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Avoid the Most Common Stumbling Block When Transitioning to a Technology Company

By Mario R. Garzia

In its 2018 global markets outlook report, T. Rowe Price added a new section on technology innovation and disruption to recognize the increasing importance of technology across all industries. The firm cites big data analytics and artificial intelligence (AI) among key trends that drive value and disruption. Companies leading the charge with these technological trends have had outsized success; this group presently includes the four largest companies by market cap, as well as the first trillion-dollar companies. These powerful trends have enabled relatively new entrants to disrupt a myriad of well-established areas, including finance, transport, retail, automotive, hospitality, entertainment and even space exploration. The 2019 KPMG CEO Outlook survey most commonly cited emerging/disruptive technology as the greatest threat to the organization’s growth.

Most companies are well aware of the current digital transformation taking place and the subsequent need to adapt to and leverage new opportunities. Over 70 percent of Fortune 500 company CEOs now say that their companies are “technology companies,” and Deloitte’s second biennial global cost survey shows that 61 percent of global respondents consider digital disruption to be a top corporate risk. However, the quest to transform themselves into technology companies is very slow, costly, and difficult for many organizations. As they struggle to incorporate new digital and data-based technologies, the technology space continues to evolve at an exponential pace; this further increases organizations’ technology gaps and puts them at an ever-increasing competitive disadvantage. The gap eventually becomes insurmountable, and more able competitors step in and take market share. Companies that successfully leverage the technology and data trends are growing stronger and reaping the bulk of the benefits of innovation, while the rest fall behind. 


There are plenty of challenges that complicate an organization’s successful move into the digital age. At the same time, new technical developments have reduced the barrier to entry in almost all areas of endeavor, and new market entrants readily disrupt incumbents. Examples include the rise of ride-sharing companies, which put pressure on traditional taxi services and sometimes even cause them to shut down; online retail’s impact on brick-and-mortar stores; fintech innovation forcing financial behemoths to take notice and adapt; big car manufacturers working to keep pace with car startups; and even NASA competing with private companies in space exploration. New regulations—such as the European Union’s General Data Protection Regulation and the California Consumer Privacy Act—require organizations to develop strong data governance and security policies and procedures, and ethics committees to ensure that customer trust is maintained.

The rapid advance of technology has been particularly challenging for information technology (IT) departments that have had to quickly adapt to the rise of mobility; technology democratization; and software and architectural changes that support evolving needs, such as a move to microservices or continuous intelligence. These departments have moved to structured and unstructured data warehouses and data lakes, and transitioned from in-house data centers to cloud computing and then serverless computing. They play a never-ending catch-up game with corporate demands for processing and storage, which grow exponentially to support expanding analytics and AI modeling needs. The importance of these technological developments has placed IT groups in the corporate spotlight, transitioning them from internally-focused service organizations to strategic partners that drive a company’s transformation and future success.

Companies also face plenty of analytical challenges beyond the oft-cited difficulties in growing their skill sets in data science, AI, and deep learning. They must bridge corporate data silos and contend with legacy systems and data quality issues to properly handle the dispersed and diverse data sets that exist throughout the organization. Organizations need to increase the availability of end user data, which might be shielded behind a distribution channel partner. They have to handle issues related to data with high dimensionality, understand the limits of their “raw data,” and ensure the accuracy of their models while avoiding common problems like model overfitting. While data bias, deep fakes, and AI explainability are still active areas of research, companies nonetheless must find ways to guard against them and minimize the chances of falling prey. A U.K.-based energy firm whose chief executive's voice was impersonated using AI to demand a large funds transfer is one such example.

