The application of AI with BPM will now be even more disruptive in the execution of structured and unstructured processes. AI technologies provide sentiment analysis and the possibility to convert unstructured data to something more organized. Machine Learning and Deep Learning are results of a system continuously learning from the workflow information it is capturing when processes are executed in order to improve performance, effectiveness and efficiency of the processes. Process Automation ,using Cognitive Systems, acts according to the most correct decisions based on the previous or real-time analysis.

IoT requires an automatic activation of processes or tasks that respond to the information of a sensor on an object; or predictively (condition-based) send orders to the intelligent things to operate unsupervised in a concrete way for a defined period of time. BPM can orchestrate all instances of a process necessary to manage the activity with sensors in real time and can store all traceability.

This will be used to automate repetitive and rules-driven business processes that do not require human intervention. In the BPM process flow, some tasks will be automated using bots (software robots) that replicate an employee's activity on the desktop of their computer to process complex operations in the same way as a human but achieving the same productivity. This occurs without errors and provides more time to employees for more important activities.

A trend that will improve the performance of BPM business processes. User task execution with smartphones requires a new intelligent conversational interface based on natural-language. Virtual Personal Assistants will be increasingly more valuable as an intermediary between humans, things and the BPM engine.

This is important to practitioners of BPM to provide better operational efficiency. Understanding events and triggers can determine certain behaviour patterns that can be used, in turn, to detect problems or opportunities for change. BPM initiatives will increase aggregation of core BPM capabilities with complementary platform elements including event processing, operational decision management and real-time predictive analytics.

In BPM, this is required for unstructured and unpredictable processes, where the exact sequence of tasks and activities cannot be determined in advance. The BPM focus will not only be oriented to structured processes, as many workers spend much of their time responding to unstructured and unpredictable work patterns too. these process instances are difficult to automate because it is necessary to respond ad-hoc to each event that is happening until solving the process. In unstructured processes, the end point is known, but the way to achieve the end result is determined by each stage/milestone in the management of that case. Each case will imply a specific sequence of activities and predefined results; hence, to solve these unstructured and unpredictable processes, ACM systems are applied, and this capability has been included by the main manufacturers of BPM systems (normally in BPMS this is called Intelligence BPMS). The rest of the manufacturers will shape their offer to be able to compete.

Currently, companies are not considering Process Mining, however universities are investigating their great potential and applying it to specific projects. Organizations spend a lot of time modelling processes, since they cannot get all the information on what the AS-IS process is like. Process mining techniques allow a party to extract information from event and transaction logs to discover models that describe processes; so it reduces the time necessary to model a process that is then optimized and automated in BPM.

Organizations are now demanding a fully functional automated process (pilot) that can be personalized in a few days and can be perfectly adapted to the processes of the company. The evolution from BPM to low-code has been in play for several years now. In 2018, many old-school BPM vendors will update their BPM products to provide process-focused low-code platform.

BPM initiatives will start to include Virtual Reality (3D environment), Augmented Reality (real-time use of data integrated with real objects) and emerging Mixed Reality (extend physical world – touch and sound) to increase the productivity of the employees, as it is possible to use them to enhance design, training, visualization and to provide hands-free information during the execution of any activity of the business process.

Predictive and prescriptive analytics allows the detection of large data volumes (Big Data), Natural Language Processing (NLP), contextual analysis and active analytics to monitor the progress of activities, analysis of activities and changes around processes aligned to KPIs and strategic objectives. Augmented analytics uses machine learning to enable employees to spend less time exploring data and more time acting on the most relevant insights for executing the business activity.

In the coming months, many BPM vendors will talk about the challenges and opportunities involved in the shift towards the use of AI technologies in BPM. AI will start to play a key role in digital transformation in all organizations and BPM will see a new disruption from the inclusion of machine learning and new AI features. For most companies, these BPM industry trends mean that it is the moment when organizations will have to approach their digital strategy and begin to apply it more broadly. Digital innovation in companies requires reviewing all current processes to make the necessary changes to achieve their digital and corporate objectives and for BPM; discipline and technology will play a major role in the things to come.