The Cognitive Path
Designing for intelligent robotics
Policy Research
(building on previous research originally published for IBM Design Institute)
Envisioning a new co-operation
Robotic technologies have evolved beyond automated task execution to encompass a scope of AI-based capabilities. These intelligent robotics are steadily revolutionizing many aspects of knowledge process, changing how individuals and enterprises view their activities and operations.
For enterprise, there are fundamental questions in transformation, such as:
- How will intelligent robotics affect our operating model in the next five years?
- What is achievable with cognitive technologies?
- What is the future of work with these new inter-agent cooperations ?
Few enterprises, however, are making a great deal of progress beyond narrow demonstrations of capability and are now looking more seriously at larger-scale paybacks through the new value levers available. As enterprises look toward this horizon, they are seeking ways to create appropriate, ethical strategy and governance models for firm-wide automated decisions, while laying the foundation for an enterprise co-operated by humans and intelligent robotics.
Questions for design
- Which functions and processes should be more automated and how ?
- How will automations be owned and managed, including interventions and exceptions ?
- How will policies and procedures be created that both control and encourage innovation in intelligent robotics ?
- How will enterprise value and differentiation be created with these new technologies ?
- How will economies of operational scale be ensured to avoid waste in disconnected technology investments ?
- How will knowledge work be transformed within the enterprise ?
- How will robots share information and how will compliance, security and privacy be maintained ?
- How will change implications be managed and new skills developed, particularly around intelligent automation governance and analytics ?
- How will enterprise intelligent automation align with corporate objectives for innovation and the business model ?
Scenario planning
While the enterprise benefits of intelligent robotics go way beyond cost reduction to encompass greater control and sophistication in service design, few organizations understand the upfront and ongoing cost drivers.
To mature intelligent automation delivery method and benefits, the enterprise should seek to gain a thorough understanding of cost drivers, including automation management, technology environments, skills development, analysis and controls. From this standpoint, intelligent robotics can be applied consistently and aligned with strategy over functionality.
As labor capacity is released within the enterprise, post-automation strategic levers will need to be evaluated and the requirements for new skill sets assessed. High-value skills in automation management, service design, data curation and advanced analytics will be focus areas as new opportunities for growth emerge.
Indeed, market forces for labor and intelligent automation are evolving quickly and enterprises should begin to run scenarios and futures, based on new operating models, future work patterns and human impact.
Orchestrating cognition
In defining an intelligent automation strategy and governance, a full spectrum of capabilities is required to execute and define the instrumentation of automation and cognitive technologies across end-to-end processes.
Techniques in process management determine critical steps to ensure that inefficiency is not automated and that new agility is enabled in the process. End-to-end automation can then be determined effectively, with consistent approaches to redesigning operations combining different elements of robotics, autonomics and cognitive. While autonomics represents the self-managing aspects of process automation, intelligent robotics go further, incorporating artificial intelligence and learning disciplines, such as perception, attention, anticipation, planning, memory, learning and reasoning.
In laying the foundation for a path toward cognitive automation, a variety of capabilities should be developed within intelligent robotic frameworks:
- Cognitive applications: Cognitive-by-design application development
- Cognitive APIs: Service touchpoints and decision functions for robotic tasks
- Cognitive process mining: Insights on processes, with machine learning algorithms for self-optimization
- Cognitive process orchestration: Direct, initiate and control robotic tasks
- Cognitive expert systems: Systems based on theories of expert human reasoning and learning
Image: IBM Institute, 2017
Aligning autonomy
Enterprise pilot projects and proofs of concept often leave a void between their relative success and the scale-up potential. Multiple, disparate automation instances do not account for the complexities of change and technology integration at scale. Furthermore, the skills required for discrete automations, represent but a segment of the skill-sets required for co-operation with intelligent robotics at enterprise scale. 4 dimensions can be mapped on a maturity continuum to align autonomy in development practices and drive innovation.
- Identified opportunities
- Benefits sequencing
- Innovation ownership
- Strategic control
There are a vast array of work processes and activities that can be automated. Physical and virtual workforces alike, should have the freedom to automate and innovate and yet, be strategically aligned for scale and technology governance. Maturing through narrow implementations, toward fully embedded inter-agent capabilities, and a shared path to increased cognitive potential.
Author: Ivan Sean, c. 2018-19 | USA
© 10 Sensor Foresight
Period: 2016-2019 | Language: English
Core Concepts: Cognitive Automation; Intelligent Robotics; Aligned Autonomy
AI-Usage: Non-generative digital platforms, output validation
Conflict of Interest: None
References: Expansion of research originally published by IBM Institute, 2017 | 'Cognitive Automation', Key Note Talk, Watson Research Center, 2017 and IBM Think, 2018 | 'Strategic Hub for Innovation', Working Group, Securities & Exchange Commission, 2016