Enterprises of the future will be complex system of systems of socio-cyber-physical actors, that operate in dynamic and uncertain environments. They will need to continue delivering on their goals in the face of unforeseen changes and events (such as global pandemics) along multiple dimensions. The goals they aim to meet are also likely to evolve rapidly as enterprises look to exploit opportunities as they emerge in their ever changing environment.
These dynamics will play out at multiple levels, requiring a cohesive response across three key planes of enterprise, namely: the intent plane concerned with the purpose of the enterprise, the processes plane concerned with the processes needed to realise the purpose, and the organisational plane concerned with the organization of the (socio-cyber-physical) actors (and infrastructures) that enact the different processes. All of this facing continuously shrinking time windows. This puts hitherto unseen demands on enterprises as regards responsive decision-making with partial information in the face of uncertainty and swift adaptation to support continuous transformation while optimizing stakeholder value.
Meanwhile, wave after wave of information technologies, such as (statistical) AI, IoT, Digital Twins, Low-Code, No-Code, etc, bring the promise of enabling enterprises to be more intelligent, more efficient, more flexible, and even more agile. As a consequence, it is to be expected that future enterprises will be increasingly model-guided, AI-powered and data-fueled; giving birth to what we prefer to call AI-native enterprises.
The emergence of AI-native enterprises, however, also raises fundamental design challenges: How to ensure coherent design of such enterprises? How to balance change and stability? How to manage uncertainty? How remain (just enough) compliant to regulations? What about ethics and privacy?
Furthermore, what is the future role of existing disciplines such as Enterprise Modelling, Enterprise Engineering & Architecting, Modelling & Simulation, Process Engineering, Knowledge Engineering, and AI towards the emergence of AI-native enterprises? How do fundamental concepts such as actor-network theory, multi-agent system theory, and control theory fit? Can novel technologies, such as Machine Learning, Adaptive Software, Digital Twins, and Reinforced Learning, further enable the emergence of AI-native enterprises?
The workshop aims to discuss these, and other relevant, issues across the entire gamut ranging over the state of art and practice, limitations and lacunae, possible means to overcome them, case studies illustrating the line of attack, and future work. As such, the goal of the AInE workshop is to bring together leading researchers across different relevant fields, in order to (1) explore the challenges facing the emergence of AI-native enterprises, and (2) exchange and discuss ideas, concepts, approaches that aim to meet these challenges.
Topics
Topics relevant for submission, but not limited to:
- Modelling Data-driven Organizations
- Enterprise Engineering and Architecting
- Modelling and Simulation
- Multi-perspective Business Processes
- Modelling Enterprise Security, Risk, Privacy and Regulatory Compliance
- Modelling in Industry 4.0, Cyber-Physical Systems, and Digital Twins
- Adaptive Software
- Knowledge Management and Enterprise Modelling
- Human Aspects in Enterprise Modelling
- Large Scale Organizational Structures and Enterprise Ecosystems
Workshop website and submission site
https://conf.researchr.org/home/ai-ne-2021#Call-for-Papers
https://easychair.org/conferences/?conf=poem2021 Please remember to select " AI-native Enterprise" Track.
Important dates
- Submission via EasyChair: end of Sun Oct 3 AoE
- Reviews Due: end of Fri Oct 22 AoE
- Decision communicated to authors: Tue Oct 26.
Workshop Chairs
Vinay Kulkarni, Tony Clark, Henderik A. Proper, and Sreedhar Reddy