Using Competitive Intelligence Thinking to Control Risks with AI Systems

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Using Competitive Intelligence Thinking to Control Risks with AI Systems

Many are frantically embracing artificial intelligence (AI) to gain a competitive edge. To be seen as cutting edge and optimising their processes. Some have jumped on the bandwagon and will move once the next one comes. But what’s clear is that they must also grapple with the heightened risks of its implementation. Never mind those who have built something that’s a pointless waste of time. Or has it already been superseded by even newer AI? This article is called Using Competitive Intelligence Thinking to Control Risks with AI Systems.

For AI’s safety and security, proactive risk must be implemented. In this article, we’ll delve into AI risk management. Explore the potential risks associated with AI and the best management practices. And we may mention competitive intelligence once or twice.

Understanding AI Risks

The National Institute of Standards and Technology (NIST) defines AI risks as the: potential:

“harm to individuals, organisations, or systems resulting from developing and deploying AI systems”.

These risks can originate from various sources and countries, including: 

  • The data used to train and test AI models
  • The algorithms, and 
  • How the AI system interacts with users

AI risks include everything from biased people hiring tools.

to financial market crashes caused by uncontrollable trading algorithms. As AI adoption becomes more widespread, these risks can have far-reaching impacts. Making it crucial to address them proactively.

Measuring, Managing, and Mitigating AI Risks

Governance, risk, and compliance processes are pivotal in identifying and managing AI risks. Three significant approaches form the foundation of AI governance:


Organisations must adhere to guidelines that inform the development and use of AI, including legislative standards and ethical norms. 


Addressing risks arising from design flaws and a lack of appropriate governance is essential. Implementing robust procedures can help mitigate potential harm.

Ethical Consciousness

Taking actions motivated by moral or political awareness. A desire to do, or been seen to do, the right thing. Encompassing adherence to codes of conduct, corporate social responsibility, and institutional philosophy. Integrating responsible principles throughout the design, implementation, and maintenance phases is crucial. These principles could minimise or prevent harmful consequences during AI projects.

AI Risk Management: Identifying, Assessing, and Mitigating Risks

Risk management involves identifying, assessing, and managing risks related to AI technologies, including:

  • Technical risks
  • Security vulnerabilities
  • Algorithmic bias
  • Ethical considerations
  • Regulatory compliance

Five main risk verticals must be considered when assessing AI systems:


Algorithmic failure in unexpected circumstances or attacks leads to financial and physical loss. Mitigation strategies include improving model generalisation and using adversarial training.


Mitigating the risk that AI algorithms may mistreat individuals. Or groups of individuals, particularly in applications with significant societal impacts. Data debiasing and model amendments can reduce bias. Who built the AI, to what ends and what motivation? Do they see AI as a weapon?


Managing the potential for algorithms to leak sensitive. Or breaching and using personal data. Data minimisation and anonymisation techniques can address privacy risks.


Reducing the risk of system decisions being challenging for users and developers to understand. Improved documentation procedures and interpretability tools can address this risk.


Mitigating the risk of the system not performing well relative to its business case. Regular performance monitoring and data collection can improve model efficacy.

The Emergence of AI Regulation and the Importance of Transparency

Governments worldwide are proposing regulations to manage AI risks effectively. The European Union’s AI Act and the US government’s Blueprint for an AI Bill of Rights aim to create risk-based frameworks for AI use. Organisations must adopt robust governance and risk management practices to comply. And avoid the associated reputational and financial risks.

Closing the Gap: Embracing AI Risk Management

Despite the soaring adoption of AI, many are lagging in implementing risk management. A gap exists between the strategic priority given to AI and the implementation of responsible-AI programs. By control risk AL management frameworks, we gain the ability to:

  • Gain insights with an AI inventory
  • Upskill the workforce
  • Improve AI system performance

The regulation of AI has been a topic of significant interest and concern in the West. The US, UK and EU and others are working to develop AI regulations to address the associated risks. The primary goals of these regulations are to ensure the responsible and ethical: 

  • Development
  • Deployment
  • Use of AI 

While fostering innovation and maintaining a competitive edge. It’s about control risks.

However, these strict regulations in the West could put us at a disadvantage. Especially when dealing with regions where AI development is not regulated. Such as Russia, North Korea, and China. Clearly, the West, Russia and China all see AI as a weapon to be used. AI is both a weapon of mass destruction and a weapon of mass corruption.

Let’s explore some of the key aspects and implications of this issue:

Regulatory Divergence

A key concern is that the West’s stringent regulations may create a regulatory divergence between regions. The West may impose strict rules to ensure ethical AI development. But countries with less regulation may have a competitive advantage by adopting a more permissive approach. This could lead to outsourcing AI development to countries with less regulation to bypass stricter rules.

