Last year my curiosity deepened, I sought to gain a comprehensive understanding of Artificial Intelligence and Machine Learning. This led me to enroll in MIT’s leading business growth 6-month course, an opportunity for which I am immensely grateful to the Northwest and MIT teams. It’s fascinating and somewhat ironic that an AI-driven Facebook ad played a role in guiding me towards this course. The intricate workings of algorithms behind the scenes remain a marvel to me. I will admit that there was machine-human teaming involved here.

The field of artificial intelligence and machine learning has been actively explored by computer scientists for over 70 years. However, it’s the recent advancements, particularly in generative AI, thanks to incredible tools like #chatgpt and #bard, that has garnered significant attention. Other organizations, like SAP, have also made remarkable strides and developed multiple use cases. As technology continues to evolve, we can anticipate even more exciting advancements, and perhaps you might find yourself leading the way in this transformation.

In our daily lives, we often encounter complex challenges and problems that we may have given up on due to a lack of enthusiasm or a sense of inertia. But the advancements in technology now encourage us to view these business problems from a fresh perspective and push the boundaries with the aid of these technologies. One of the key enablers has been the democratization of computing power available through the cloud, along with the talented teams that make these innovations possible. Here are some of my learning on how to approach AI and ML project for your organization:

A Two-Pronged Approach:

Prong A: Leverage the remarkable out-of-the-box technologies available to further our business objectives. Embracing these cutting-edge solutions should be considered a baseline strategy.

Prong B: Identify the specific problems or opportunities at hand. The first step is to prioritize the business challenges we want to address. There is a possibility that an out of the box solution could be a potential. At times we may need to write additional logic or come up with a new end to end solution.

I believe we need to take both prongs to tap into the potential. #stayahead

The Power of Teaming and Collaboration

As we move towards building more intelligent systems, a critical factor is having the right mix of talent. This includes data scientists, business teams, tech experts, and consulting partners all working collaboratively to drive innovation. Creating a core team and fostering collaboration among them and the extended business teams is essential. This team should possess a deep understanding of the domain and business goals. Brainstorming with teams to identify areas for improvement and even revisiting fundamental statistical concepts and calculation is worthwhile during this time of transformation. This will help us get on the same page to develop our AI model and algorithms. Communication needs to be streamlined for a free exchange of ideas and information.

What is the business problem to be solved?

We must remain opportunistic, matching technologies to problems or directly addressing the challenges at hand. Leveraging analytics based on business metrics, such as P&L and balance sheet, and benchmarking can serve as an excellent starting point to identify improvement areas. Analyzing P&L with different criteria, like DSO, Brand P&L, and Plant P&L, can provide valuable insights. Some of the key use cases that come to mind are:

• Suggested corrective actions based on variances
• Cash application to improve the hit rate
• Inter-company matching to automatic reconciliation
• Forecasting and predicting based on historical and actual data

Structuring Business Transformation: A Holistic Approach

In order to fully harness the potential of AI and ML for enterprise transformation, it is essential to approach the process systematically. By breaking down the journey into distinct phases, we can ensure a smooth and successful integration of these cutting-edge technologies. Here’s how to navigate each stage:

Ideation and Team Formation

The initial phase of any transformational journey starts with ideation – identifying the business challenges that AI and ML can help address and formulating a clear vision for the project.

Assessment of Process, Systems, People, Data, and Governance

Before diving into the development phase, it is crucial to conduct a thorough assessment of various aspects of the organization. Understanding these elements helps in identifying potential roadblocks and aligning the project with the organization’s overall strategy.

Build

The “Build” phase involves developing and training the AI and ML models tailored to the specific business use cases identified earlier. This is where the team’s expertise comes into play, utilizing the latest tools and frameworks to create robust algorithms and models. The team collaborates closely during this stage to iterate and fine-tune the models until they meet the desired performance criteria.

Test

Testing is a crucial step in ensuring the reliability and accuracy of the developed AI and ML solutions. Rigorous testing is carried out, simulating real-world scenarios and various data inputs to validate the models’ performance. This phase helps identify any issues, biases, or potential areas of improvement that need to be addressed before deployment.

Deploy

Once the AI and ML models have been thoroughly tested and refined, it’s time to deploy them into the organization’s operational environment. The deployment process requires careful planning and coordination with relevant stakeholders to ensure a seamless integration with existing systems and workflows.

Run & Improvise

The real test of success begins during the “Run” phase, where the deployed AI and ML solutions are put to work in real-life situations. Continuous monitoring and feedback loops are essential to assess the models’ performance in real-time and to identify any new challenges that may arise during practical implementation.

By following this structured approach, organizations can maximize the potential of AI and ML, driving business transformation and reaping the benefits of these transformative technologies. As we embrace AI and ML, we have the opportunity to revolutionize our operations, solve complex problems, and create a brighter future for our enterprises. Let’s continue to innovate and lead the way in shaping a new era of business excellence with these powerful tools at our disposal.

Additional Resources:

SAP AI and ML community: https://community.sap.com/topics/machine-learning

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