Table of Contents
- Executive Overview
- Digital Transformation Workforce Maturity Model
- Phase 1: Discover and Inform
- Phase 2: Pilot and Prove
- Phase 3: Identify and Transition
- Phase 4: Blend and Extend
- Phase 5: Transformed
- People Find New Ways to Problem Solve
Across industrial and infrastructure markets, “disruption” and “digital transformation” are the buzzwords often used to describe the fundamentals of today’s digital economies. They describe a broad set of market characteristics that have come about due to improvements in the use of data, technology, and interconnectivity, but the big question is about addressing digital transformation workforce change and navigation.
More data is being generated and accessed than ever before. Increasingly, physical assets (devices, machines, and other “things”) are being interconnected. Advanced technologies such as machine learning proliferate. The cost of these advancements continues to drop, encouraging industrial and infrastructure-related organizations to harness this combination to modernize, improve, and transform their businesses and services. One goal is to put barriers in place to prevent competitors from accessing new and existing markets and revenue.
Currently, many companies view this digital transformation as being technology-driven. Machine learning, cloud architecture, microservices, augmented reality, and IIoT platforms are just some of the technologies driving endless discussion among end users as well as fierce competition among solution providers.
Often missing from these conversations is a focus on the human element of digital transformation. Where do people fit in? The changes that digital transformation will have in the workforce are likely to be the most far-reaching and sustained effects. Not only will digital transformation change the number of people needed to do work, it will rewrite how that work gets done.
As such, those planning or going through digital transformation quickly realize that managing the human element successfully can be the most difficult aspect of the journey. That makes sense, as machines don’t “push back” when it comes to change, but people often do. Also, these transformation initiatives almost always have direct impact on humans. Knowledge and expertise, hiring practices and staffing levels, teams and organizational design, sales and support, customer engagement, etc. are all eventually affected.
This report will discuss digital transformation through the lens of human impact, examining the changes in the workforce as industrial and infrastructure organizations become increasingly data-driven and service-based. ARC includes a maturity model that outlines the five phases of digital transformation workforce evolution. Over the course of the maturation process, the workforce evolves from initially just being impacted (often negatively) by the outcome of transformation to actively embracing and driving continual change. For the initial three phases, we provide readers with recommended actions for how to consider the workforce roles and resources needed to support digital transformation.
Digital Transformation Workforce Maturity Model
A common trap that companies fall into with digital transformation, particularly those starting their journey, is to view it as a technology exercise. A workforce maturity model helps avoid that mistake by broadening the lens through which digital transformation can be viewed.
Organizations need to apply as much, if not more, energy to managing workforce and culture change as they do technology improvement. This focus needs to start in the initial discovery phase. As the organization outlines short- and long-term objectives, it must ask human-centered questions, such as:
- What roles and resources are needed to begin digital transformation and how do those shift over time?
- What skills are high-value in an increasingly digital and data-driven organization?
- How will knowledge management change?
- Will the expectations for leadership evolve? If so, how?
The digital transformation maturity model ensures those workforce questions stay front-and-center. ARC classifies this workforce maturity in five stages: Discover & Inform, Pilot & Improve, Identify & Transition, Blend & Extend, and Transformed.
We’ll discuss each phase in more detail. Few companies, if any, have achieved phases 4 and 5. As companies move closer to these phases, ARC will revisit the maturity model and provide more detailed guidance in the form of additional recommendations.
Phase 1: Discover and Inform
For the past few years, executives at industrial organizations have spent considerable time trying to understand and define digital transformation. Is it a market driver, or a data-centric business reaction to market drivers? What benefits and challenges should be expected? How do companies manage the transformation?
The initial phase, Discover and Inform (D&I), is where these formative questions are asked and answered (sometimes inaccurately). Those initially engaged in digital transformation spend considerable time coming to grips with technological concepts and terms, much of it outside their area of expertise. This is particularly true for operations personnel wrestling with IT/OT/ET (information technology/operational technology/engineering technology) convergence.
As these individuals try to answer these key digital transformation questions, they are confronted by a dizzying array of technology-centric information, ranging from accurate and pragmatic to hyperbolic and misleading. A natural initial reaction to this is to view digital transformation through a lens of technology rather than people. As a result, the D&I phase places little emphasis on digital transformation workforce evolution.
The workforce change that does occur can be seen in the emergence of individuals or small groups to champion change. They usually arise after change is mandated by the C-suite or out of frustrated reaction to long-standing, poor business or operational practices.