Impact on Innovation

Overregulation in the West might stifle innovation. Thus hindering the development of cutting-edge AI technologies. Innovation often thrives in an environment that encourages experimentation and risk-taking. If AI regulations are too rigid, bureaucratic hurdles will slow down research and development. This will give countries with looser regulations a head start in technological advancements.

Security Concerns

Russia, North Korea, and China have been known for their aggressive pursuit of AI technologies. Including, of course, applications for military purposes. To believe the West is any different would also be highly naive. The West publically aim to regulate AI responsibly. But what’s stopping less-regulated countries from developing AI systems without ethical considerations? Potentially leading to unintended security risks in the international arena.

Geopolitical and Economic Competition

AI is considered a strategic technology with significant geopolitical and economic implications. The race for AI dominance has become an essential aspect of global competition. If the West’s regulations restrict AI growth, it may impact its competitive position. Allowing other nations to take the global lead.

Addressing the Challenge

We must strike a balance to address the concerns of overregulation and maintaining a competitive edge. A balance between fostering innovation and ensuring ethical AI development. This could involve flexible regulations encouraging responsible AI practices. With the promotion of investment into ethical AI research and development. Not sure when investors become the beacon of hope regarding ethics and doing the right things. But that’s another story. Whatever they say on their websites, they exist to make the greatest ROI possible. 

International Cooperation

Addressing the challenges posed by less-regulated regions requires international cooperation. The West can collaborate on developing common AI standards and global development principles. But multilateral efforts will establish a level playing field and prevent a “race to the bottom”.

So how competitive intelligence and corporate intelligence can help?

Competitive and corporate intelligence are vital in helping manage risks associated with AI. They provide valuable insights and strategic information that can aid decision-making. And to enhance AI risk management and ensure successful AI implementation. Let’s explore how both forms of intelligence can help in the context of AI risk management:

Identifying Competitor Strategies

Competitive intelligence involves gathering and analysing data on competitors’ actions, products, and strategies. Understanding how competitors use AI help benchmark their AI initiatives. Again identifying potential risks and opportunities. Enabling you to learn from competitors’ successes and failures. Enhancing their AI risk mitigation strategies.

Corporate intelligence involves monitoring and analysing market trends and emerging technologies. Keeping abreast of AI developments allows anticipation of potential risks. Then take proactive measures based on pre-defined scenarios. Insight helps: 

  • Understand state-of-the-art AI solutions
  • Define the potential risks associated with new technologies
  • How competitors respond to these risks

Ethical Considerations and Regulatory Compliance 

AI systems often raise the ethical dilemmas discussed above. Competitive intelligence done correctly should provide valuable insights. So you understand how others in the industry address these challenges and comply with the regulations. Learning from their approaches assists in formulating ethical guidelines and risk management strategies.

Intellectual Property Protection

AI often relies on proprietary algorithms and innovative techniques. Competitive intelligence helps isolate potential threats to your IP. Such as patent infringement or reverse engineering attempts. By proactively protecting IP, you can safeguard their AI innovations from competitive risks.

Talent Acquisition and Retention

AI systems require skilled professionals to develop, deploy, and maintain. Intelligence identifies talent gaps and understands how competitors attract and retain AI talent. Allowing you to create compelling human resources strategies to mitigate talent shortages.

Vendor and Partner Risk Assessment

Many organisations rely on third-party vendors and partners for AI-related services and technologies. Competitive intelligence can assist in evaluating potential vendors’ reputations. Their financial stability and security practices. Helping reduce the risk of choosing unreliable or insecure partners for critical projects.

Customer Perception and Reputation Management

Corporate intelligence can gauge public sentiment and customer perception of AI implementations. And understand how customers perceive and receive AI allows you to address any negative sentiment. Again mitigating potential reputation risks proactively.

Cybersecurity and Data Privacy 

AI systems often handle vast amounts of sensitive data, making them susceptible to cyber threats and data breaches. Competitive intelligence provides insights into competitor cybersecurity practices. This can be used to benchmark and enhance your data protection measures.

Using Competitive Intelligence Thinking to Control Risks with AI Systems

By leveraging competitive intelligence, you can stay informed and make decisions. Manage risks associated with AI systems. And ensure that you are well-prepared to navigate the complexities of the AI landscape. At the same time, remain compliant with regulations, and gain a competitive advantage in the market. As AI regulation is a complex and evolving field, international cooperation is needed. To develop common AI standards to balance innovation and managing AI risks. So allowing AI technologies to be developed and deployed for the benefit of humanity. While still maintaining a competitive edge on the global stage. Effective AI risk management is crucial to succeed in their AI strategies. To foster innovation, gain customer trust, and achieve sustainable growth in the AI-driven world.

And remember these aren’t the droids you are looking for. Let’s talk…

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