Here's how you can mitigate the risks of excluding strategic thinking in AI decision-making.

Define Goals 🎯 Strategic goal definition is paramount for effective AI decision-making, aligning processes with long-term objectives ✔. Would you agree that setting clear goals is vital for ensuring AI systems contribute to long-term success?🤔 Innovate with dynamic goal-setting mechanisms and cross-functional teams 📈.

Defining goals is crucial before deploying AI in decision-making processes. Essentially, it would help if you established clear strategic goals to ensure the AI aligns with your long-term objectives. If you skip this step, the AI might focus on short-term gains or metrics that don't matter to your overall strategy. By setting these goals, you provide a framework that guides the AI, helping it make decisions that contribute effectively to your overarching ambitions.

As a strategy expert in digitalization and AI, I emphasize the importance of clearly defined goals and visions that must be closely linked to corporate objectives. This is crucial for the successful deployment of AI. AI can easily optimize short-term or irrelevant metrics without clear directives, jeopardizing long-term business goals. By setting precise goals, AI decision-making is guided in the right direction, effectively supporting the company's overarching strategy.

‣ Ensure goals are specific, measurable, achievable, relevant, & time-bound (SMART) ‣ Align AI goals with overall business strategy & KPI's ‣ Involve key stakeholders in goal-setting to get buy-in & diverse perspectives ‣ Prioritize goals based on potential impact &feasibility ‣ Build in flexibility to adjust goals as business needs or external factors change ‣ Establish clear metrics to track progress &success ‣ Regularly review & validate that AI outputs are meeting intended strategic objectives ‣ Set ethical boundaries & consider potential long-term consequences of AI decisions ‣ Ensure human oversight in goal-setting & decision-making processes ‣ Regularly assess & adjust goals based on AI performance & changing business dynamics

I propose to my clients to + Focus in Interpersonal Skills, to enable effective communication and teamwork between human-AI + Employees need industry-specific knowledge to make informed decisions when using AI + Skills: provide training programs to enhance skills for working with AI + Share Best Practices + Regularly update employees on AI trends and encourage continuous learning

➡️Understanding how AI makes decisions is key to using it wisely and ethically. It’s not just about the data or algorithms—it’s about knowing the whole process to ensure transparency, especially in critical areas like healthcare and finance. ➡️Recognizing that AI might reflect biases in the data it's trained on helps us make adjustments to keep decisions fair and objective. ➡️As AI models get more complex, we need to tackle the 'black box' challenge to keep AI's workings clear and under control. Additionally, understanding AI helps in making sure it sticks to ethical norms and adapts well over time, enhancing how it integrates with human decision-making.

Understand the data that's being used to train the model, how it's been gathered, what it represents, and then you should have better insight into the process of strategic thought included.

Understanding that we typically deal with narrow AI, which operates through specifically programmed algorithms and defined rules that guide its analysis and decision-making, is crucial. Unlike humans, this type of AI does not think autonomously; it recognizes patterns based on its programming. Understanding this is important to identify where strategic oversight is needed. This knowledge ensures that AI decisions align with the broader goals of your company and do not just chase short-term metrics. Thus, you can effectively utilize AI while minimizing risks without involving general AI's complex yet unrealized technology (GenAI).

‣ Ensure data used to train AI is accurate, unbiased, and representative ‣ Provide strategic framing and guardrails to guide AI in the right direction ‣ Use AI insights to inform decisions, not make them outright ‣ Regularly audit AI models for drift, bias, or misalignment with goals ‣ Develop AI literacy among diverse leadership teams to effectively oversee AI ‣ Explore AI's potential to identify innovative opportunities humans might miss ‣ Emphasize the importance of strategic human-AI collaboration

Monitor Decisions 📊 Regular monitoring of AI decisions is vital for maintaining strategic alignment and preventing deviations from objectives🤳. Would you agree that monitoring decisions ensures AI remains aligned with strategic goals?🤔 Innovate with AI ethics committees and real-time decision dashboards.✌

To mitigate risks of excluding strategic thinking in AI decision-making, implement continuous monitoring of decisions. In autonomous vehicle navigation, real-time tracking of route choices and adjustments enables analysis of immediate safety concerns alongside long-term traffic flow and urban planning considerations. By integrating monitoring systems, deviations from strategic objectives can be promptly identified and corrected, ensuring AI decisions align with broader goals. This approach fosters adaptability and foresight, enabling AI systems to navigate complexities effectively while minimizing the risk of overlooking strategic implications.

