Inside Digital Transformation: 3 Key Lessons for Success

As digital transformations continue to mature, we – the authors of this article - are in the forefront of driving these growth initiatives for large enterprises across a wide range of industries. As both advocates and implementers, we have the opportunity to witness organizations adapting to the paradigm shift brought about by emerging technologies and processes, such as SaaS and AI. This journey proves to be exhilarating, although not without its share of challenges and valuable lessons learned.

Drawing from our business and technical experiences, we are sharing some hard-earned insights with you – the IT and Business leaders trying to navigate the exciting but formidable landscape of digital transformation. In this article, we extracted and are sharing three key lessons distilled from our insights:

1. Define the Why,
2. Seize the power of digital technology, and
3. Exercise active leadership

Define the Why: The Beacon

Many digital transformation initiatives begin by addressing the ‘What’ question, “What can we do with this <AI/ IoT/ AR / 5G/ ..> technology to improve our business operations?”?and subsequently come up with a respective plan to address. For example, in one of our early digital transformation projects of a major industrial manufacturer, we embarked on an ambitious initiative to implement Industrial IoT within its shopfloor operations, intrigued by its potential and the buzz it was generating in the industry. Without a clear business outcome in mind, and without a clear use case, we found ourselves adrift in a sea of possibilities, unsure which aspects of the operations would benefit most from this technology. This resulted in an unfocused project objective and inconsistent stakeholder collaboration. The project consumed significant resources, both in terms of time and budget, with our team members often finding themselves working at multiple objectives due to the lack of a unified purpose and a respective set of success measures.

Eventually, we did manage to implement the technology in some operational areas, but the project went over budget, took longer and fell short of anticipated results, missing both expectations and opportunities. It is unfortunate that this result is actually quite common in the industry, as market research shows (see Why IoT Projects Fail ).

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This experience served as an internal wake-up call, with the lesson that focusing on a technology's promise is not enough; it's crucial to first define the business purpose - the Why. Only then can you align technology adoption with specific business objectives. Along with that you will also need to specify accompanying measures and metrics that provide a quantifiable way to evaluate the success achieving those objectives. These are essential in the journey to gauge progress, inform decision making, and drive continuous improvement towards achieving the said business purpose.

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To illustrate a ‘Why’ definition, in a subsequent transformation project, we started by exploring the business and operational challenges faced by another world-class leading manufacturer. It was defined that our primary objective should be to improve the financial margin of their production facilities, and following further analysis it was identified that enhancing the availability of their production systems may yield a margin step-up of several million dollars annually per factory.

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Guided by this specific objective and defined measures, we introduced a digital solution harnessing IoT data and AI technologies, that were configured to connect and integrate a specific subset of data sources and allowed us to fine tune the exact AI algorithms to support an accurate prediction of imminent downtime. Moreover, the solution did more than address availability of their production processes, it also significantly increased transparency across the entire shopfloor, which in turn streamlined their operations and supported better informed decision making.

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Shared Lesson: Start with defining the Why, a clearly articulated business objective and supporting measures to serve as your beacon. This will aid in evaluating progress and success. This case, among others, provides valuable insights into the complexities of ‘Effective AI implementation’, a fascinating topic that merits its own discussion.

Seize the power of digital technology: The Catalyst

As architects of digital transformation, we recognize technology as a perpetually evolving enabler that can unleash the growth potential of a business and even disrupt complete markets and ecosystems. As we observe the rise of various technologies, from the advent of Cloud computing to the surge of Machine Learning and Generative AI, one theme that consistently applies is 'Adapt and learn.'

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Cloud technology and SaaS are great examples of significant initial game-changers allowing companies to focus on their core competencies and achieve an improved set of business and operational measures, including scalability, cost-effectiveness, and accessibility. Given those measure of success, it is often that we guide companies to minimize their heavy reliance on traditional on-premises servers, which are not only costly to maintain but also lack the flexibility and scale to support more advanced processing, such as advanced data analytics and AI/ML technologies.

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However, Cloud and SaaS technologies have unlocked even greater business value. A widely recognized challenge many organizations face is data fragmentation, a consequence of using various software applications across multiple departments. This issue hampers optimization, leads to considerable decision-making delays, and impedes effective collaboration among departments.

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By offering a unified, comprehensive platform, Cloud-based SaaS solutions consolidate various data management features and tools into one streamlined system. This cohesive solution provides organizations with real-time access, seamless data integration, and the ability to foster cross-departmental collaboration by leveraging data across different regions and cloud providers. The centralized framework also aids in data governance and security, ensuring that data is safeguarded and accessed solely by authorized personnel.

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Further, Cloud technologies provide the ability to scale computing resources up or down based on demand. This scalability is particularly important for data analysis tasks that involve large datasets or complex AI/ML models. The leading cloud providers offer advanced AI/ML capabilities, such as pre-built models, automated machine learning tools, and data analysis frameworks. These services empower organizations to capitalize on state-of-the-art AI/ML techniques without requiring in-depth expertise.

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We had the opportunity to work closely with an international manufacturer that faced challenges arising from separate cloud implementations in their regional facilities. The company targeted optimization of their production operations by harmonizing their data across factories, and consequently enabling data analysis and machine learning models to be applied. However, complexities arose due to data repositories spread across different cloud vendors, impeding efficient data analysis and optimization across the organization.

