Category Archives: Artificial Intelligence

Artificial Intelligence is transforming ERP solutions


If you don’t innovate fast, disrupt your industry, disrupt yourself, you will be left behind – John Chambers

Without a doubt, artificial intelligence (AI) will have a profound impact on the footprint of ERP solutions in the foreseeable future. AI will enable organizations to further optimize their operating model made up of business processes, software applications, governance structures and technology infrastructure.

To orchestrate this transformation, organizations must revamp their IT strategies and roadmaps and ingest the value of AI and ERP integration. These technologies go hand in hand because they cover the same spectrum.

AI enabled ERP solutions will by default impact the heart and soul of day-to-day operations. The mix of people, process and technology is going to change. AI solutions will take over routine tasks  in the end-to-end business process model that are currently performed by humans. This particular change is driven by an ongoing need to reduce operational cost. It is an irreversible process: “You either disrupt or get disrupted.”

At the same time AI can augment people’s capabilities and the effectiveness of the organization as a whole. This can be realized by shifting the focus to non-routine, analytical and creative tasks. Such a shift can only happen when AI and ERP are being addressed at the same time.

With the evolution of ERP solutions over the last 2 decades, organizations have gained access to a wealth of structured data. Nonetheless, they continue to struggle with transforming data in meaningful information, decisions and actions. The last 5 years, this situation got further challenged with the explosive growth of unstructured data that organizations capture without a clear approach on how to properly use it.

The ingredients to solve this situation are available and becoming more and more mature every day:

  • ERP solutions that enable organizations to run cost-efficient and effective operations
  • Big data solutions that can manage structured and unstructured data
  • Business analytics solutions that can provide information with a user-friendly experience
  • Cloud infrastructure that can make enterprise solutions widely accessible
  • Computing power that allow enterprise solutions to manage large data sets and complex algorithms
  • In-memory database technologies to explore large data sets in real-time
  • AI solutions that can learn, speak, read, respond, predict and execute transactions
  • Internet of Things (IoT) technology to capture real-time performance information

Customer service is an example of a functional area where AI technology is emerging. Very recently, KLM deployed an AI solution that has  the intent to improve the response to customer inquiries received through social media channels.

The deep learning and natural language processing solution for KLM was developed by DigitalGenius. It masters more than 60,000 customer questions and answers. That number keeps growing, because the ‘smart solution’ continues to learn and improve overtime. When a customer service agent receives a question, the solution gives a proposed answer. It’s up to the discretion of the service agent to follow up on the advice or not. The human interaction with the customer is still there, because customers still find that important. KLM is the first airline that deployed a customer service AI solution in the industry.

An AI enabled ERP solution for customer service integrates the customer interaction with the work order management process. The AI solution understands and learns from historical inspection reports and work orders. Depending on the nature of the customer inquiry it gives a proposed answer to the service agent. The AI solution assists with the planning and scheduling of the work by finding the earliest possible date to dispatch a service technician. That’s possible because it understands the required skill set and availability of required service parts.

This scenario is relevant for example for Cities. They render multiple services to commercial and residential customers. A customer may have more than one service issue at any point in time. An AI enabled ERP solution would in this case have the ability to provide insight into the status of all services by accessing and interpreting data from many systems. There may be many work orders in different stages of completion managed by different operational units. The AI enabled ERP solution would assist the agent with adequate communication to the customer, and effective coordination of the work with the departments.

Maintenance is another functional area where AI will be integrated with ERP solutions. A digital assistant  (DA) can help the service technician with the root cause analysis for corrective maintenance issues. The DA has deep understanding of the technical structure, performance and maintenance history of the troubled equipment. It also knows how  the equipment performs compared to similar units at other sites. The service technician is asking questions to the DA and gets evidence-based recommendations back. The DA obtained knowledge from the core ERP system and sources from the OEM. Inspection reports and work orders are important process documents to maintain for that purpose.

AI solutions are starting to appear in the area of predictive maintenance, which is different than preventative maintenance. The latter is triggered by time, events, or meter readings and results in planned, scheduled work. Predictive maintenance is much more based on real-time information about the actual performance of the equipment. Oftentimes, sensors and other Internet of Things (IoT) technologies play a critical role in capturing that information and relaying it back to the AI enabled ERP solution. Predictive maintenance has the objective to reduce maintenance cost. Where preventative maintenance indicates that a part has to be replaced, predictive maintenance may recommend to replace it later based on the actual condition.

SAP is gradually moving into the AI space. SAP has a cloud-based predictive maintenance solution that is based on prediction models and machine learning algorithms. The service technician has access to dashboards with key performance-indicators and ad-hoc reporting capabilities. It helps the service technician to understand the real-time performance of the technical infrastructure and intervene when required.

Earlier this month, SAP announced ‘SAP CoPilot, the chatbot for enterprise users. SAP CoPilot is based on natural language processing and machine learning technologies. The main purpose of SAP CoPilot is to explore data and business situations, and assist users with evidence-based decision making with the help of a DA. SAP CoPilot will gradually learn from empirical data, which means that its recommendations become more reliable overtime.

The potential of AI enabled ERP solutions is immense. There are a number of steps to take before you can embark on a project implementation. The formulation of a digital strategy is one of them. I will elaborate on these steps in the next article.

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The augmented project manager


You can dream, create, design, and build the most beautiful place in the world, but it requires people to make it a reality – Walt Disney

The evolution and functional application of artificial intelligence (AI) is on the rise in many different ways and forms. The technology is going to impact the role of the project manager substantially in the next decade. We are shifting towards an era where intelligent machines work on our behalf rather than work on our command.