To effectively manage AI and ensure it aligns with your strategic goals, taking proactive steps is essential. Regularly monitor AI systems' outcomes and decision-making processes to understand how decisions are made. This insight allows you to detect any deviations from expected behaviors early on. Should AI start straying from your strategic objectives, intervene promptly to correct its course. Maintain rigorous oversight to ensure that AI continues functioning as a supportive tool under human control, not as a replacement for strategic decision-making. Always check that AI’s actions are consistent with the company's long-term goals, adjusting its parameters and guidelines as necessary.

‣ Establish clear KPIs and metrics to track AI decision outcomes against strategic objectives  ‣ Implement regular audits of AI decision processes and results to identify potential misalignment early  ‣ Assign accountability for monitoring AI to diverse roles/teams for consistent, well-rounded oversight  ‣ Develop playbooks for human intervention when AI decisions deviate from strategy  ‣ Continuously reassess and adapt monitoring approach as AI capabilities and strategic priorities evolve  ‣ Ensure transparency and explainability in AI decision-making to build trust and facilitate effective monitoring

Think of AI in decision-making like a talented but young football player on your team. Just as you would regularly monitor the player’s performance in matches and training to ensure they align with the team’s tactics and goals, you need to keep a close eye on the decisions made by AI. This involves not just looking at the results but also understanding how those decisions are being made. If the AI begins to "play out of position" or deviates from your strategic objectives, you're there to coach it back on track promptly. Regular monitoring ensures that AI remains a valuable player in your team's strategy, enhancing your efforts rather than replacing the need for human insight and leadership.

When doing POC for an AI-powered resource allocation system, our initial algorithm focused solely on maximizing efficiency. However, we quickly realised that it failed to consider the long-term strategic goals of the organization. By adjusting the algorithm to include parameters such as skill development, team morale, and alignment with company objectives, we were able to create a more balanced and effective solution. It's essential to remember that AI is a tool, and its decision-making capabilities are only as good as the data and logic we provide. By actively involving strategic thinkers in the development process and continuously refining algorithms, we can harness the power of AI while mitigating the risks of short-sightedness.

Adjusting and iterating on your algorithms based on feedback quickly will be the difference between handling a risk quick enough before it becomes a major problem. This is where an agile methodology comes into place with plenty of testing, demoing, feedback cycle to iterate.

there are several methods we can use to adjust the algorithms: 1-Regularization techniques: Implement regularization techniques, such as L1 or L2 regularization, to prevent overfitting and ensure the AI's decisions are aligned with strategic objectives. 2-Ensemble methods: Use ensemble methods like bagging, boosting, or stacking to combine multiple models and improve their collective performance. 3-Human-in-the-loop: Implement human oversight and review of AI decisions to correct errors and align them with strategic objectives. 4-Human-AI collaboration: Design collaborative systems where humans and AI work together to make decisions that align with strategic objectives.

Maybe an unpopular opinion; but when AI decisions don't align with the norm - investigate them with eyes wide open. In more than one occasion, I've seen AI present a decision or new objective that seems to be out of alignment with business goals at first pass. Investigating this result, rather than changing the algorithm to "conform" is a prudent course of action. AI can find trends that are deeply hidden in the data/facts that you analyze. When you see a substantially different response, don't dismiss it, or try to correct the algorithm. Dig into it with an open mind and you may just find a perspective that can only be attained by a "disinterested third party", which in this case, is the AI. Think hard before knee jerking and adjusting.

