
Life with GDPR

Life With GDPR – Navigating the EU AI Act

Tom Fox and Jonathan Armstrong, renowned expert in cyber security, co-host the award-winning Life with GDPR. In this episode, they discuss a pressing deadline for compliance officers: the February 2nd enforcement of the EU AI Act’s prohibitions on unacceptable AI risk.

Tom and Jonathan look at the phased implementation of this complex legislation, detailing the obligations of businesses using AI in their EU operations. Jonathan emphasizes the importance of identifying ‘shadow AI’ within organizations, from HR recruitment tools to consumer applications, and the substantial penalties for non-compliance, which can reach up to $35 million or 7% of global annual revenue. They also cover a practical five-step plan to help companies move towards compliance, involving board awareness, an AI inventory, assessment of AI tools, contract reviews, and transparency measures. Tune in to understand the nuances of this legislation and how to prepare your organization before the rapidly approaching deadline.

Key takeaways:

  • Understanding the EU AI Act
  • Prohibited AI Applications
  • Corporate and Personal Liability
  • Steps to Compliance

Resources:

Connect with Tom Fox

Connect with Jonathan Armstrong

Categories
Blog

Revolutionizing Compliance with AI-Powered KPIs 

In the modern corporate landscape, traditional key performance indicators (KPIs) are struggling to meet the demands of dynamic compliance environments. These legacy metrics often fail to align operations, prioritize resources, and drive accountability toward strategic objectives. For compliance professionals, these shortcomings are particularly critical: ineffective KPIs can lead to missed risks, inefficient processes, and poor decision-making, ultimately jeopardizing organizational integrity.

In a recent article in the Sloan Management Review, entitled The Future of Strategic Measurement: Enhancing KPIs With AI, authors Michael Schrage, David Kiron, François Candelon, Shervin Khodabandeh, and Michael Chu explored these and other issues, which I have adapted for the compliance professional.  By incorporating artificial intelligence (AI), organizations are reimagining what KPIs can accomplish—not just as performance trackers but as drivers of strategic differentiation and value creation.

The Shortcomings of Legacy KPIs in Compliance

Legacy KPIs often focus narrowly on outputs, such as the number of training sessions conducted or hotline calls logged. While these metrics provide valuable data, they frequently fail to provide solid information in various ways. The first is that legacy KPIs are taken in a vacuum with no appreciation of the interconnected nature of corporate risks. Just as compliance does not (or at least should not) operate in a vacuum, risks in one area often cascade into others, yet traditional KPIs rarely reflect these interdependencies. The retrospective nature of KPIs. Metrics rooted in historical data are inherently backward-looking, limiting their utility for forecasting and proactive risk management.

Finally, corporate silos, which are a perennial challenge in compliance, and static KPIs can reinforce them rather than foster cross-functional collaboration. Legacy KPIs do not promote alignment across disparate corporate functions. These limitations hinder a compliance professional’s ability to effectively anticipate, prevent, and address misconduct.

Enter Smart KPIs: A New Era of Compliance Metrics

AI-powered KPIs offer a smarter, more dynamic approach to performance measurement. These metrics are descriptive, predictive, and prescriptive. Such metrics will allow a corporate compliance function to provide new and different insights, such as some of the following.

  • Analyze past and current compliance performance to identify gaps.
  • Anticipate future risks and compliance trends based on patterns in data.
  • Recommend actions to mitigate risks and optimize outcomes.

For example, AI can transform a traditional metric like the “number of third-party audits conducted” into a prescriptive KPI that evaluates audit results, predicts the highest risk areas, and recommends corrective actions.

Case Study: Wayfair and the Evolution of Lost-Sales KPIs

The article discussed Wayfair’s reengineering of its lost-sales KPI and offers valuable insights for compliance professionals. Initially, the retailer calculated lost sales on an item-by-item basis, but AI analysis revealed that many “lost” sales were category retentions, as customers purchased alternative items. This revelation led Wayfair to redesign its KPI to measure category-based retention. The result? Smarter metrics aligned product placement with operational constraints, improving customer satisfaction and operational efficiency.

This case study provides a clear set of lessons for corporate compliance and the compliance professional. Compliance teams can use AI to rethink KPIs that do not fully capture performance nuances. For instance, instead of merely tracking the number of training completions, a smarter KPI could evaluate behavioral changes post-training or identify employees most at risk of ethical lapses based on historical data. This, in turn, could provide greater insight into training effectiveness and how a compliance professional might think about targeted training.

KPI Governance: A Compliance Imperative 

One of the most critical aspects of AI-enhanced KPIs is governance. Organizations need robust governance mechanisms to ensure KPIs evolve with strategic objectives and maintain their relevance over time. For a compliance professional, this means several different approaches.