The enthusiasm for the emerging technology is outranking the scepticism by far. A recent poll held during the Microsoft Emerging Tech Virtual Summit showed that 59% of the participants find AI supercool, 36%  take a more conservative position, and 5% is deeply worried. The outcome is encouraging and indicating that people’s readiness to adopt the technology in their life and work is bright.

Driving forces like growth of computing power, maturity of cloud technologies, and enhancements in algorithms boost the evolution of AI in the last 5 – 10 years. Tractica, a market intelligence company that focuses on human interaction with technology, predicts a steep curve in AI revenue. They forecast that annual worldwide AI revenue will grow from $643.7 million in 2016 to $36.8 billion by 2025.

AI as technology exists for decades, but we may not have realized that as much as we do today. Imagine that the postal industry world wide is processing billions of documents and parcels using AI since the seventies. They automatically sort items and trigger distribution events based on text recognition software.

It is widely expected that AI is going to replace routine tasks that are highly predictable. In this case, the spectrum of change holds all possible colours. It means that all jobs will be impacted, some more than others. Evidence-based decision making enabled by AI will become a standard for many jobs. It is already happening in the health care industry.

Non-routine tasks that have a high degree of uncertainty, require creativity and social interaction, will continue to be performed by humans. AI will struggle with recognizing patterns in the available data set and is therefore unable to understand and process a transaction. For these none-routine tasks, AI will enhance and scale the role of humans by acting as an advisor instead of a worker. The following diagram illustrates that.


The role of the project manager is going to change with the infusion of AI into the work environment. I wrote the white-paper  ‘The augmented project manager’ about it. The document speaks about artificial intelligence or cognitive computing in more detail and how it can be applied to the role of the project manager.

The value of AI for project management is immense. It is a matter of time that AI roots itself deeper and deeper in the role of the project manager. We know that AI as technology is making rapid progress. We also know that the application  is the hardest part. To channel and expedite a meaningful adoption in the role of the PM, I am working with business partners to found a Think Tank. I will keep you informed about the progress we make in this blog on

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Functional areas where machine learning is applied first


“The future of business process improvement is on making them intelligent. Machine learning is the driving force”

Machine learning is on a steep adoption curve and making its inroads in our daily lives and work. The application of the technology won’t be an issue at all. There’s an abundance of meaningful value propositions for many functional areas, business processes and roles across multiple industries.

Software vendors of enterprise business solutions are focusing their product development on machine learning and other related artificial intelligence technologies. CEO Bill McDermott of SAP said that intelligent applications will fundamentally change the way you do work in the enterprise in the next decade. He mentioned that we need the system to tell us what to do.

In many publications about machine learning we read about IBM Watson beating humans at Jeopardy, or Google’s AlphaGo beating a Go world champion. There are predictions, for example from Nick Bostrom,  that indicate that singularity, or the moment in time when a computer will be as intelligent as a human, is going to happen around 2040.

We also know that machine learning solutions work with structured and unstructured data. Data is growing at a fast pace and doubles every 2 years. 80% of the data is unstructured and 20% is structured. The technological advancements of the last 5 – 10 years removed barriers that artificial intelligence has been struggling with for many decades. Computing power being the most important one. As an indication, IBM Watson can read 200 million pages in 3 seconds and understand the content.

Machine learning solutions are coming our way. A fundamental principle is that they predict based on past behaviour.

Think about weather predictions. IBM acquired the digital assets of The Weather Company in 2016 and is leveraging IBM Watson platform to provide meaningful services to businesses and consumers. The weather notifications you can receive on your smart phone are coming from IBM Watson, a machine learning solution.

Machine learning solutions aren’t always perfect and there are ways to go. Facebook is using machine learning to recognize and understand photos, video and audio that are posted by its users. Content that is not meeting Facebook’s standards is removed. An example of this is the recent removal of a 1973 world press winning photo of the Vietnam war. After a public outcry Facebook reversed its decision.

A few weeks ago I had a personal experience with Facebook’s machine learning solutions and how it influenced a post. I was using an iPhone app to splice a few videos and thought it would be great to add audio to it. I selected the tune from the app library. When I posted the video, Facebook rejected it, because it believed that I did not have the rights to use the audio. Facebook’s interpretation was wrong, because the tune was general available to the app users. It’s an indication that machine learning solutions can learn on their own, but need human intervention to train them.

Machine learning is going to change the way we design and optimize business processes and functional roles. Automation will shift the role of humans more to exception based interactions and real-time, evidence-based decision making. The level of people, process, technology and data integration will further increase. Standardization of end-to-end processes in the supply chain will further manifest.

My expectation is that the software vendors of enterprise business solutions like SAP, Oracle and Salesforce will put their focus on functional areas where there is a high volume of routine transactions first. Think about the order-to-cash process where recurring orders flow through the order entry, fulfillment and delivery processes with limited human intervention. Think about the customer service process where the scheduling of work orders is further automated. Through internet of things technologies, real-time data becomes available that enable machine learning solutions to schedule service orders at the right time with the right spare parts ordered and skilled technician assigned.

There will be niche solutions too where machine learning is augmenting the human capability in a specific area. Think about travel and expense management solutions, where the processing of entries is for the greater part done without human intervention. Another example is recruitment. Machine learning solutions will take over the steps of identifying and screening candidates. Recruiters will receive a short list of qualified candidates and can focus more on the softer aspects that do require human interaction, for example determining if the candidate is a good fit with the organization.

Interaction with customers through call centres and other channels like email, apps or internet is another example. Machine learning solutions can understand text and speech and process simple transactions. Audible from Amazon does that to process refunds for audio books that the customer does not like.

Machine learning and other artificial intelligence solutions are at the top of Gartner’s Hype Cycle for Emerging Technologies, 2016. The evolution of the technology in the next decades will be fascinating, because it is coming so close to our existence as human beings. The potential to apply it in a meaningful way to our live and work will be enormous.

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