Artificial Intelligence (AI) has recently become the current commercial buzzword. AI offers tremendous promise for analyzing data and extracting relevant insights that may be used to make critical strategic business choices. Businesses all around the globe are searching for methods to make use of the advantages of sophisticated technology to help them expand. Artificial Intelligence has emerged as a breakthrough technical advancement that has altered the whole commercial environment, thanks to its extraordinary capacity to aid organizations in making critical decisions. AI and its integration with various applications are assisting businesses in generating massive profits.

Artificial intelligence (AI) has permeated enterprise operations to the point that it now determines an organization's success, including in the area of project management. In a report, Project Management Institute (PMI) examines how six AI technologies are affecting today's project managers and will affect project management operations in the future. PMI's AI Innovators: Cracking the Code on Project Performance (2019) found that in the next three years, project professionals expect overall AI usage to jump from 23% to 37% and the majority of respondents (81%) said their organizations are currently being affected by AI technologies. SEE: The ethical challenges of AI: A leader's guide (free PDF) (TechRepublic) "Project leaders are in the earliest stages of adopting AI to streamline--and improve--project work. AI technologies are already contributing to higher productivity and better quality," said Mark Broome, chief data officer at PMI. "For example, technology is decreasing the amount of time project managers need to spend on activities like monitoring progress and managing documentation--they can rely on AI for these more administrative tasks. The time saved can then be repurposed to more strategic and creative tasks and planning."

A few days ago my fellow Red Hatters Mario Fusco, Matteo Mortari, Mark Proctor, Donato Marrazzo and myself (Edson Tirelli) had the opportunity to attend Decision Camp 2019. Following the tradition from previous years, this is a conference focused on Decision Management and related topics, with an emphasis on practitioners, vendors and users of the technology. In other words, a 3-day conference that packs a lot of content, mostly technical and strategic. This year in particular the agenda was packed full of interesting and relevant topics, ranging from human centric topics (like coordination of collaborative decisions), to compelling use cases (like airport gate scheduling), to glimpses of what is coming on the DMN standard (like temporal reasoning and discussions about DMN 2.0). You can find a good review of all the presentations on Sandy Kemsley's blog.

A great article appeared in Information Age recently based on an interview with Tom Davenport If you want to see the benefits of AI, forget moonshots and think boring. In it, Tom argues that "if enterprises ever want to see the benefits of AI, they must embrace the mundane". This is particularly true for companies that aren't tech startups. As Tom says, "moonshots are possible if you're a tech giant and you have billions of dollars to spend on experimenting" but what if you can't "pivot"? What if you have to keep doing what you have always done – manufacturing things, selling insurance, providing banking services? Big, boring companies need to take a different approach.

Intelligent Business Process Management Suites (iBPMS) is defined by Gartner as having capabilities such as validation (process simulation, including "what if") and verification (logical compliance), optimization, and the ability to gain insight into process performance have been included in many BPMS offerings for several years. The iBPMS market today is the natural evolution of the earlier BPMS market, except with added features (above) making it possible for greater intelligence within business processes. In light of this, we recently spoke with Miguel Valdes Faura, Founder and CEO of Bonitasoft, about iBPM software to help provide a more depth definition. He's also provided us with some insight into iBPM solutions, where Robotic Process Automation (RPA) comes into play, and his views on the marketplace. MF: There are a lot of AI related technologies that are complementary to BPM when it comes to improve business processes and applications efficiency, compliance and continuous improvement.

Andreas Becks is our expert on the re-imagination of decision management. Whether we talk about improving customer experience, applying chat bots, preventing fraud, realising IoT applications like predictive maintenance, implementing credit scoring or claims management, or just automating internal processes, analytics and eventually AI will have a profound impact on these large and small decisions. Across all industries the discipline of'decision management' is seeing explosive change. It is also a topic that seems to defy consistent definition, especially with organisations progressing with analytics with varying levels of maturity. Andreas Becks is our expert on the re-imagination of decision management, so ten minutes with him seemed like a good idea.

There were some interesting and intense demos of how process would change over time. We all saw process linking with decision management, customer journeys, IoT, process mining, advanced analytics, AI, RPA, Robots, Blockchain, voice and image recognition. There were many dimensions of process evolution practically demonstrated in 30 minute segments. It was clear that process will be involved with significant innovation in the evolving digital world and that transformation is doable in increments. While most of the participants were vendors, there were notable visionary end users like Quicken Loans (the designers of customer journey called "Rocket Mortgage").

Many define artificial intelligence (AI) as machines that can think -- and perhaps even act. As businesses and consumers make decisions every day, AI-infused customer experience solutions that include decision-making components can radically affect how, when, and why customers actively engage. Many brands still rely on gut feel and a cookie cutter approach to govern the majority of their day-to-day sales and customer service decisions, though. They lack the ability to be proactive in automating simple decision-making or providing customer-facing employees with genuine insights to guide their more complex decisions. Often, it's because brands' analytics aren't applied or operational.

Organizations have made large investments in big data platforms, but many are struggling to realize business value. While most have anecdotal stories of insights that drive value, most still rely only upon storage cost savings when assessing platform benefits. At the same time, most organizations have treated machine learning and other cognitive technologies as "science projects" that don't support key processes and don't deliver substantial value. However, there are a growing number of large but innovative companies that are driving measurable value through "operational machine learning"--embedding machine learning on big data into their business processes. They're employing a new generation of software, skills, and infrastructure technologies to solve complex, detailed problems and deliver substantial business value.

Organizations have made large investments in big data platforms, but many are struggling to realize business value. While most have anecdotal stories of insights that drive value, most still rely only upon storage cost savings when assessing platform benefits. At the same time, most organizations have treated machine learning and other cognitive technologies as "science projects" that don't support key processes and don't deliver substantial value. However, there are a growing number of large but innovative companies that are driving measurable value through "operational machine learning"--embedding machine learning on big data into their business processes. They're employing a new generation of software, skills, and infrastructure technologies to solve complex, detailed problems and deliver substantial business value. One company found the approach so successful that a manager said it was like "printing money"--a reliable, production-based approach to generating revenue.