How can you innovate decision-making with AI and ML?

By leveraging AI and ML technologies, decision-making can be innovated through automated data analysis, pattern recognition, predictive modeling, and real-time insights, enabling faster and more accurate decision-making processes.

AI and ML helps you to accelerate your decision making process, to focus on the most critical aspects. These can enable you to add value through the right levers, instead of burning your energy and time on matters that can be handled through AI and ML. It is like outsourcing your mental workload and freeing your intellectual capital for a better return on investment.

Artificial intelligence (AI) and machine learning (ML) offer promising ways to enhance decision-making. They excel at quickly analyzing vast data, providing insights and predictions that inform better choices. While AI can handle routine decisions, it also frees humans to tackle more complex tasks. However, this isn’t without challenges: data quality and ethical considerations like algorithmic bias are crucial. To leverage AI effectively, it’s important to blend human and machine insights, prioritize transparency, and allow for continuous learning. This hybrid approach promises more effective and responsible decision-making.

Machine Learning is in some sense more "classical" and often comparable to statistics, although it encompasses lots of other techniques. AI as we talk of it right now is the hot stuff where we walk closer to human or human-like capabilities of information processing, even surpassing humans in some fields.

Naively, the first thing that comes to mind is that AI might help with important, so macro-level decisions that execs need to make. But the real value of AI lies in the micro-level tactical and operational (automated) decisions: can we accept someone for this loan and against what terms, should we approve this claim, what is the best marketing offer or recommendation to show now, or what is the service problem a customer has and what could be the best solution. And within these processes and interactions you can find even more finegrained decisions: can we automatically handle this case or should a human look at it and if so what team, should we escalate or prioritize, what could be likely outcomes etc. Decisioning makes AI/ML actionable.

Don't use pure ML for decision making! Add some other fields into the mix, especially OR (operations research). Or add a human in the loop, the now famous RLHF (reinforcement learning from human feedback). ML is not robust to take important decisions. Use ML to steer some parts of the decision process but add some robustness with humans and/or OR (and possibly other fields).

AI and ML are invaluable for decision-making due to their tangible impact, as seen in my recent project. By leveraging ML and DL models to gather Amazon reviews and present insights via a dashboard and reports, we gained actionable insights. These insights empowered informed decisions, significantly enhancing product search and boosting sales by 25% within just one month. The ability to extract meaningful data and derive actionable insights promptly demonstrates the instrumental role of AI and ML in facilitating rapid, data-driven decision-making, directly impacting business success.

AI and ML can significantly innovate decision-making processes by providing data-driven insights, automation, and predictive capabilities. They can: 1. **Data Analysis and Insights:** AI and ML algorithms can process vast amounts of data quickly, identifying patterns and trends that might be missed by human analysis. This helps decision-makers make more informed choices. 2. **Predictive Analytics:** Machine learning models can predict future outcomes based on historical data. This is useful for forecasting market trends, customer behavior, and potential risks, aiding decision-makers in preparing for various scenarios. 3. **Personalization:** AI can analyze individual preferences and behaviors to offer personalized recommendations.

AI and ML are indeed game-changers for quick, data-driven decisions. They sift through complex data and offer precise recommendations. Yet, these tools are confined to data parameters and can't grasp subtleties beyond algorithmic limitations. The synergy of AI and human decision-making is where true innovation lies. AI offers the data foundation and speed, but it's the human mind that provides the critical overlay. Only this duality ensures decisions are not only efficient but also deeply considered. We should not treat AI tools as 'slaves' but as 'companions' or 'co-pilots' and make sure to get not only faster or automatic decisions but also better ones.

Never ever use ML only to take decisions. ML is not robust enough to take decisions. Beside you need a prescriptive approach to take decisions, not a predictive one.

Some of the challenges with using AI and ML for decision making are 1. Change in data distributions over time in the application making the deployed model inaccurate, 2. Bias in the developed model where the model does not work well for all locations, ethnicities, age groups etc leading to suboptimal experience for some of the users, 3. Lack of supervised training data in some use cases making it harder to build a high quality model, 4. Non deterministic behavior in the use case leading to challenges in building a model, for example predicting how people behave at in intersection in a self driving car use case.

Complexity, data quality, interpretability, bias, ethical considerations, and integration with existing systems ARE the challenges of using AI and ML for decision-making.

To add to @Jay Pillai contribution, the challenges of decision-making is that automated solutions can be (completely) wrong for different reasons. One such reasons is the alignment problem. You build a system thinking it would solve the problem in a certain way but actually that system is doing something completely different you did not expect. A kind of "oops" experiment.

Finding and replicating the processes that make us humans unique and orchestrating similar pathways into or with LLMs will be a super exciting challenge for the next years. It will need input from many people encompass many (human) lessons learned. If we strive for AI to solve our skill issue problems (which we have in many fields) new pathways to better governance, communication and education will emerge. As of now - we're still quite far from Skynet. Nevertheless, a good balance of safety/ alignment and explorative drive would be good ;)

By leveraging AI and ML technologies, decision-making can be innovated through automated data analysis, pattern recognition, predictive modeling, and real-time insights, enabling faster and more accurate decision-making processes.

To construct decision-making systems, you need to do some prescriptive analytics, not predictive analytics! Prescriptive analytics not only try to reply to the question "what is the optimal action?" (to full-fill a certain goal/objective) but comes also with lots of tools and even some fields like OR (operations research), CT (causal theory) and others! We should stop using only ML for decision making!

AI and ML can be utilized for decision-making by analyzing data patterns to predict outcomes, identifying trends, automating processes, and offering insights based on historical data. Techniques like classification, regression, clustering, and reinforcement learning help in making informed decisions by processing large datasets to find correlations and make predictions. Integrating AI models into decision-making processes can optimize strategies, enhance accuracy, and streamline operations across various industries.

Take online courses on AI and ML concepts. Read books and articles on applied AI. Experiment with open source ML tools. Research use cases in your industry. Consider in-person training. Stay current on new developments. Balance concepts with hands-on experience.

Add to your skills OR (operations research). This is really a field that helps you take decision and do some sensitivity analysis. This is extremely important for very important and critical decision making processes. ML gives lots of very useful information but is not very robust, even if you train your model and obtain incredible KPI. One huge problem with ML and relying on ML only to take decisions is that ML by itself will not tell you if a solution is good or not. Yes, you hope your solution is good, especially that you model is well trained but there is no way to assess the real quality of a solution within ML only.

This area is already covered by Decision Science, which combines Data Science / ML / AI with Behavioural Science to improve decision-making. I would recommend looking up Cassie Kozyrkov, the former Google Chief Decision Scientist, to learn more about the topic.

As mentioned by others, there are other fields out there to help make decisions. You can not make a decision only based on predictions. It is not enough. What if your predictions are wrong? What if you don't predict the right stuff? What if the data you based your predictions on are not very good? In reality, data is messy, not necessarily in abundance and often drifts away. Another thing to take into account is that your decisions will affect the whole process you are studying and this might be very difficult if not simply impossible to take into account with predictions only.

Ai is the latest tool among many that a leader has. Remember that AI can be short on wisdom and judgement. And, we are beginning to see at times that "garbage in equals garbage out."