Strategic advice to leverage new technologies

In my years of implementing SPM across different teams and organizations, the notion of an object can be distressing. Unlike its sibling hierarchy, phases, the object concept is not well understood or even implemented much of anywhere in other project methodologies, setting aside enterprise architecture methodologies, which often model objects more extensively. IT people accept the idea of a project phase without question. When confronted with the notion of an object, however, the apparent simplicity of the definition of an object transitions quickly to difficulty upon further probing. 

We can characterize the fourth stage of automation, smart automation, by intelligence embedded across customer channels (stores, call centers, websites, etc.), processes, systems, and platforms. Smart automation builds on the previous stages and uses intelligent means to bring automation to all aspects of a business value chain, from customer experience (CX), worker experience, internal processes, and operations to partner collaboration, covering all types of systems. As we explore in this Advisor, however, it is important that smart automation design carefully consider the subtle interplay between humans and machines to understand the nuances of those activities humans are good at and those for which machines are efficient and reliable.

Industry proponents have been pushing the idea that artificial intelligence (AI) is set to have a major impact on customer experience (CX) practices. But how do end-user organizations feel about AI’s potential for facilitating CX? After all, they are the ones who will or will not utilize the technology. In this Advisor, we share some initial results from our ongoing CX management survey offer some insight into organizations’ attitudes toward AI’s potential impact on CX practices.

In the post-Agile world, the Agile mindset is so natural that people don’t even need to reference it anymore; they look at the larger picture. Where should we be directing our energy, and will we get there? In the fifth piece, Gabrielle Benefield and Kubair Shirazee tell the story of using the Mobius framework, with its double-loop learning and ultra-rapid feedback, to help small business owners in Pakistan and perhaps stop the spread of radicalization and extremism.

One of the most exciting ideas percolating through the Agile community is “solutions-focused” thinking, advancing through micro changes. In the next piece, Géry Derbier and Soledad Pinter tell stories of using solutions-focused thinking over several years, in the large — across an organization — and in the small — at the single-person and single-team level.

Andy Hunt and Alistair Cockburn were part of the group that wrote the Agile Manifesto, and Joshua Kerievsky was practicing Industrial XP at the time. We, as foundational members of the Agile movement, are among those who feel that Agile has become overly complicated over the years, and we are now working to simplify things; to make sure the core ideas work in all fields, not just software. As we describe in the first article of the issue, it is time to reboot Agile.

This Executive Update asks whether teams can use AI to increase the performance of Agile teams. Data collection, analysis, prediction, and reporting tend to make up a large proportion of the AI capabilities used in the project and portfolio management disciplines. Most tools are not uniquely oriented toward Agile delivery; this Update will concentrate on the Agile dimension. (Not a client? For a limited time, you can download a complimentary copy of this article here.)

Design thinking provides a structured approach to uncover the human factors in smart automation. It is also important to understand the design considerations involved. We explore those as well as the systems/technology and business processes involved in this Advisor.