Technology trends for the change community

by Sep 7, 2022

I’ve been working with a client on a digital transformation engagement that is heavy on automation and its had me in a reflective mood.

While much of the change we do focuses on new technology—digital transformations, the domination of SaaS based platforms and services, the shift of careers to cloud based roles, etc. we really have not seen much change in adoption of technology in our change practice except for the introduction of Power BI in making some pretty swank dashboards.

SaaS based enterprise change tools seem to come and go – nothing really dominates in the market, and with this comes a lack of innovation in the space. It needs to take hold to innovate!

There’s no shortage of exciting options available to us.

Think about Blockchain, and NFTs – The essence of blockchain is a ledger of trust. While it may have emerged in the context of Bitcoin, the industry adoption of blockchain has shown its value in offering proof in the place of trusted institutions. Might there be a blockchain application that enters the change delivery space, replacing the human building of trust?

Mindfulness is simply a conduit to better data processing

And yes, I know I was bullish on Augmented Reality, Virtual Reality and Gamification as a way of digitalizing the change experience… and that has not transpired. But I do think it would be worth casting an inquiring mind into blockchain given the uptake from institutions that are seemingly conservative.

Boundaries. Limits. Edge

Robotic Process Automation (RPA) is an increasingly common domain to be working in change. Despite the prevalence of the work, there has been little curiosity about how we could use machine learning and artificial intelligence to pre-empt responses to change and provide insights on how we might approach change. This could be integrated into the ideation of change (to understand probabilities of success), to the leading of change (to provide insights on which messages work best), and to the delivery of change (to understand which teams to commence the roll out of change with).

Where are the conversations on how we use big data—in a socially responsible way—to change?

Where are the conversations on how we might integrate biometrics into our dashboards to understand how people are receiving change?

If your Apple Watch can send an alert to your GP on your impending heart attack, can we send alerts to the People and Change teams on levels of stress with change?

The issue of social responsibility is a big one to address—we know that there are huge issues with the ethics of machine learning, artificial intelligence, and the detrimental impacts of bias in coding. I don’t think this is a reason to avoid the conversations though.

Let’s Play 


Here’s an interesting exercise to play with – take Gartner’s Top 12 Technology Trends of 2022 (or your vendor / guru of choice) and see if you can translate how that could impact change leadership and practice (positive or negative). So, for example the trends run heavy on four areas: automation, management of data (quality, access, storage and ethics), artificial intelligence(AI) and modular to total experience (scale of technology). 


Hyper automation – Much of our change work is starting to focus on the people side of hyper automation – addressing the reluctance and fear to embrace this as a business approach. But where are we looking at change activities and seeking to hyper automate? Could we be as reluctant as our business stakeholders? Do we REALLY need to create a stakeholder list EVERY time? Or can we have an automated list pulled from a dynamic database (Spoiler alert, we can).


The trends on management of data speak to questions about which change data should be used and help us sort through the noise in change efforts and asks us to weave in data threads outside of the standard organisational data (eg customer data, competitor data and social media), a change fabric if you will. Cybersecurity mesh could be a useful metaphor in thinking about the change mesh – best of breed, stand alone change risk solutions to protect the change objectives. Privacy-Enhancing Computation is a technology that will be core to avoiding the misuse of personal information in change efforts – where we consider the convergence of physical and digital signals. While I have mentioned the value of ethics committees, we can take away the morality contests with privacy enhancing computation.


Decision Intelligence is one of the areas where change leaders and practitioner’s alike struggle. To some extent when engaging with change data it can feel like drinking from the firehose – so imagine a world where we had decision intelligence built in our change designs, giving us confidence and speed in the next steps of change.

AI Engineering – I still think this is an area that change could benefit from – and I get that the ethics side of it is hugely problematic. But with decades of change experience, we could surely be able to use Artificial Intelligence to augment and update the way we lead and deliver change.

 Autonomic Systems – Oooh – this one could get very scary for the change practitioner or leader who wants to focus on just delivering change as they always have. But in keeping with change is everyone’s business and those who work in change ALREADY see their role changing having self-managed physical or software systems that learn from their environments and dynamically modify their own algorithms in real time to optimize the change efforts. Imagine a world where you have a system that picks up there has not been enough eyes on the change communication, so rewrites a piece and redistributes to the areas that are lacking knowledge?

Generative AI is super exciting and the answer to every organisation or team telling you they are special. The ability to personalise and individualise change training is of immediate use here.


The advent of Cloud-Native Platforms is worth looking at as a counterpoint to the challenges of implementing SaaS based software (the one-way, same way) nature of them. It may also be the solution to my observation that the SaaS based enterprise tools have not taken off – cloud native will permit the desired customisation and agility. Similarly, Composable Applications – We are already using this principle in some part when we build change approaches that lean, agile and reflect ‘change is everybody’s business’. Take a minimum viable change process and repeat where its effective – don’t reinvent the wheel, but do break it down to the smallest composable application of change. 

 In contrast consideration of Distributed Enterprises means a digital-first, remote-first business model to improve employee experiences, digitalize consumer and partner touchpoints, and build out change experiences – this one is a no brainer, should this be all of our change efforts today? Total experience is a business strategy that integrates employee experience, customer experience, user experience and multi-experience across multiple touchpoints to adopt change. Call it ChX™

 Consideration of technology trends needs to occur in tandem with consideration of business trends. Gartner identify the following three macro trends in relation to data and analytics:

  • Activate Diversity and Dynamism
  • Augment People and Decisions
  • Institutionalize Trust

 At first glance these look very much like drivers of good change…

As I said, I’ve been bullish on technology trends before and not seen them play out. The cost to implement in change is perhaps too high, goodness knows.

But for the companies who pride themselves on being leading edge, for the practitioners who see themselves at the forefront of change, and the change leaders who want to future proof, I would suggest a little more reflection in this space might pay off.

 Let’s play.



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