What do you do if your strategic decision-making needs a boost from artificial intelligence?

AI is a legend, this is just LLM. We are very far from an AI and definitely will not get there very soon! Let's take it easy!

Next wave is here, its fun surfing once you learn it! So, first thing is to learn what does this mean to you and around, the stakeholders. Not on tech unless you are from technology team but what's happening, its impact! What new shining tools and disruptions happening? Having known what's happening and its impact for good or otherwise, I would focus at two distinct focal points. First, zoom in to what I and my team does and what I can do like financial audit may have very different meaning than that of talent acquisition. Second is zoom out at an enterprise level and look for opportunities keeping customer or potential customer in mind. Question, can pharma co have micro drug formulation that can create custom drug just in time?

One needs to understand that, like any other tool, AI is also a tool for performing a task. In my experience, utilizing AI in strategic decision-making necessitates understanding the development of a strategy first. For this, one needs to understand one's own business and the what, why, and how of the customers' needs. Now, see which areas like research or planning, trend study, or pattern recognition could be delegated to AI so that human error, delay, and computing limitations can be overcome and a better result delivered that would eventually help in crafting a better business strategy or making crucial decisions. AI is the tool in the process of making better decisions and not a process or entity itself that would do it.

To boost strategic decision-making, first, I'd identify areas where AI can add value, like data analysis or forecasting or advertisement or consumer behavior or day-to-day productivity improvement. Then, I'd explore AI solutions tailored to my needs, whether it's predictive analytics or machine learning algorithms. Collaborating with AI experts within or outside the organization could provide insights into the best tools and approaches. Implementing AI might involve training team members or integrating AI systems into existing processes. Regular evaluation and adjustment ensure AI continues to enhance decision-making. Ultimately, it's about leveraging AI's power to make your work smarter with data-driven choices.

Well AI will give generic results if provided with generic prompts. We should use AI to solving complex problems. When we have large amounts of data and responses from users.

Analyze the data sets that have added to the decision making of AI models. Also, due to ever evolving nature, be careful in sharing the set of data that you want the Ai model to operate upon. This is the most defining stage of any strategic plan which shapes the nature of all your next steps.

Artificial intelligence is highly effective at analyzing large datasets, a task that can be time-consuming and error-prone when done manually. By integrating AI into your strategic planning, you can swiftly sift through data to pinpoint key metrics crucial to your decision-making process. AI algorithms can identify correlations and trends that may not be immediately obvious, providing you with deeper insights into the market and your business operations. This level of analysis can help you make more informed, data-driven decisions, ultimately enhancing your strategic planning process.

📌 Learn how to leverage AI for data analysis to gain insights and inform strategic decision-making. Understand how AI algorithms can analyze large volumes of data quickly and accurately, identifying patterns, trends, and correlations that may not be apparent through traditional methods. 📌 Familiarize yourself with data preprocessing techniques, feature selection, and model evaluation to ensure the quality and reliability of your analysis results. By harnessing the power of AI for data analysis, you can make more informed and data-driven strategic decisions that drive business success.

This is one of my favorite capabilities of GPTs. Data analysis has always been the most tasking and perhaps the most challenging part of my job as former Decisioning PM and often relied on multiple analysts and data scientists to get the information that I needed. It took forever and multiple negotiations and debugging. GPTs have made this super easy today.

business decision has always been backward looking (reactive than that of predictive). Analytics changed bit of that by providing some analysis of the data but AI is changing the world. When you are building any strategy - feature, product or organisation level, One of the biggest challenge is predict the future or atleast guess it 80% right. AI and its subset ML (machine learning) is helping you there in enabling you to plan the moves with much more clarity and get competitive edge.

Effective use of artificial intelligence (AI) in strategic decision-making is a critical factor for future competitiveness. Decision-makers must skillfully balance human judgment with AI capabilities. The ability to delegate decisions to AI and navigate the interaction between humans and AI will be essential. By harnessing AI, organizations can streamline processes, enhance decision-making, and fuel innovation, ultimately gaining a competitive edge. Incorporating AI into decision-making involves a thoughtful blend of machine precision and human discernment, allowing for creative problem-solving and refined risk evaluation.

As far as the future is concerned, AI is not much better than humans. Both humans and computers try to "predict" the future based on deep analysis of past facts and patterns. AI can do it more deeply than humans, it can process more information and take into account more different factors. But what AI cannot (and it is not certain that it ever will be able to) is to take into account the fact that the future is not a continuation of the present or the past. So using AI as a co-pilot that can offer you more scenarios in which it will take more nuances into account is perfectly reasonable. But assuming that AI knows the future better than you do is dangerous. It doesn't know it either.

One of the most valuable aspects of AI in strategy is its predictive capabilities. Through machine learning, a subset of AI where algorithms improve over time through exposure to more data, you can forecast future trends and behaviors. This predictive power enables you to anticipate market changes, customer needs, and potential risks, allowing for proactive rather than reactive strategies. With AI's assistance, you can plan several moves ahead, ensuring that your business remains competitive and resilient in a rapidly changing environment.

Explore AI tools and techniques for generating predictive insights to support strategic decision-making. AI algorithms can analyze historical data to identify patterns and trends, allowing you to make predictions about future outcomes and scenarios. Familiarize yourself with predictive modeling techniques such as regression analysis, time series forecasting, and machine learning algorithms like decision trees and neural networks.