Encouraging a collaborative approach between AI and human expertise is essential for optimizing decision-making processes and leveraging the strengths of both domains. AI excels at processing vast amounts of data and identifying patterns, while humans bring contextual understanding, intuition, and nuanced reasoning to the table. By combining these complementary strengths, we create a powerful decision-making framework that incorporates strategic thinking and holistic perspectives.

Collaborative Approach Multidisciplinary Teams: Assemble multidisciplinary teams comprising AI experts, domain specialists, ethicists, and decision-makers to ensure a holistic and diverse perspective in AI decision-making processes. Stakeholder Engagement: Actively engage stakeholders, including end-users, communities, and regulatory bodies, to gather feedback, address concerns, and foster trust in the AI system's decision-making capabilities. Knowledge Sharing: Foster knowledge sharing and collaboration within the organization and with external partners, enabling the dissemination of best practices, lessons learned, and emerging techniques for responsible AI decision-making.

A practical example of a collaborative approach is in diagnostic healthcare, where AI algorithms assist in imaging analysis but the final diagnostic decisions are validated by medical professionals. This model utilizes AI for its computational power and speed, while leveraging human expertise for critical thinking and ethical considerations, ensuring that healthcare decisions are both accurate and humane.

Implementing a continuous learning process for both AI and staff, where both evolve and adapt to each other, fosters an environment where strategic thinking is constantly integrated into decision making.

Establish a dynamic feedback loop between your AI systems and strategic decision-making processes. Continuous learning isn't just about improving AI algorithms; it's about evolving your strategic approaches in tandem. By analyzing AI outputs and their impact, and revisiting strategic goals regularly, your team ensures that AI remains a robust tool tailored to long-term objectives. Encourage cross-disciplinary teams to engage with AI insights, fostering a culture where technology and strategy inform and enhance one another, driving innovation and mitigating risks.

At the pace that things are changing now a commitment to regular AI training and certifications, along with updates every so often, is something that absolutely has to be done. You can't just get a certification now, and then expect to be good in 5 years.

Keep learning and adapting. As AI gets smarter with more data, your team should too. This keeps everyone on their toes and integrates fresh insights into everyday strategies.

Promote a Culture of Constant Learning and Adaptation: Promote an environment where AI teams and the entire company are always learning and adapting. Promote experimentation, innovation, and information exchange to gradually improve AI capabilities and strategic thinking.

To mitigate the risks of excluding strategic thinking in AI decision-making, it's crucial to establish continuous monitoring mechanisms to ensure your organization complies with AI ethics and regulations. Even in jurisdictions without specific AI regulations, existing laws still apply. Keep a close eye on regulations like data privacy, intellectual property, employment laws, and more. This proactive approach helps safeguard against legal and ethical pitfalls while fostering responsible AI development and deployment.

The greatest risk of excluding strategic thinking in AI decision-making is not that the AI will make incorrect choices, but that it will make perfectly logical choices based on flawed (human?) input. AI without strategic thinking becomes a tool that accelerates us towards the wrong destination. It's not about preventing AI “sentience”. It's about fostering critical thinking about the human side of the equation.

With all the hype surrounding AI, it's often difficult to know what is what. When should one step up to the curtain, pull back and see just who is pulling all the levers back there? AI is a game changer, that there is no doubt. Is it all about ChatGPT, heck no. Mitigation is about starting small. What offers the greatest ROI for the least risk. You could start with automating a manual process using RPA, freeing up X colleagues. Instant win, with little risk and low cost. AI needs great data to work well. Start by assessing your data quality and trustworthiness. If good, explore machine learning (ML). E.g., What customer group has the highest LTV. Low effort, low risk, and high potential ROI

Use explainable AI techniques to make the decision-making process transparent and understandable. This can help you identify any potential biases or errors in the AI's decision-making, and correct them as needed.