  1. Continuous Review of Metrics. Regularly revisiting KPIs to ensure they remain aligned with evolving regulatory landscapes and business priorities.
  2. Meta-KPIs for Quality Assurance. Developing “KPIs for KPIs” to assess their accuracy, relevance, and effectiveness.
  3. Cross-Functional Oversight. Establishing governance structures that bring together compliance, legal, and operational teams to oversee metric design and implementation.

The bottom line is that accountability for KPI performance, both the metrics themselves and the outcomes they drive, must be embedded into the compliance framework.

How AI Enhances Compliance KPIs

AI-enhanced KPIs bring new capabilities to compliance programs in three key manners. First, in risk anticipation. Predictive KPIs can identify emerging compliance risks, such as regulatory changes, third-party risk management, or shifts in employee behavior, enabling proactive mitigation. The second area is holistic insights. By analyzing data across functions, AI can uncover hidden correlations, such as how employee hotline reports, visits to the compliance department website, or even the number of requests to FAQs might signal compliance risks in supply chain operations. Finally is the area of targeted recommendations. Prescriptive KPIs can suggest specific actions, like prioritizing high-risk vendors for audits or tailoring training to address observed knowledge gaps. For example, AI could analyze whistleblower reports alongside financial data to identify patterns indicative of systemic fraud, providing actionable insights for remediation. 

 This more holistic approach also addresses one of the key risk areas around KPIs: stagnate KPIs. The 2008 financial crisis underscores the dangers of relying on outdated KPIs. Banks’ dependence on “value at risk” metrics, which failed to account for the growing influence of subprime mortgages, contributed to catastrophic losses. Compliance professionals must guard against similar pitfalls by regularly challenging assumptions underpinning legacy KPIs. AI can aid in this process by continuously analyzing data to reveal when a metric is no longer fit for purpose.

Steps to Implement Smarter Compliance KPIs

Compliance professionals can take the following steps to transition from legacy to AI-enhanced KPIs.

  1. Audit Existing KPIs. Assess whether current metrics adequately capture compliance risks and align with strategic objectives.
  2. Leverage AI for Data Analysis. Use AI tools to uncover hidden patterns in compliance data, such as correlations between employee turnover and ethics violations.
  3. Collaborate Across Functions. Work with IT, legal, and operations teams to ensure KPI redesigns reflect organizational priorities.
  4. Invest in Training and Culture. Equip compliance teams with the skills to interpret and act on AI-generated insights while fostering a culture of data-driven decision-making.
  5. Monitor and Improve KPIs. Establish processes for ongoing KPI evaluation, ensuring they evolve alongside regulatory and stakeholder input and business changes.

Challenges and Ethical Considerations 

While AI-enhanced KPIs offer immense potential, they also present challenges. These challenges include some of the following. Just as with more generative AI, algorithms can be biased. AI models are only as unbiased as the data on which they are trained. Compliance teams must ensure that their AI systems uphold principles of fairness and equity. Always remember the Human in the Loop to preclude over-reliance on AI. While AI can inform decision-making, it should not replace human judgment. Compliance professionals must strike a balance between algorithmic insights and ethical considerations. Finally, there are data privacy concerns. Collecting and analyzing large datasets for KPI development must comply with data privacy regulations.  

Conclusion: The Future of Compliance Metrics 

The rise of AI-enhanced KPIs marks a paradigm shift in measuring and managing compliance performance. By embracing smarter, more dynamic metrics, compliance professionals can gain deeper insights, anticipate risks, and drive better outcomes.  Much like Wayfair and other forward-thinking organizations, compliance teams must be willing to challenge the status quo, leverage technology, and prioritize continuous improvement. The era of static, backward-looking KPIs is over. In its place is a future where smart KPIs enable compliance functions to not only measure performance but actively enhance it—turning compliance from a cost center into a source of strategic value. The question is not whether your organization should adopt AI-powered KPIs but how soon your compliance program can reap the benefits. The time to act is now.

Categories
Compliance Tip of the Day

Compliance Tip of the Day – Using AI for Continuous Monitoring

Welcome to “Compliance Tip of the Day,” the podcast where we bring you daily insights and practical advice on navigating the ever-evolving landscape of compliance and regulatory requirements. Whether you’re a seasoned compliance professional or just starting your journey, we aim to provide bite-sized, actionable tips to help you stay on top of your compliance game. Join us as we explore the latest industry trends, share best practices, and demystify complex compliance issues to keep your organization on the right side of the law. Tune in daily for your dose of compliance wisdom, and let’s make compliance a little less daunting, one tip at a time.

Today, we consider how AI allows compliance to take a proactive, data-driven approach to emerging risk analytics.

For more information on the Ethico Toolkit for Middle Managers, available at no charge, click here.

Check out the entire 3-book series, The Compliance Kids, on Amazon.com.