Veja como você pode empregar o aprendizado de máquina para a tomada de decisões estratégicas na inovação de negócios.

Before quality data, you need talented staff that can carry out ML analysis. This is currently not an easy task for smaller companies or government organizations. I recommend leveraging student groups and internships to exchange job experience at your organization for highly skilled (albeit temporary) talent.

To leverage machine learning for strategic decision-making in business innovation, it's crucial to focus on quality data and understanding ML basics. Quality data ensures accuracy in predictions, allowing businesses to anticipate future trends and customer behaviors effectively. By harnessing machine learning algorithms, companies can optimize operations, enhance customer experiences, and stay ahead of the competition in an increasingly data-driven market.

Creating and implementing ML models for business processes requires a lot of quality data and a keen understanding of KPIs being optimised. It is crucial to understand the complexity of the problem and try a couple of ways to approach it to better solve the problem.

Quality data is definitely a pre-requisite, as they say 'garbage in, garbage out'. Apart from this, it's imperative to ensure that the data available is comprehensive i.e. covers all aspects that are needed to establish a causation relationship between features and the target variable. Other factors responsible for achieving good results include pre-processing, feature-engineeeing, choice of model, and a feedback loop.

Employing machine learning for strategic decision-making in business innovation involves using historical and real-time data to train models that can provide insights and predictions to support strategic decisions. This can include: Predictive Analytics Customer Segmentation Anomaly Detection Optimization and Personalization By integrating machine learning into strategic decision-making processes, businesses can gain a competitive edge, drive innovation, and adapt to evolving market conditions more effectively.

Machine Learning mimics some aspects of human learning, particularly the ability to identify patterns and improve with more data. In the real world, there are data points that are not relevant for detailed categorization, such as variations of the same product family or company names from different countries. Therefore, by using Machine Learning algorithms, it can help the company to detect and extract the latest relevant information from internet articles based on historical data.

Trading models between organizations will unlock faster iteration on already pretrained models, to not start from scratch. Prove me wrong.

Like using any data and input for decision making, it still comes down to how you interpret it and the energy that is affecting the outcomes of those decisions that you make. You bring that, whether you are conscious of it or not. No machine or algorithm can do that for you. The energy you make decisions with infuses your outcomes - and your ability to interpret the data.

ML analyses require large quantities of data. Keep that in mind, even if you aren't conducting any ML research yet. The more data you can collect and keep, the better your advantage will be as ML proliferates and becomes more commonplace.

Make sure to utilize Analysts who understand statistics. ML isn't a plug-and-play process that spits out divinations; it's still very much scientific and requires skilled staff to run properly.

Formulating business innovation success as a machine learning problem can accelerate and improve the quality of the decision-making process. By identifying key features like type and size of client, product or service quality, pricing, response times, etc., and feeding large data sets from historical data, customer feedback and legal guidelines into the algorithm, predictions on future success of business innovation in certain target segments can be made in real-time. Once the foundational model has been created, it can be adjusted during the process. In my view, this approach reduces time-to-market and allows leadership to combine the analysis, considering other strategic implications on people, processes or society.

If you don't already, sit down with your executives and conduct a risk assessment at least once a year. Internal audit teams are great at this 😉. AICPA has some templates that can be used to get started if you haven't gone through this before. Mapping out your operations this way helps highlight where process automation could have the highest impact.

I think we need to ensure that algorithms are "ethical by design". We can prevent unfairness and discrimination through reviews by multifunctional and diverse teams before launching new machine learning models. An Ethical Commission and regular reviews in Risk Committees, L&D upskilling within the organisation, and regular external auditing and client conversations are important to ensure bias-free decision-making.

Don’t forget that everyone is not as knowledgeable about AI/ML as you. Make sure you understand the intent and desired outcome so you can advise and mold along the way. I’ve found that most start with “I want AI/ML so I can say I’m using it” rather than “I need to make this business process better and can AI/ML help”. Always understand the why!

Understanding limitations of AI in predictions. Learn when and for what to trust your current AI solutions and wherein you would need more data to make that happen. Just blindly trusting AI powered strategic decisions doesn't work. Using explainable AI methods is best for some critical scenarios.

Service industries such as healthcare, education, transportation, recreation, travel, and fashion that wish to implement machine learning to analyze and detect innovation opportunities in their product offerings, thereby maintaining or increasing their competitive advantage, and also being able to detect and react quickly to market changes, must ensure that the data they feed into the machine learning model is unbiased and a complete and reliable representation of the entire potential user population of these services. By doing so, we will ensure that the resulting actions and analyses are not subject to questionable biases that could impact the projected results.