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Introduction
Digital transformation is a multi-year journey that enables an organization to modify existing offerings into digital workflows. McKinsey He puts it succinctly when he describes digital transformation as “the realignment of an organization to create value through the continuous deployment of technology at scale.”
There are several words here that require attention. Restructuring is key, as it fundamentally changes the way operations are run on legacy applications and processes. The next goal for CXOs is to create value, which involves opening up new revenue streams, improving current operations, or creating a differentiator by enhancing the customer experience. And all of this is possible if the right use of technology is leveraged at scale.
At first, it can seem overwhelming, with a variety of possibilities preventing you from taking the first step. Therefore, it is critical to have a visionary perspective with a strong business and execution mindset to go beyond the next big thing and instead focus on building a data-centric culture.
You might be wondering: why am I promoting a data-centric culture in the world when everyone is aiming to be ai-centric? I will discuss this in detail in this post.
The challenge of legacy systems
Speaking of legacy systems and processes, let’s discuss how this outdated software and hardware that may be deeply embedded in an organization’s operations poses a greater challenge when integrating with modern applications and platforms.
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Some modernization strategies focus on gradually upgrading components in a phased manner, while in other cases, complete re-engineering is the best option. Replacing legacy systems involves developing new applications from scratch that can meet current and future business requirements.
The next important part of digital transformation is the scale that comes with the cloud. Moving legacy applications to the cloud provides the much-needed scale and flexibility with additional benefits that are difficult to achieve with on-premises infrastructure, such as:
- Improved performance
- Cost savings
- Enhanced collaboration
- Advanced security, such as encryption, identity and access management, and threat detection.
Overcoming challenges beyond legacy
No digital transformation is easy. The word “transformation” implies change, which often leads to resistance to change, sometimes due to a lack of digital skills. Therefore, companies must design change management and training programs to promote a continuous learning mindset.
If there is one crucial skill we must adopt in today's rapidly evolving world, it must be the ability to discover the latest technological innovation.
Data and customer orientation
The effectiveness of any digital transformation program depends on the quality, availability, and accessibility of data. A data-centric culture places data at the center of every decision-making process, where accurate, complete, relevant, and timely information drives every decision.
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The next big obstacle affecting the rate of digital growth is data silos. As digital tools and technologies mature over time, the world is quickly becoming obsessed with personalization, which is best achieved with a 360-degree view of the customer journey.
It involves implementing omnichannel strategies to analyze their behavior across multiple touchpoints, allowing businesses to leverage data analytics to meet their preferences.
Transformation in the world of ai and generative ai
Amidst all the ai frenzy and hype, one crucial piece is often missing: data. So, building a solid data foundation is the first step to leveraging advanced analytics and ai, which help analyze large data sets and identify patterns that drive strategic business decisions.
ai fatigue is real, not just for us but for ai veterans as well. That’s why separating the signal from the noise becomes crucial to building a profitable business.
To align any initiative with your organization's strategic objective, consider asking the questions, starting with because:
- Why are we doing this? Why should someone work on this initiative and not any other? These questions will help analyze the opportunity cost and uncover crucial factors such as return on investment, associated risks, applicability and reliability of the solution, among others.
- Next come the “what” questions. What will it take to get there? What is the business goal and metric for declaring success?
- How to solve the problem? Can it be solved through automation or would an algorithmic solution work better? Is there a way to solve this problem currently? What is the benchmark? Evaluate if there is a pattern in the process? What is the data needed to model it? How to design the system to integrate with the current technology stack?
- If you’ve made it this far and have looked at how to use ai correctly to drive business growth, you’re well on your way to ai transformation. And that’s where another key element comes in that most people neglect: ethics. Imagine you’ve solved a complex business problem that was a perfect product-market fit, but you scrapped the project simply because ethics weren’t built into the design. So it’s important to create a risk assessment framework and the necessary guardrails to make the system fail-safe.
We all have a role to play
Finally, a successful transformation is a collaborative effort that begins with building a strategic roadmap and must be communicated across the organization to ensure everyone is aligned on the common goal.
This is not a one-off project, but rather an ongoing process that requires continuous adaptation and evolution. Organizations must therefore foster a culture of innovation that promotes new ideas. Turning failures into learning opportunities encourages teams to think outside the box, which is essential for building an organization prepared for the future.
Vidhi Chugh is an ai strategist and digital transformation leader working at the intersection of product, science, and engineering to build scalable machine learning systems. She is an award-winning innovation leader, author, and international speaker. Her mission is to democratize machine learning and break the jargon so everyone can be a part of this transformation.