AI has a reach of many more reports that a person can access at any point of time. As a result, it works well as a great tool to test your outcomes. It is a good system to put in your suggestions and see if the suggestions fall on similar lines. A lot of times (if your research is good) you'll have a much more drilled down outcomes on your end.

Just as with insights about the future, relying on AI to offer better solutions is naive. But AI can offer MORE solutions or variants of solutions. And in that sense, asking for its opinion and listening to its answer may be well worthwhile. But I wouldn't recommend trusting AI's judgment in strategic matters.

Today, we have large amount of data to process. Even Analysis will give us multiple results to choose the best one. AI is master at providing a decision support.

I would lean strongly on AI supporting rather than guiding on any decisions within a business. Although AI is a great spotter of trends and patterns it is not aware of the context around that data that’s required to make good decisions. AI is impartial. It won’t choose information based on preference. AI is not unbiased. It will offer insights based on whatever data you intentionally—or unintentionally—seed it with. Proceed with caution. Be pessimistic. Check its ‘thinking’.

AI is a decision-support tool that enhances human judgment by providing data-driven scenarios and outcomes. It helps clarify the impact of each strategic choice, guiding you to select the best path for your business goals and risk tolerance. AI is invaluable for navigating complex decisions!

Artificial intelligence is a powerful piece of equipment to support the decision-making process. You can start your own implementation with a brief structure: - Identify the people (roles) you should listen to, - identify the reports that are the basis of your screening, - define any framework for your process (Porter's 5 forces or something else). Try to implement this process using GPTS or other similar tools. These steps will help you make your process clearer and more repeatable.

Integrating AI into strategic decision-making starts with pinpointing where it can significantly impact. Once you've identified these areas, ensure your data is clean and ready for AI analysis. Selecting the right tools and algorithms is crucial, as is training your team to use them effectively. Remember, the success of AI implementation hinges on a solid strategy and adapting based on AI insights. This approach not only boosts efficiency but also enhances decision-making precision.

To get the most out of AI, start by clearly defining your strategic goals and the specific decisions you want to improve. Then, identify the relevant data sources needed to train your AI models (internal data, external market data, etc.). Assess whether you need to upgrade your data infrastructure for better collection and management. Next, carefully select or build AI tools that align with your needs. It's wise to start with a pilot project, focusing on a well-defined area to test the technology. Finally, and crucially, emphasize human-AI collaboration. Remember, AI is a powerful tool, but human judgment and expertise are still essential for interpreting results and making the ultimate strategic calls.

Don't blindly rely on any AI tool to address all of your problems just because everyone is talking about it, though accept the changes, take a moment to reflect on your current situation. Yes, audit your own business, but preferably have someone else do it since you might be biased and someone with new eyes could spot things easily. If the results are the same as you noted years ago, just stop, identify the problems, learn from them, and put the fixes in place. We never stop learning, and this particular 'new machine age' helps us to do just that by allowing us to reflect, learn, fix things, and iterate by using additional artificial intelligence to potentiate humans.

A culture of continuous learning is driven by creating a team that is hungry for challenges and are willing to start an uncharted journey testing ideas and technology to drive experiments with innovations. But the most important factor is to design experiments that together increase the knowledge in the team and deliver visible results and benefits. Without such a concrete and measurable deadline, the most talented team will loose its focus.

What I have learned is not to start with AI (the how) at all. With your business goals and metrics targets in a good starting place, work backward from your customers through their whole journey with your brand. What initiatives create a simple, easy, personalized, and immersive experience for them with your brand. Then look at ways AI can aid in that experience. Efficiencies, more rapid delivery, more data insight to inform products and offerings, marketing etc. The “why” Once you feel you have it in hand the do the same for your employee experience. What can you do to enable productivity, creativity, and ingenuity supported by your goals and metrics. Now you iterate on how AI can power those initiatives

I’ve been in AI for 13 years and strategy is just about the last thing I could imagine using AI for. As humans helping AI’s understand strategy is what makes predictive pipelines effective. The primary tenant of the best AI’s are nonlinear “thinking” that definitionally we cannot comprehend. And a strategy built without fully understanding all the components is a bad strategy.

Go hybrid. Use wet-ware🧠 & software together which are still superior to either alone ( for now ). Start by frame-storming & / or brain-storming all the solutions you can with wet-ware FIRST. Why ? Because it's good for your 🧠 & will give you a broader range of solution paths once you bring in our (eventual) digital overlords. Doing the reverse will cause cognitive anchoring. That's when the ideas of others narrows your solution set prematurely. Try to think of more solutions than you think you can. Take a 20 minute break doing NOTHING, or pick it up tomorrow after a FULL nights sleep from which you wake WITHOUT AN ALARM (🧠science supportes). Rinse & repeat until you have NO new ideas. Now hit GPT or other AI for its help.

AI is a very broad subject, and various ideas and technologies are at different stages in the hype cycle. It is therefore critical to know how mature the topic is, run lots of ideas in parallel by adjacent teams of experts, and accept a healthy rate of failures. Not all experiments will succeed. And that's ok. This is about innovations that can provide a 10X benefit.