Organizations that foster a culture of continuous change must also encourage a culture of innovation, as technology is the key enabler of change. Ralph Rio, vice president of Enterprise Software at ARC Advisory Group, argues that technology provides consistency and can help accelerate change programs to new levels of productivity.
Program managers faced with the challenge of leading the change management process need to embrace innovation because technology can help automate their processes and their streamline workflows, reducing the disruption of change and helping to ensure a positive outcome.
Andrew Tarantola, senior editor at Engadget, notes that today’s technological innovations are driven by information and automation. Artificial intelligence, he says, is shifting the way we interact with computers and data, and AI is the next logical step for technological advancement in industry.
Here is how these technologies can enable sustainable change for organizations, and how to implement them throughout the change management process.
Tools That Facilitate Continuous Change
A few of the key goals of continuous improvement are to increase productivity, improve quality, maximize value, lower costs and decrease delivery times of projects. By investing in the right technology, businesses are able to meet those goals by streamlining workflow and maximizing their employees’ time.
Here are some of the automation tools those companies are implementing into organizational processes to facilitate change:
Workflow Automation Tools
More and more companies are integrating automated technologies with their human workforces. The WorkMarket’s 2020 In(Sight) Report: What AI & Automation Really Mean for Work study concluded that 90 percent of business leaders and workers see advantages to automating tasks at their organizations. In one of our previous posts, we posit that automating the change management process helps keep the data clean and eases the transition due to change.
One such automated tool is workflow automation software, which makes complicated business processes easier to manage. Workflow automation software can be especially important in continuous change projects simply because of the sheer volume of tasks and paperwork involved in implementing and managing change.
Program managers who have access to a tool that can automate the change approval process and documentation production process are better positioned to see the change initiatives succeed, explains our Chief Defense Industry Strategist here at Artemis, Joe Kerins. Such software automatically adjusts the project baseline per the approved change, and then automatically adjusts workflows.
This potentially creates huge time savings. The 2020 In(Sight) study showed that 78 percent of business leaders believe automated processes could save them up to three hours a day. Having to spend less time on administrative and repetitive tasks means program managers and their teams are free to focus on more impactful work.
Turning Data into Intelligence
Data drives business, and organizations are constantly seeking new ways of capturing and analyzing data to improve processes. In a previous post, we make the case for why and how data is an informer for the change management process.
For example, the ability to run multiple “what if” scenarios to analyze the impact of change provides unparalleled data about the potential impact of change. These scenarios predict how a change will impact the constraints of a project through to completion. Armed with this real-time information, program managers can make instantaneous decisions changes that have a positive impact.
Another example is the ability for program managers to build efficiencies and quality checks into their software program to ensure that change programs follow project-specific rules and regulations. The software learns the checks and alerts the user when certain tasks will result in the breaking of rules. Program managers can proactively make adjustments to ensure this does not happen, preventing issues later in the project delivery.
Natural Language Processing (NLP)
The use of natural language processing in businesses cases in on the rise. NLP is the ability of a computer program to understand human speech while it is spoken, allowing humans to talk to the machines as if they were human. A classic example of technology using NLP is Apple’s Siri.
Natural language processing is finding its place in gaining intelligent insights for businesses thanks to its data mining and analysis capabilities. Kaushik Pal, a technical architect and the Founder & CEO of TechAlpine with 19 years of experience in enterprise applications and product development, says NLP can help a company mine information from vast amounts of data, provide better documentation and improve process efficiencies for documentation.
For example, in a customer service use case, NLP is capable of going beyond simple insights into customer behavior to analyze the feelings of a customer, called sentiment analysis, to provider deeper insights into product or service performance and what can be done to improve quality.
Deploying Technology in the Change Management Process
In our post on how program managers can facilitate continuous improvement, we unpacked the continuous change management process and how it helps organizations evolve. It is important to recognize where technology fits into that process.
During the impact analysis phase, machine learning would be especially helpful for all of the data the technology could provide on the potential impact of change. James Hodson, chief executive officer with the AI for Good Foundation, notes that machine learning is reshaping some companies’ supply chains thanks to a deeper understanding of evolving consumer preferences.
These technologies are also helpful for sustaining change. This phase is where workflow automation tools have their biggest impact. Taking automation and AI to the next level, Japanese company Fanuc sells robots that can learn on their own, notes Cathy Reisenwitz, technology writer at Capterra. The robots combine AI powered by machine learning algorithms to automate processes. And, such technology is not beholden to a shop floor. Machines that automate processes are also improving efficiencies in the back-office setting. When mundane tasks are automated, more innovative progress can be achieved.
Keep in mind, however, that while these technologies are available to enhance business efficiencies and innovation, organizations that adopt them without a strategic plan in place are setting themselves up for failure. As Hodson notes, “Artificial intelligence cannot turn a business’s performance from bad to good, but it can make some aspects of a good business great.”
He recommends a few simple steps to integrate these technologies into an organization:
- Catalog business processes and collect as much data as possible about how decisions are made and the data used to make those decisions.
- Focus on simple problems that are well-defined and well-understood, and where the data supports the ability to make a decision.
- Do not use machine learning when business logic is all that is necessary.
- Use machine learning to create decision-support systems for complicated processes.
The bottom line is that when implementing these technologies, they should be used to simplify, not complicate. Be sure to think strategically about where technology will have the most impact for managing continuous change.
Research from Capgemini shows about one-third of US companies are using AI at scale (“i.e., they are going beyond pilot and test projects”).
Because there are so many AI tools available across such a broad spectrum of capabilities, Jacques Bughin, Brian McCarthy and Michael Chui at McKinsey suggest taking a portfolio approach to implementing AI.
In the short term, focus on use cases where there are proven technologies, scaling them across the organization to drive value. In the medium term, experiment with emerging technology to prove its value in key business cases before scaling. In the long term, work with a third party to solve a high-impact use case with advanced AI technology to capture competitive advantages.
The key is recognizing where technology can provide efficiencies for an organization that is continually evolving.
What Does This Mean For End Users?
Technology can help facilitate the change management process through automated tools and data intelligence capabilities. For program managers, this means more insights with which to base change decisions and tools that help manage and sustain change over time. The end result is an improved change management process that utilizes the strengths of employees and the efficiency of automation to improve upon processes and deliverables.