Reframing Strategies and Structures

The keys to supporting digital transformation

by Henry J. Lindborg

According to McKinsey and Co., about 70% of digital transformations fail.1 Historically, total quality management (TQM) has posted the same results. In 1994, Michael Beer reported that TQM’s failure rate exceeded 75%.2 Both were to be expected.

Any programs promising transformational (total) change carry the inspiration and baggage of myth. Envisioning how we want to make rapid change may invite unrealistic promises and magical thinking.3 So, if we’re not entirely improved or thoroughly digitized overnight, we’re disappointed and speculate that our organizations are full of “rebels against the future” (Luddites) who doom us to living among the failing 70%.4 We forget that comprehensive change usually is iterative, and best enacted through long and short-term projects.

Deep, positive changes in operations and culture are possible—and often necessary—responses to disruption. A useful definition of transformational change is, “intended and multidimensional change that departs radically from an organization’s past precedents, aims at large-scale readjustments, and is complex and systemic. Organizational leaders seek these changes to overcome organizational inertia and set a new course for their organization’s development.”5

A critical factor is intention—that is, strategy. Healthcare, for example, will continue to be affected by artificial intelligence (AI), health sensors and quantum computing.6 For these to contribute to transformation will require systematic strategic change and a conscious assessment of the effects on risk management, organizational structure, finance, human resources, training and service.

Serious study of AI—computationally created entities that exhibit intelligent behavior—dates back to the early 1960s.7 Now, with rapid advances in AI, understanding the results of its application—along with other advanced technologies—is key to developing transformation strategies. Recent research based on surveys of global organizations conducting more than 7,000 projects found that although only 8% of respondents were advanced leaders in deploying technologies, experienced firms showed business results: “improvements in operational efficiency, increase in revenue, strengthening of offerings’ competitiveness, and customer experience enhancements.”8

The study also found that there are seven traits that distinguish leaders from laggards: “integrated data management, CEO priority, security strategy, digital processes, digital strategy, agility and open innovation ecosystem.”9 Challenges included the need for digitally skilled and knowledgeable staff, as well as a lack of “organizational agility, internal resistance to change, security risks, lack of leadership and sufficient funding, as well as the challenge of integrating new digital technology with existing technology.”10 Quality professionals will recognize aspects of these challenges as common to most change projects—with a new emphasis on technology.

Career issues in particular are raised by the staff’s technical qualifications. What are the prospects for quality managers without a technical background? The authors note that “AI success is not just a function of technical skills such as data science capabilities and skills in new digital technologies and cybersecurity. Managerial skills in the form of strategic capabilities are vital. … At the heart of these managerial skills is awareness and understanding. … Questions such as ‘How can AI help defend, grow, or transform our business?’ or ‘How can AI improve operational efficiencies?’ are indicative. Answering such questions does not require an in-depth how-to technical understanding of AI, but does require managerial curiosity and interest paired with firm, customer, and industry knowledge.”11

Simply deploying AI or any other technology—even in important functions—by itself doesn’t constitute transformation, especially of culture. A comprehensive, integral approach enhanced by agility and led by the CEO is essential. If we return to TQM, we find some good, long-standing advice on leadership and flexible response. In assessing why TQM transformations failed and suggesting new structures, Beer’s research found that programs that sought to motivate top-down without adequate feedback created gaps between rhetoric and reality that extended across units.

Further, he advised, “Organizations must unhook themselves from their functional moorings by delegating authority to lower-level cross-functional teams who have decision rights to implement process changes using the technical methods of TQM. In almost all cases, this means functional managers will lose power, and process team leaders and members will gain power. For these changes to occur, the basis of power must shift from authority based on position to authority based on knowledge and proximity to problems and information.”12

His guidance still is valuable for quality professionals. Strategically integrated projects with good managerial feedback loops can support transformation. Though managers may lack deep technical know-how, they can tap that expertise in project team members able to assess benefits and consequences of technical implementation.

