What do you do if AI is not enhancing your decision-making processes as a leader?

As a first step, learn about the different tools available in the market for your specific needs. How to do that ? Use search engines or AI platform integrators to learn about the different available tools. Then start exploring these based on your niche.

?? Functionality:?Does the AI tool do what you need it to do, and how well does it do it? (e.g., Is an image recognition tool accurate in identifying objects?) ??Accuracy & Bias:?How reliable are the AI's outputs? Is there potential for bias in the tool's results? (e.g., Does an AI resume screener fairly assess candidates from all backgrounds?) ??Explainability:?Can you understand how the AI tool arrives at its conclusions? Transparency is important for trust. (e.g., Can a spam filter explain why it flagged an email?) ??Privacy & Security:?How does the AI tool handle your data? Is your data secure and used responsibly? ??Ethical Considerations:?Does the AI tool raise any ethical concerns, such as fairness or potential for misuse?

Before subscribing to an AI tool, make sure you know your needs. Then, you are able to compare the tool with your needs. The biggest mistake is to buy a tool without assessing its abilities to fulfill your needs

If AI is not enhancing your decision-making processes as a leader, there are several steps you can take to address the issue: 1. Identify the Problem Areas 2. Assess AI Implementation 3. Review Data Quality and Availability 4. Evaluate Model Performance 5. Seek Feedback from Users 6. Invest in Training and Education 7. Collaborate with AI Experts 8. Iterate and Improve By taking these steps, you can address challenges and enhance the effectiveness of AI in your decision-making processes as a leader

1. Evaluate the suitability of current AI tools for your needs. 2. Consider if a mismatch in tool selection is hindering decision-making. 3. Ensure AI solutions are tailored to your industry's specific requirements. 4. Assess if the tools are designed for the types of decisions you face. 5. Investigate alternatives that may offer a better alignment with objectives. 6. Remember, popularity or complexity doesn't equate to the best fit. 7. Seek AI tools that integrate seamlessly into your decision-making process. 8. Compare features, usability, and outcomes of various AI solutions. 9. Consult with industry peers or experts for tool recommendations. 10. Prioritize AI tools that enhance, rather than complicate, decision-making.

Ensure the AI system is fed with high-quality, relevant data, Inaccuracies or biases in the input can lead to ineffective decision-making & irregularities

-Ensure the AI system is fed with high-quality, relevant data. Inaccuracies or biases in the input can lead to ineffective decision-making. -Sometimes, the issue lies in the algorithm's design or its alignment with your decision-making goals. It may be time to tweak or completely overhaul it. -Implement a loop of continuous feedback and learning for the AI system. This helps it to evolve and adapt over time, improving its decision-support capabilities.

An AI system is as good as its data source. Fine tune and optimize the data source, and make sure that you address all applicable use cases and relevant variables. The output will be as robust as the quality of your data source for any AI system.

AI does not work well if the data fed into the system is of poor quality. Garbage in garbage out To ensure AI results, improve governance and quality of your data. Make sure you understand how your AI system works to select relevant data fed into the system

If AI is not enhancing your decision-making processes as a leader, it's essential to reassess your approach and identify potential reasons for the lack of effectiveness by considering the following: Reassess Data Quality and Availability: Review the quality, relevance, and availability of data being used to train and deploy AI models. Ensure that the data sources are reliable, diverse, and representative of the decision-making context to improve the accuracy and reliability of AI-driven insights.

Education and continuous learning is important when it comes to mastering AI platforms or systems. For mass used platforms, you are likely to find mentorship programs and training cohorts. For niche and customized systems, consider training you and your team thoroughly by a certified instructor and/or user.

I believe ongoing education and training in AI technology are important. Providing your team with the necessary skills to use AI effectively can significantly impact decision-making. Empower your team with AI and data science skills to make the most of available AI tools. It's essential to invest in continuous learning and development in AI to stay competitive in today's fast-paced technological world. By promoting a culture of learning and growth within your organization, you can keep your team ahead in AI innovation. Let's seize the opportunities that AI technology offers and aim for excellence in our knowledge and skills.

Simulated intelligence is a thriving area. As it occurs with each roaring area, there is a great deal of dread of passing up a major opportunity working out among partners. Thus the vast majority simply make a plunge without a legitimate methodology set up. Furthermore, that is quite possibly of the most terrible mix-up you can settle on while executing man-made intelligence into the choice making stage. Try not to involve simulated intelligence for it. Have a reasonable objective and methodology set up prior to utilizing it

Alignment is key. It's important to evaluate the current AI tools and processes you have in place - more is not always better. Identify where the gaps are and look for areas of improvement. Invest in training to better understand how to leverage AI for decision-making. Collaborate with AI experts to optimize your strategies. There is a lot of information for specific use cases and tutorials available online.

??Clear Vision & Goals: Everyone understands the organization's purpose and long-term goals. (e.g., Become the leader in sustainable energy solutions). ??Aligned Activities: All departments and teams work towards the same strategic objectives. (e.g., Marketing promotes eco-friendly products, R&D develops new green technologies). ??Resource Allocation: Resources are directed to support strategic priorities. (e.g., Invest in renewable energy projects, not fossil fuel exploration). Performance Measurement: Track progress towards goals with clear metrics. ??Culture of Communication: Open communication across teams ensures everyone is informed and aligned. (e.g., Regular meetings to discuss progress and challenges).