At this point, since digital transformation is narrowly defined and managed, workforce implications are suppressed, even by these digital transformation champions. Generally, this dynamic is due to the technology-centric focus of the D&I phase. However, workforce issues are also often “swept under the rug” out of fear that raising these will negatively impact organizational buy in to change.
Consider a common use case that emerges in the D&I phase: asset failure prediction. This very narrow use case contrasts with trying to implement overall transformation of maintenance practices, which is much more complex. Thus, while many would assume that success in the former will translate into success in the latter, this is rarely the case, since an overall transformation typically introduces scalability issues and workforce process disruptions that are much more entrenched and difficult to solve.
Phase 1 Recommendations
Encourage start-small/think-big perspectives to better identify issues that will emerge as the organization tries to scale. Begin by creating a digital transformation roadmap that includes workforce issues. The first step in creating such a roadmap would be to determine your digital transformation profile via an organizational assessment.
The profile provides direction for D&I and ensuing phases. Digital transformation begins with the proper framework in mind—with a clear identification and understanding of organizational objectives, skills gaps, and likely internal roadblocks. This approach helps organizations move beyond technology and data to consider process and people aspects of digital transformation. From a workforce evolution perspective, this assessment should include analysis of the following factors:
Phase 2: Pilot and Prove
As the discovery phase concludes, industrial and infrastructure organizations move on to the Pilot and Prove (P&P) phase, which involves developing digital transformation proofs of concept and associated return on investment. This often begins with asset connectivity and monitoring or, as mentioned, failure prediction analytics. Obviously, the emphasis in this phase is to create controlled use cases for pilots.
This phase, too, is often technology-centric, as practical issues with data management, solution architecture, and technology governance dominate. Though workforce issues aren’t driving concerns, an awareness emerges that workers are central to sustained, scalable digital transformation.
Pilot projects almost invariably default to leveraging existing human resources. Emphasis is placed on integrating existing skillsets within defined jobs for tactical purposes: data integration and cleaning, data science, solution vetting and education, and technology implementation. Wide gaps exist in skills and understanding within the organization. During this phase, reorganization (stemming from digital transformation) is typically avoided.
Here, the heavy lifting for projects falls upon existing subject matter experts. Those resources are typically supplemented by solution providers and outside expertise. The table on the next page shows typical roles that emerge in, for example, an analytics pilot.
A common mistake companies make in this phase is to assume that if a solution can solve one problem, it will naturally be able to scale to solve others. Due to a mix of technology and workforce issues, an organization will find that isn’t the case. Issues surface when more data and personnel are integrated into the project scope or when use cases are broadened. Often, organizations point the finger at technology when considering problems with scale.
Certainly, scalability failures can be due to technological limitations or a lack of understanding of what the technology can do. However, workforce challenges also clearly emerge that inhibit digital transformation growth and success. It’s important not to underestimate these.
In a digital transformation initiative for analytics, as an example, these challenges arise not around data science, which might surprise some. In fact, that resource is readily available via solution providers and can be later onboarded once the organization develops a comfort level with digital transformation.
Instead, scalability issues arise when additional operational subject matter experts (SMEs) are integrated into digital transformation initiatives. SMEs understand (and often design) operational processes and best practices. These high-value workers have specific knowledge of how to operate equipment, execute maintenance, and ensure safety. Their contribution to successful digital transformation is critical. Yet, they frequently resist change, causing issues with adoption and return on investment.
Often, these industrial SMEs are overwhelmed and are reacting to a series of constant fires, and they have years of experience working this way. The result is that urgency of the immediate prevents SMEs from taking the time to properly implement the step change necessary for sustained, successful digital transformation. The result is that digital transformation is limited within the scope of what can be managed in a reactive daily work environment.
Phase 2 Recommendations
As the organization moves from education into piloting, acknowledge and address the workforce challenges. This means that planning needs to incorporate a workforce readiness component, not at the organizational level, but at a more granular, solution level. For example, for analytics-driven digital transformation, this readiness planning might answer questions such as:
- Where will post-deployment process and productivity metrics improve from what is currently done? What will be done with those expected improvements (e.g., time saved, redundancy eliminated)?
- What are the resource expectations across the pilot footprint—creation, deployment, consumption, improvement, and extension?
- What needs to change in terms of current job load so that the necessary cross-functional personnel can participate?
- Is the improvement expected to be immediate or iteratively achieved? Has the planning accounted for the nature of the change on workforce?
