Three-quarters of digital transformation initiatives are stuck in “pilot purgatory.” Why are so many projects unable to scale their digital systems at an enterprise level?
While technical boxes may be checked, organizational adoption – if and how employees welcome the change – is often ignored. Achieving genuine buy-in from people is a much more complex challenge than installing hardware or software. Go figure.
From our experience, there are four common hold-ups when it comes to organizational adoption. As an IT leader, ask yourself these questions:
- How are you making it easier for your employees to visualize a new “day-to-day” and make them comfortable with those changes without fear of job insecurity?
- In what ways are you reinforcing and reskilling your existing talent so that data competency is pervasive across your organization?
- Where can you revise your organizational structure to encourage collaboration rather than competing priorities that slow down the project?
- What processes can you institute (or improve) that will enable a faster pace of change so that your company can learn continuously and organically?
Changing roles and job security
Most people do not willingly accept change, especially when it comes to job disruption. Recent research regarding job security and automation trends shows that about a quarter of people are fearful that “AI will take their job.”
This percentage is lower than it was a few years ago, thanks to a better understanding of the human role in the workplace of the future, but it still calls on CIOs and IT leaders to thoughtfully consider how they will convey changes to valued employees.
Consider the employee who typically spends many hours a week collecting data and building reports. Their natural responses to a new digital tool will almost definitely be fear of reduced job security if their concerns are not addressed directly. For such employees to adopt modern technology, there must be an incentive.
For starters, as systems are connected and processes are digitized, many information-based tasks that were once done manually, such as data collection and reporting, become automated. This frees up their time to focus on more strategic, meaningful, and high-level tasks. It’s not a matter of replacement but evolution. The role of the employee will change from doing repetitive tasks to more analytical and problem-solving work.
Defining these new roles does not need to be a strictly top-down decision. Implement a proactive change management strategy to engage all impacted employees early in the process. Incorporate employee feedback in the solution and organizational design process, leveraging those who will be most impacted to shape the future of the organization, building change champions and advocates along the way.
Rethinking the approach to talent needs
Data analytics does not always require data scientists. CIOs and IT leaders often reach a turning point when they discover that most employees can be trained to become resident data analytics subject experts. When employees combine new knowledge of data analysis with their existing knowledge of the processes or machines, they can quickly be at the forefront of a digital journey.
This is welcome news to most IT leaders, simply because the demand for skillsets in data science and cybersecurity has skyrocketed. Upskilling existing team members can be critical in attaining sustained adoption and continuous improvements of digital solutions. This includes long-term improvements in employee engagement and retention, increased cross-functional collaboration, and adoption of modern technology trends.
Along with their technical skills, employees need to be skilled at diagnostics and problem-solving using the data now readily available to them. Employees who may have previously been data-gatherers can shift to become problem-solvers based on new data-driven insights. Make sure your employees are ready to learn and grow to take advantage of these opportunities.
Effective collaboration among IT and operations teams
When two forces within an organization are unwilling to work together, it can create immense friction. In the case of digital transformation, the priorities of IT and operational technology (OT) are often competing and unaligned.
For instance, in a manufacturing setting, OT and operations teams focus on improving the productivity of the plant (i.e., making more product for less cost). On the other hand, IT typically focuses on sustaining enterprise platforms and mitigating cyber risk.
Competing priorities can also result from how projects are funded. OT teams are often focused on solving problems in one plant and sometimes even incentivized to compete against other plants within the same supply chain network. IT teams may be more interested in a scalable solution that benefits all plants but are not able to fully fund a project.
When these two priorities are exclusive, it limits collaboration and delays the digital transformation process. However, adoption depends on effective collaboration between IT and OT teams: OT teams bring the manufacturing process expertise and knowledge of where the data is born. IT teams ensure that the enterprise platforms and requisite network infrastructure are reliable, scalable, and secure.
To align the objectives of both IT and OT teams, consider formalizing initiative teams dedicated to digital transformation with leads and subject matter experts from both IT and OT functions. These cross-functional teams, funded jointly between operations, engineering, and IT, can collectively define initiative objectives, collaborate on implementation plans, and be the change champions across the organization. They should have a common group of executive stakeholders, read out progress together, and be rewarded together.
Enabling a faster pace for learning and experimentation
To stay competitive in today’s digital world, organizations must stay up to speed on digital initiatives. They must extract lessons quickly and apply those learnings to implement new functionality. Creating a culture that fosters continuous growth and learning opportunities is not only critical to the successful adoption of digital tools but to continuous improvement of digital technologies through experimentation.
Core attributes of “learning organizations,” a concept popularized by Peter Senge, include systematic problem-solving, experimentation, and knowledge transfer. Systematic problem-solving incorporates generating a hypothesis, testing that hypothesis, and using data as opposed to assumptions as the basis for decision-making. A minimum viable product (MVP) approach can be used to quickly test a hypothesis and generate data to evaluate the solution’s efficacy. Using a common enterprise platform can enable you to scale an MVP across the enterprise quickly.
Digital transformation requires a change in thinking – about the way we address employees’ concerns, their desire to learn and gain new skills, their ability to make higher-level decisions, and their keenness to experiment and problem solve. But too often, organizations are trapped in a box. They overlook the critical link between having the right technology and achieving a step-change in operational productivity. When IT leaders ask people-centered questions, they can get to the heart of why their digital transformation initiatives are stuck and make necessary changes to move those efforts along.
[ Discover how priorities are changing. Get the Harvard Business Review Analytic Services report: Maintaining momentum on digital transformation. ]
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