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The Impact of Artificial Intelligence in Modern ERP

Eric Shuss

Eric Shuss

The Microsoft Dynamics AX/D365 Support Team at Avantiico is focused on solving our client’s problems, from daily issues to large and more complex problems.

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The Impact of Artificial Intelligence in Modern ERP

While AI is one of the hottest fields in the technology world today, it has been slow to work its way in to the modern ERP (Enterprise Resource Planning) landscape until just recently.  Many are still having difficulty understanding what the truly implementable possibilities available today are versus the hype touted by companies across the globe.  One recent study revealed that over 40% of “AI companies” don’t use any AI at all (1).   So to help you separate the wheat from the chaff, on this blog, I will be discussing where AI has come from, what works today and what are the near-term solutions that can make a significant impact for you, your company and your industry.

So, for those not familiar with me or Avantiico, a bit of background.  I have been actively working in the field of Artificial Intelligence for over 30 years.  I was driven by my love of computers and robots since I was a teenager and felt that since I didn’t get the natural version, artificial intelligence was a good substitute. I grew up in a family business that viewed computers as our competitive advantage so it was clear to me even then that every aspect of the business could benefit from good process and good systems. I spent most of my career implementing ERP systems including Manman, Baan, Oracle and the Microsoft family of systems from Axapta through its current incarnation, Dynamics 365 Finance and Operations. The other side of my career has been doing AI consulting and startups as well as being a board member of several AI companies. I have specialized in many aspects of AI including Chatbots and Natural Language Processing (NLP), computer vision, machine learning and AI for Good to figure out how we develop moral and ethical AI.  I have had the honor to work with some of the most amazing and innovative people in the field and continue to learn every single day. A few years ago, I stepped out the ERP industry to focus on AI exclusively only to find that my passion for large complex business systems would be my best and highest use, so I have joined Avantiico to help bring advanced AI to the Dynamics 365 ecosystem.

Avantiico is a Microsoft Gold Partner who specializes in strong application and technical expertise. When I was looking for the right company to join to bring AI to this market, Avantiico had the right combination of excellence and vision due to the amazing team that the founders had assembled. Many of the people that I respected most had joined the company back when it was called AXMentor.  They changed the name due to Microsoft rebranding the product from AX to Dynamics 365. Same company, just a new name for the company that was known for being the ones you called when an implementation had gone off the rails. When I reached out to Avantiico, I was excited to see that the top management and founders, Lauge and Henrik, also shared my belief that it was time for a focused initiative to bring AI to their customers. They saw that Microsoft was pouring enormous resources into AI across their product lines and that everything from Cortana to the HoloLens, Azure to Power BI, and now, across the Dynamics 365 product suite. They agreed that companies today must have an AI strategy that complements their ERP strategy to be successful.

A Few Definitions:

One definition of Artificial Intelligence is the ability for software to identify complex patterns and act upon that information. Another definition is a computer system able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. So, whether that is a system that looks at large amounts of traffic data to find better routes for navigation like Waze or Apple Maps, one that reads and listens customer service call data to create Natural Language chatbots to handle incoming customer calls and texts or advanced vision systems to spot product defects that can’t be seen by the human eye during production, AI can augment us with human-level or above abilities.

AI also has several other variations and definitions that are important to understand. Machine learning is a term to describe a subset of AI that analyzes a large amount of data to carry out a task, rather than traditional software defined by a programmer. In the past, Machine Learning required complex data preparation, data manipulation and manual articulation to be effective but this has recently evolved into deep learning systems that are capable of unsupervised learning from data that is unstructured or unlabeled. This has expanded the usefulness of machine learning from the research and academic worlds to have major opportunities for the Enterprise. Neural networks are the computational models of machine learning that are designed in a way that is similar to how neurons in our brains work.  They come in many flavors that are used based on the type of data and outcomes required. Some of the most common types of neural networks include Recurrent, Modular and Convolutional Neural Networks. You do not need to understand the various Machine Learning types to implement AI in the Enterprise as companies are building applications, including Microsoft, on these foundations with predefined data integrations and user-friendly interfaces. I will go into more detail on the various types of AI, including Narrow AI, Artificial General Intelligence (AGI) and even the future of ASI, Artificial Super Intelligence in future blogs.

The AI landscape in 2019 is dominated by the major players in technology realm including Facebook, Amazon, Microsoft and Alphabet/Google in the US and Baidu and Tencent in China.  These behemoths are sucking the oxygen and talent out of the rest of the world and consolidating their capabilities and power.  There are smaller AI start-ups, but they tend to be outspent or acquired before they can gain real traction. A small band of open source AI projects like SingularityNET, Fetch, Ocean Protocol and Seed are trying to navigate the waters and will be a topic for a future blog. 

Why Are Companies Using AI Today?

The same study found that 94% describe AI as an important tool to solving problems in their organization. The chart below details the strategic challenges that are facing both private and public sector organizations and it is easy to see how each of these are arenas being tackled by today’s top AI companies.

The study then showed that just 27% of respondents said that their organizations are using AI in key processes while an equal amount are not using it at all at this time. 46% have one or more AI pilot projects that are currently under way and it is only 6% that responded their organizations have no plans to include AI in their technological roadmap. The most used AI technologies include image analysis (35%), virtual assistants (31%), predictive analytics (29%), machine learning (28%) and natural language processing (26%).

In the upcoming blogs I will talk more about these technologies and how you can incorporate them into your ERP roadmap as well. For example, image analysis is one of my favorite AI technologies as I have worked with companies deploying it in many exciting ways from sentiment analysis to be able to read peoples macro and micro facial expressions and body language to understand complex emotional states and signals. This can be very useful when trying to have contextual conversations with people in sales and customer service environments. Besides extremely accurate facial recognition, we even can identify an individual just by their walking gait and posture and the military just announced a tech that can identify a person based upon the heat signature of their heartbeat using frickin’ lasers.

Cutting edge work is also being done in agriculture by mounting cameras on tractors and combines to be able to identify invasive species and plant disease in real-time so pesticide spraying can be extremely well targeted to reduce toxic chemicals in our food supply. We are in talks to start some new projects around real-time manufacturing quality inspections using Microsoft’s Dynamics 365 Production and Quality modules using image analysis, machine learning and predictive analytics this fall. 

We are also working on bringing together AI and AR/VR technologies in the retail space to give immersive and emotional connected sales environments to help brick and mortar retailers entice customers in to getting out of the house and back in to the stores.  Companies from such diverse industries such as homebuilders, autos and RVs, fashion and jewelry are all moving quickly to stake out the competitive advantages needed to stay ahead. I am interested in what you have seen in this arena and what has worked and what is still a work in progress.

So, what has been the impact of AI in your company’s modern ERP journey? Is there a roadmap to guide the way? What do you see as the biggest challenges to implementing new technologies and what are the risks of moving too fast or not moving fast enough? Can you imagine how AI can help you and your colleagues do your job? How can AI help you and your family manage the complex world we live in and remain a positive and ethical force? These are some of the questions I want to discuss with you so I can better understand how we at Avantiico can help. 

Many fear the coming AI revolution but I believe we can make enormous progress if we have transparency and open discussions about the risks and rewards. Thank you for taking the time to read this and I hope this introduction has been helpful. Please let me know what you think I got right and what I got wrong. Also let me know what questions you have and any insights you have learned from your own AI journey. I look forward to continuing the discussion with you all.

References  1 – https://www.technologyreview.com/f/613078/about-40-of-europes-ai-companies-dont-actually-use-any-ai-at-all/

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