- What communication plan is in place to outline expectations, timelines, and impact on the workforce?
How will the organization manage knowledge transfer related to the insights and best practices that will be produced?
Phase 3: Identify and Transition
As the organization navigates through the P&P phase, it begins to view digital transformation through a business-wide perspective, which brings it into the Identify and Transition (I&T) phase. During this phase it identifies key strategic benefits it can achieve and seeks to transition skills and practices from the pilot level to core competencies. Because change is so pervasive during this phase, it is the most challenging phase in terms of difficulty, disruption, and duration.
In the I&T phase, leadership recognizes and emphasizes the human factor of digital transformation both in terms of improving productivity (positive) and the potential to reduce the workforce, which has negative impact on employee buy in. Digital transformation core competencies emerge within the organization and some roles start to shift, usually beginning with those involved in initial pilots. As those pilots produce measurable results, automate processes, and scalability challenges are overcome, those individuals find the bandwidth to shed existing roles and responsibilities that are no longer high value in their digital work environment.
In large part, most projects are still driven by internal champions and specialists, who often just happen to be those involved in initial pilots. While many manual processes are automated, there is still a strong cultural bias on human decision making in digital transformation to act as check/balance against perceived technology-generated risk.
One of the main workforce impacts is the growing collective recognition of what key skills are required for sustained digital transformation. While hard (technical) skills are still considered of the highest value, the importance of soft (human) skills is recognized. Cross-discipline roles emerge for key digital transformation processes, particularly those that combine data management, internal client management, use case definition, and project management.
Operations work through the process of how to bring the necessary IT skills into their purview. Data provisioning skills emerge as critical as many data access barriers collapse. Some of these key skills include:
As an example, consider an organization that adopts a platform-as-a-service solution (PaaS) for digital transformation as a flexible, opportunistic way to identify and quickly improve operational processes. Application lifecycle management becomes germane to developing and deploying the software applications that will need to be built to do so. Application lifecycle management typically covers a range of IT-specific functions such as code governance, process flows, production, testing, etc.
In some instances, operations might need to develop and maintain application lifecycle management skill and support. Or, they may opt to leverage existing IT-department skills. Either way, the ability to use these tools become mandatory for effectively transitioning to the use of PaaS as a key tool in digital transformation.
Phase 3 Recommendations
Many organizations will see this phase as the most difficult. In many ways, organizations will have to learn how to shed some aspects of their risk-averse traditions to become more agile. Roles, processes, expectations, and accountability change. To help with the inevitable heartburn, organizations should:
- Mimic some of the things many current platform providers offer to overcome adoption barriers. For instance, productize digital transformation internally. Create and market process packages internally for high-value, proven use cases to provide lines of business with quick and sustainable wins. This would include a mix of ideation tools and services, human resources, best practices, and deployment, scale and handoff workflows. Doing so will limit disruption and learning curves while accelerating digital transformation of large swaths of the business.
- Continue to hire and collect expertise that emphasizes math and programming related to transformation technology – including calculus and linear algebra, statistics, scripting language, relational databases, distributed systems, mining, modeling, machine learning, and visualization.
- Emphasize transition and reskilling education, as it will be vital in shifting experienced workers with deep intellectual property (IP) expertise into new roles in which they can help accelerate digital transformation.
Phase 4: Blend and Extend
As the organization achieves success identifying and transitioning skills and best practices into core competencies, it will hit an inflection point characterized by sudden acceleration in digital transformation. ARC is not aware of any industrial company that has entered this phase yet, though some have certainly announced their intent to do so. Industrial digital transformation is too new and the change too far reaching for it to have been blended into a company’s DNA, particularly from a workforce perspective.
In the Blend and Extend (B&E), organizations are making the distinct transition from just becoming comfortable with digital transformation, to integrating it into all aspects of the business, including the workforce. Drawing upon lessons learned in the I&T phase, results delivered, or organizational design trial and error; the company permanently blends previously separate skills through a natural progression. Standout skills from the I&T phase, such as data science, will begin to “disappear” to a degree as operational leads are increasingly able to deliver those skills via increasingly intelligent technology with role-specific user experience.
As a result, old roles fall away, replaced by new jobs that emphasize a more diverse set of cross-functional skills better suited to digital environments. An example might be the ability of an operational individual (or small team) to blend data wrangling, analytics, domain expertise, project management (and other soft skills) to intuitively develop, visualize, and oversee real-time, highly-specific process optimization strategies.