Yet the biggest challenge is often overlooked: in order for transformation to a tech-savvy firm to succeed, the people and culture need to be aligned with the change. Culture issues can be significant, even for seemingly successful transformations underway (see Bloomberg’s article on Walmart’s culture clash). A recent Deloitte study assessed the state of business analytics and found that nearly 70 percent of executives are uncomfortable using insights from data analytics. The study concluded that corporate culture is the biggest roadblock to leveraging analytics. Similar results have been echoed by other studies. For example, a recent NewVantage Partners study found that 77 percent of respondents indicated that business adoption of AI and big data initiatives continue to pose challenges, and that the problem is people rather than technology. Participants in an MIT Sloan study reported that the biggest obstacle to digital transformation is lack of urgency, which they simply summarized as complacency. Research by Clayton Christensen shows how even well-meaning, well-managed companies can fail to adapt to new technologies and succumb to outside disruption. This work—together with the work of Rebecca Henderson and Kim Clark —points to existing corporate structure, processes, and competencies as the main causes of failure for otherwise-successful organizations. So, while many challenges exist on the journey towards becoming a technology company, the first and most important issue to overcome is the alignment of people and culture to new technology goals.

Transformation Starts with People and Culture

My experience working with several large and mid-sized companies is consistent with the aforementioned research and suggests the following key steps to successfully adapt an organization.  

Assess the Opportunity and Develop the Vision

Digital transformations often begin with limited commitment and no clear business purpose, simply to “test the waters.” Progress with this approach tends to be very slow and frequently leads to wasted effort and failure, all while the organization continues working as usual. Successful transformations result from careful assessment of business opportunity and risks, highlighting the need to adapt to new trends that impact the market and facilitate the development of a lucid vision statement for the future. The assessment can be carried out by leadership or conducted at a grassroots level, but ultimately leadership must own the results and vision. A thoughtful assessment accounts for potential disruption by new entrants, which is easily overlooked if a company is only gauging progress against established competitors who are now struggling and working towards becoming technology companies themselves. It should consider the use of technology to not only improve current products and processes, but also look for opportunities to innovate (which is where disruptors are likely to arise). 

The 2019 Fortune 500 CEO Survey reports that 60 percent of respondents said that they use AI to improve efficiency and reduce cost, while only 22 percent use AI for product innovation. While there is clear value in increasing efficiency and reducing cost, there may often be even greater opportunity in doing very different things in very different ways that can disrupt the market or enter an entirely new space. I have met with companies that work hard to improve their current product line but miss the opportunity to leverage their assets and enter what could ultimately become a much more lucrative market. A recent MIT Sloan and Boston Consulting Group survey states that 70 percent of companies that invest in AI have little or no impact to show for their efforts. The successful ones view AI as a way to upend their current business practices.

Build the Plan and Commit to the Change

With the assessment and vision in place, it is important to develop a clear plan and understand that while the approach and intermediate goals will change based on successes and failures, commitment to the new vision is unwavering. Assessment of business opportunities and risks associated with the evolving digital landscape should demonstrate that what made the company successful in the past will not necessarily ensure continued success in the future. 

The plan should include changes to corporate processes, including human resources. Such changes ensure that the needed skills—hiring, reporting structure, rewards system, employee training, etc.—are nurtured and funding mechanisms are in place to support the growth of nascent areas, which at times may come at the expense of traditional project areas.

Engage the Organization

Communicate the plan and build support across all areas and levels of the organization, starting at the top. Companies cannot simply delegate the transformation to underlings or relegate it to a small group. Everyone must understand the plan and be part of the journey, from the senior-most executive to the recent hire. Roles vary for each person, but all employees must know their roles and understand that they have a stake in the change. 

Proceed with Urgency

Once the pieces are in place, proceed with urgency to drive through the plan and deliver on the vision. With each additional delay, the technology gap and potential for new disruptive entrants grows bigger and harder to bridge.

Mario R. Garzia presented this work during a minisymposium presentation at the 2019 SIAM Conference on Computational Science and Engineering, which took place earlier this year in Spokane, Wash.

This post originally appeared on the Data Powered Future blog. It is republished with permission and a few light edits.

Mario R. Garzia is president of DIGA Group consulting. A data science and big data executive, his past experience includes senior and executive level research and development roles at Microsoft and AT&T Bell Laboratories. Garzia holds a Ph.D. in mathematical systems theory from Case Western Reserve University. His M.S. and B.S. are in mathematics.
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