In addition to understanding the rewards and challenges of cross-functional projects, quality professionals bring valuable skills learned during lean implementation. Expectations shouldn’t focus on lean alone, however. In addition, digital transformation itself shouldn’t be viewed as a lean project.

Based on in-depth studies “in 10 diverse delivery systems, as well as over 200 research papers reviewed in 11 literature syntheses,” Michael I. Harrison concludes, “Regrettably, limited evidence supports … high expectations. Lean can contribute to improvement, but successful [l]ean applications typically yield focused, step-by-step improvements rather than dramatic jumps in performance or transformational organizational and culture changes that run broad and deep.”13 At the same time, with these caveats, lean tools and techniques, along with other quality approaches, still have value.

The transformative changes involved in the whole spectrum of Quality 4.0 require a comprehensive reframing of our strategies and structures. Digital transformation requires patience, clear-sighted acknowledgment of our present state, understanding of how and why we want to be different, adequate resources, robust enterprise risk management, leadership at all levels, coordination of projects that lead to breakthrough advances, and quality managers equipped to learn from the past as they adapt to the uncertainties of the future.

References and Note

  1. “Unlocking Success in Digital Transformations,” McKinsey and Co., October 2018, https://tinyurl.com/ulocking-success.
  2. Bert Spector and Michael Beer, “Beyond TQM Programmes,” Journal of Organizational Change Management, Vol. 7, No. 2, 1994, pp. 63-70.
  3. Transformation myths have fascinated readers since Ovid’s Metamorphoses. On magical thinking, see Richard Stivers, Technology as Magic: The Triumph of the Irrational, Continuum, 1999.
  4. The term “rebels against the future” is borrowed from Kirkpatrick Sale, Rebels Against the Future: The Luddites and Their War on the Industrial Revolution: Lessons for the Computer Age, University of Michigan, 1995.
  5. Shoou-Yih Lee, Bryan J. Weiner, Michael I. Harrison and Charles M. Belden, “Organizational Transformation: A Systematic Review of Empirical Research in Health Care and Other Industries,” Medical Care Research & Review, Vol. 70, No. 2, 2013, p. 2. (The author thanks Michael I. Harrison for calling out this research.)
  6. Bertalan Mesko, “The Top 5 Technologies That Will Change Health Care Over the Next Decade,” Market Watch, Feb. 25, 2020, https://tinyurl.com/healthcare-technologies.
  7. For a history of artificial intelligence, see Stuart C. Shapiro, “Artificial Intelligence,” in Anthony Ralston, Edwin D. Reilly and David Hemmendinger (eds.), Encyclopedia of Computer Science, fourth edition, Van Nostrand Reinhold, 2013.
  8. Juergen Brock and Florian von Wangenheim, “Demystifying AI: What Digital Transformation Leaders Can Teach You About Realistic Artificial Intelligence,” California Management Review, Vol. 61, No. 4, 2019, pp. 110-134.
  9. Ibid.
  10. Ibid.
  11. Ibid.
  12. Michael Beer, “Why Total Quality Management Programs Do Not Persist: The Role of Management Quality and Implications for Leading a TQM Transformation,” Decision Sciences, Vol. 34, No. 4, 2003, p. 625.
  13. Michael I. Harrison, “Limits of Lean—Transformative Care Redesign Must Go Beyond Typical Lean-Based Improvements,” NEJM Catalyst, Feb. 5, 2017, https://tinyurl.com/beyond-typical-lean.

Henry J. Lindborg is executive director and CEO of the National Institute for Quality Improvement in Fond du Lac, WI. He holds a doctorate from the University of Wisconsin-Madison and teaches in a leadership and quality graduate program. Lindborg is past chair of ASQ’s Education Division and of the Education and Training Board. He is a past chair and current member of the Institute of Electrical and Electronics Engineers Career Workforce Policy committee.

--Carol L Dixon, 05-31-2020

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