In the B&E phase, the most valuable contributors will likely be those that have a blended data and operations expertise. The ability to “scale” those blended skills will be more important than technology scalability, which will likely have been solved. As a result, the new, in-demand industrial professionals entering the market might have formal training/degrees that combine training in skills such as:
Leaders that emerge in this phase will separate themselves by adding very strong soft-skills to blended expertise. Creativity, communication, and complex project management will be at a premium as applied to highly technical endeavors.
This blending of skills isn’t just relegated to people. It will occur, albeit in a differently defined way, as machines begin to take on additional decision making. Many decisions once considered “risky,” such as changing the operating state of an asset (other than simply on/off), required human intervention. The implications of those once risky decisions will now be well understood. Organizations will blend this added decision making into machines across increasingly larger swaths of the business, moving humans out of well-defined digital transformation processes.
A well-understood instance of this in consumer markets is the now-common adoption of machine-driven customer offer management or, going back even further, call centers and customer service. A diverse set of distinct skills and processes once considered too valuable to leave the hands of humans have been blended and extended into automated processes using machines.
In an industrial setting, an example might be asset failure prediction analysis. Instead of identifying an anomaly and sending an alert to a human for processing, a machine could identify the failure risk and determine the ideal solution. If it determines that a machine (versus a maintenance worker) is still best suited to solve the problem, it then automates the execution directly to a piece of equipment and verifies the outcome. This optimized human, machine, or human/machine decision making hails the arrival of true prescriptive analytics.
Phase 5: Transformed
Like the B&E phase, this endpoint of digital transformation’s impact on the workforce can be considered, for the moment, only as a future state. In many ways, an organization in the Transformed phase will be indistinguishable from one still working through B&E. In both phases:
- Digital transformation won’t be considered disruptive or something to push back against.
- Key roles and responsibilities will become clearly defined as requiring blended skills of math and operations. Those requirements will be driven into hiring practices and reflected in higher education.
- The core methods driving digital transformation, such as complex data management, data science, analytics, and artificial intelligence, will become so pervasive and embedded into operational and enterprise processes, systems, and devices that interactions with them will appear seamless and invisible to the user. Along with blended skills, these methods will be considered part of standard operating procedures.
- Outcome-based service will emerge as the default operating model, as real-time intelligence enables much faster decision making than in the past.
However, a few distinctions will separate the Transformed phase from B&E. These will be human-centered—the characteristics defining successful leadership and high-value skills.
As an organization is working to optimize its own real-time, digitally transformed operating environment, its customers will be doing the same. That means that what defines an optimized state for a customer at one moment could very quickly shift, driving a wave of upstream response. The ability to quickly adjust to that real-time signal will be a competitive differentiator.
Out of necessity, a different type of industrial leadership will emerge in the Transformed phase. As digital transformation becomes fully integrated, good leadership will embody the human talent for speed of recognition. This contrasts with many current (and certainly historic) leadership roles in industry and infrastructure for which success is primarily defined by mitigating risk and ensuring constancy in operational and financial performance.
Whether identifying opportunity, recognizing competitive threats, or executing effectively, these leaders will be capable of injecting speed and precision into those endeavors so that the desired outcomes are more quickly realized. This skill will be a differentiator as businesses operate in real time, value chains are digitally extended, and continual change is the norm.
In addition to speed of recognition, decision science will increase in importance. It will drive context frameworks for decisions that are then executed by techniques such as machine learning. Decision science includes behavioral economics, risk analysis and decision research, psychology, management science, and game theory, as examples. Previously specialized skills that had been of high value, such as data management and data science, will become increasingly commoditized within systems, tools, and machines, opening them up to a broader audience of users.
People Find New Ways to Problem Solve
Digital transformation, with all its myriad and often seemingly contradictory definitions, is certainly a step-change moment for industrial and infrastructure organizations and their markets. In this emerging world, disruption will continue to become a much more normal state where markets can be quickly created, changed, or toppled.
Speed of recognition is a core competency for succeeding in a digital economy. Technology can make executing that speed a reality, often injecting it into the business in new, novel, and highly valuable ways. In doing so, it is likely that many workers will be displaced, as intelligence is automated further into operations, organizational processes, and business outcomes.
Yet, as digital transformation occurs, the value humans bring to highly digital environments will increase. Opportunity to apply creativity and soft skills will open up as never before. Enabled by technology that mimics humans, people will be able to do what they have always done well—identify challenges, adapt to their circumstances, and find new ways to solve problems.
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