Is your business truly AI-ready? It’s not just about technology, the most powerful LLM and data quality but, crucially, about having a solid strategy that aligns to your business objectives. What’s our goal? Growing our customer base, boosting revenue, or perhaps enhancing customer satisfaction? Without a shared vision, AI projects risk veering off course. I always suggest to begin by applying AI to improve internal operations and save costs. As your team gains confidence and expertise, you can gradually shift focus to customer-facing efficiencies. Only then, with a firm foundation, should you aim AI at revenue-generating opportunities. Building muscle memory, and the right operating model as you progress, all whilst delivering value.

AI is a booming sector. As it happens with every booming sector, there is a lot of fear of missing out playing out among stakeholders. As a result most people just dive head first without a proper strategy in place. And that's one of the worst mistakes you can make while implementing AI into the decision making phase. Don't use AI for the sake of it. Have a clear goal and strategy in place before leveraging it.

??Circle of Influence: Feedback loops occur when a system's output circles back and influences its input. (e.g., Studying for a test improves your test score, motivating you to study more). Two Flavors: Feedback loops can be positive, amplifying change (e.g., runaway inflation), or negative, counteracting change to maintain stability (e.g., a thermostat regulating room temperature).

Feedback loops are extremely important when it comes to using AI platforms or systems. You need to constantly test the outputs, tweak the data set, include more variables, optimize the input and repeat. In the feedback loop, you will also discover scenarios which you might not have considered while feeding the system the data set. Repeat till you reach acceptable outputs for a wide range of scenarios.

Establishing robust feedback loops is indeed crucial for AI efficacy. By consistently monitoring outcomes and providing constructive feedback, AI systems can evolve and enhance decision-making capabilities. Implementing systematic feedback mechanisms is the cornerstone of AI optimization, transforming underperforming AI into a valuable decision-making tool.

AI largely works on past patterns. It can facilitate decision making around known problems, where AI performance is limited by data quality and the model adaptation to business metrics. A business leader faces new problems everyday. For AI to become truly autonomous, it would take many more breakthrough moments such as "Transformer model of 2017" that changed the face of Neural networks to the GenAI models. Until that happens, AI is more like "decision-assist" than "decision-making"

Just be open to exploring avenues that potentially, aren't as grand or opulent as AI. Whilst AI is very much in vogue at present, you should always weigh up the cost and operational complexities of a solution and bound this to the value/opportunity the solution will enable. In most cases, if you think creatively and are diligent about your technology choices you will likely find that you can build an equally elegant solution that is merely rules based, with some tailored operational reporting and lightweight automation. Not everything requires an LLM or full blown AI.

Here's a hot take: using AI tools won't automatically improve your leadership or decision-making prowess. Here's what I mean: At the end of the day, AI doesn't think like a human - not at the moment, anyway. It is still essentially a tool that is designed to assist you with various tasks. So, it's like Google in a way - it can simplify certain tasks, reduce research time, help you proofread what you wrote, etc. However, it's by no means a replacement for your judgment calls or leadership skills. Does that mean you shouldn't use them? Of course not. Learn to use them, but don't develop over-reliance on them. As a leader and a decision maker, it's ultimately up to you - not on any particular tool you use, including AI.

Always go back to what problem you are trying to solve and make sure it is woeth solving with AI! Not everything is better with AI. 1) Clarify Objectives: Ensure that the goals you're trying to achieve with AI are clearly defined. Sometimes, AI might not seem helpful because the objectives it's being used for are not aligned with your real needs or challenges. 2) Assess the Data: AI's effectiveness is largely dependent on the quality and relevance of the data it's trained on. 3) Review the Technology: It's possible that the specific AI tools or models you're using are not the best fit for your needs. 4) Ensure that you and your team have the necessary understanding of how to use AI tools effectively

AI in itself won't help make decisions. Applying AI to business use cases need to be looked upon holistically. Few considerations - 1. What is the business use case? Does it really need to be solved with AI? This is the fundamental question. Avoid the trap of a solution searching for a problem. 2. Let's assume AI is the right approach here. Does your team have right skill set to leverage AI? 3. What about data and infra readiness? Does your organization have right set-up in place to make best use of AI for decision making. 4. Often ignored, evaluate if team culture, organization change management facilitate the adoption of AI for decision making. 5. Lastly, timing of AI. Do you have a clear AI strategy - "Why now" or "Why not now".

Distinction between AI and conventional software is crucial. Unlike traditional programs governed by predefined rules, AI requires a different approach—it needs to be trained. When faced with the challenge of AI failing to enhance decision-making, Consulting AI specialists becomes not only beneficial but imperative. AI's efficacy lies in its ability to learn and adapt to specific contexts, unlike off-the-shelf solutions. Collaboration with experts is a strategic move, as they possess the expertise to craft bespoke AI models finely tuned to your organization's needs. By embracing this collaborative approach, businesses can unlock AI's transformative potential for businesses,

??? Define the problem: Know what you're solving (e.g., pick the right marketing campaign). ? Gather info: Use data & expertise (e.g., customer demographics & past campaigns). ? Explore options: Brainstorm all possibilities (e.g., channels, messages, budgets). ? Weigh risks & rewards: Consider potential outcomes (e.g., brand impact, sales, budget). ? Decide & act: Choose & take action (e.g., launch social media campaign).