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To address these challenges, our implementation strategy focused on leveraging the unique strengths of each cloud platform. We seamlessly integrated diverse data sources by employing data federation techniques, including data lakes, ad-hoc schema creation, and portable AI/ML containerization. By combining these capabilities, we ensured efficient data storage, indexing, and retrieval. The resulting amalgamation of cloud and SaaS capabilities provided the manufacturer with the desired agility, efficiency, and flexibility in their AI/ML workflows and data analysis. As a result, they were able to optimize operations both within individual factories and across the organization, while achieving significant cost saving, repeatable production processes, ensuring quality and optimized operations.

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Shared Lesson: Despite the perceived complexities, cloud platform services enable the portability and operation of data and analysis models, facilitating the management of multiple cloud and data repositories. Enterprises striving for comprehensive business and operational insights across their diverse SaaS applications, such as SAP, SFDC, and Microsoft BI, must integrate and analyze data across multiple cloud platforms. Exploring methods to federate data for in-place analysis and aggregate results becomes imperative to eliminate the need for data transfer and reprocessing.

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Exercise active Leadership: The Imperative

From our experience, we’ve learned that digital transformation necessitates more than just technology and objectives; it demands an organizational cultural and behavior shift. Active leadership plays a pivotal role in inciting this change. As leaders, we’ve jointly set the tone for our teams, promoting for example a Digital-first or Cloud-first mindset, and fostering a culture that is ready to embrace change.

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Yet, we also should realize that there are some common challenges, such as resistance to change, lack of alignment, and the need for ongoing support. In order to effectively address those, and successfully drive transformation and growth, the leaders of the organization need to apply and actively support specific behaviors, traits, and methodologies. Some of the key ones are:

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1. Visionary Leadership: Leaders must set clear, motivating visions aligned with the business strategy, inspiring teams to embrace digital transformation.
2. Effective Communication: Leaders must communicate the benefits and purpose of digital transformation clearly, eliminating ambiguity, and inspiring confidence in the team.
3. Promoting Empowerment and Collaboration: With digital transformation impacting all departments, leaders need to break down silos, fostering collaboration by aligning towards common objectives and incentives, and empowering the individuals, teams, and departments to make decisions that help achieve the common goal.
4. Agility and Resilience: Digital transformation is challenging and unpredictable. Leaders must show agility and resilience, adapting to changes, pivoting strategies as needed, and encouraging teams to learn from setbacks.
5. Encouraging Innovation: Leaders need to cultivate a culture where creativity is rewarded, risks are taken, and teams are empowered to think innovatively.

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One such example of successful active leadership is a case of a large $B software business, which went through a digital transformation including transition to SaaS and expansion of new Online Channels and a Marketplace. The software portfolio was spread across numerous business units and product lines, and the complexity of the task at hand was immense. In some cases, we were navigating uncharted waters, dealing with numerous stakeholders, each with their unique needs and challenges. This digital transformation ultimately involved reimagining the entire business and P&L model. That included redefining the customer relationship, applying Land, Adopt, Expand, Renew (LAER) strategy for Sales and Customer Success, and in a way, reshaping the organizational culture.

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From the onset, clear and assertive leadership was vital, and indeed the process began with a clear executive directive. This vision was communicated repeatedly at all levels, ensuring that everyone was aligned with the goal. Cross-functional teams were formed, covering each unit’s unique needs. Regular updates were provided to the teams, coupled with enablement for the new business environment. Eventually, despite the typical resistance to change, we were able to successfully address the concerns through ongoing dialogues and workshops, and consistently highlighting the new model’s benefits for the mid-long term.

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Shared Lesson: ?Ultimately, the transformation efforts demand a top-down cultural shift, energized by active leadership. This decisive approach not only propels a successful digital transformation, but also ignites the organization's growth potential, forging a path towards a more customer-centric, agile, and innovative future.

Looking Ahead

As we peer into the future of digital transformation, several key trends are emerging that will further shape our businesses and society. Of these, the rise of Generative Artificial Intelligence (Gen AI), with the upcoming private large or micro language models, represents a sea change with far-reaching implications. This advances beyond existing AI applications, employing AI systems that can make informed, context-driven decisions. This progression leads to heightened efficiency, cost reduction, and enhanced agility, propelling us towards the realization of autonomous, closed-loop systems and operations.

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Further, we already witness a significant shift towards innovative business models, including DaaS (Data-as-a-Service), consumption-driven, outcome-driven, and marketplaces. All unlocked by an exponential amount of data collected, analyzed and leveraged by companies and ecosystems. This cannot be underestimated. Data is becoming a critical asset and drives strategy since it empowers organizations to make data-backed decisions, gain a competitive edge, understand customers, improve operations, drive innovation, and manage risks effectively.

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In conclusion, digital transformation is a journey, one that we find to be both challenging and immensely rewarding. Our experiences teach us that success is anchored in a clear definition of Why, seizing the power of the digital technology, and exercising active leadership. We are in a truly exciting era, and we hope these shared lessons may serve as a blueprint for executives and leaders steering digital transformation towards business growth and success.

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?? Call for Action

Thank you for reading this article. Please comment to provide feedback, share additional key lessons, and/or suggest any article topic for further knowledge sharing and discussion.

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About the Authors

Yaron Riany is a seasoned business executive recognized for driving growth and digital transformations on a global scale. Throughout his career, most recently as Vice President at Siemens, he successfully grown and scaled innovative businesses, built products from the ground up, and led high-performance teams in both corporate and agile startup environments.?Read more and reach out on Yaron’s LinkedIn page .

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Mike Denley is an experienced thought leader with expertise in planning, developing, and executing Industrial IoT product programs in globally distributed organizations. He has a diverse background in software engineering and holds several degrees in business, marketing and computer science. Read more and reach out on Mike's LinkedIn page and on his blog .

Photo by Agence Olloweb on Unsplash