As AI continues to redefine the digital landscape, many companies are left wondering not just how to integrate AI, but how to reimagine their business around it.
Organizations are looking to digital transformation, which introduces its own set of questions:
- How expensive and time consuming is digital transformation?
- Who is in charge of accelerating a digital evolution?
- What will be disrupted in the process?
- Will future gains outweigh temporary pains?
But when you’re considering something as revolutionary as AI and machine learning as it exists today, the equation becomes increasingly complex.
That’s why KMS Technology spoke with AI entrepreneur and investor Dilip Dubey in a recent episode of So You Think You Can Dev?. We explore how AI is shifting from a technical add-on to a core driver of digital transformation.
Listen to the episode here, or keep reading to gain further, deeper insights.
Artificial Intelligence: A Paradigm Shift
What makes artificial intelligence of today different from years past?
Unlike the AI of previous decades, which was largely limited to rule-based systems and niche applications, today’s AI is powered by advanced machine learning algorithms and exponential increases in computing power. This has enabled AI systems to analyze vast amounts of data, recognize patterns, and make decisions with unprecedented accuracy and speed.
Modern AI is no longer confined to solving predetermined problems; it is now capable of learning and adapting, making it a versatile tool across industries. From predictive analytics in healthcare to supply chain optimization in retail, AI is driving innovative solutions that were once thought impossible.
This paradigm shift not only changes how businesses operate but also compels leaders to rethink their strategies, talent development, and long-term vision to stay competitive in an AI-driven world.
Generative AI and large language models have further changed business dynamics, with agentic AI on the horizon.
Generative AI Is Not Just Another Tool
Generative AI is not just a tool, but a force multiplier that can fundamentally reshape operations, revenue, customer experience, and innovation.
AI adoption isn’t about sprinkling in automation; it’s about rethinking the way businesses create value. He warned that many leaders miss opportunities by lumping AI in with traditional tech upgrades.
“This is a technology that is gonna fundamentally change everything in our world… labor, software, rules, regulations, how we live, how businesses operate.”
AI Transformation is Coming for Every Business
AI will not be isolated to Silicon Valley start-ups. It will become fundamental to midmarkets and enterprises as well.
“If a midmarket business is not looking to double their profitability using AI, they’re leaving money on the table.”
However, each business will face their own set of challenges and opportunities regarding their digital transformation efforts.
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With that in mind, organizations will need to adapt their AI approach based upon their spot in the larger market.
Let’s discuss best practices for adopting AI technologies that will best serve your business.
1) Drive Digital Transformation Differently
What is digital transformation?
Digital transformation is the integration of digital technology into all areas of a business, fundamentally changing how organizations operate and deliver value to customers.
It’s not just about implementing new technologies, but also about redefining business processes and modernizing approaches to meet evolving demands.
In the age of AI, this means leveraging intelligent systems that can analyze large amounts of data, automate tasks, and make predictions for informed decision-making.
However, the approach that organizations have historically taken to achieve digital transformation will no longer be sufficient.
Digital Transformation Isn’t What It Used to Be
Typically, digital transformation is structured around a roadmap approach , where companies identify specific areas that need improvement and implement a plan to address those needs.
While this approach may have worked in the past, it is no longer enough to truly transform a business for the future. With the rapid pace of technological advancements, organizations must be prepared to continuously evolve and adapt their strategies. Digital transformation is not a one-time project with a set end goal– it is an ongoing journey of innovation and improvement.
Dubey didn’t mince words when discussing the pace of change: digital transformation, as traditionally defined, is no longer enough.
“Digital transformation as it existed pre-AI is already dead.”
Instead of roadmap-driven transformation efforts that take quarters or years, Dubey advocates for what he calls “digitizing the transformation”—embedding AI into ongoing business evolution rather than treating it as a one-off project.
He also recommends a shift to agile principles, where value is delivered incrementally and continuously. These changes are necessary for businesses to keep up with the ever-increasing speed of technological advancements.
A strong digital culture within organizations can help. This approach involves fostering curiosity, innovation, and continuous learning among employees.
2) Start with ROI—and Redefine It for AI
ROI is critical for measuring success, but the old models are not well suited for AI tools.
While many executives evaluate AI projects with conventional ROI models, Dubey suggests this approach may limit innovation.
“If you look at traditional ROI models, you may actually abandon a project which could change your world.”
He recommends identifying one or two high-impact projects where ROI can be clearly measured—then using those wins to spark broader momentum across the organization.
What does this look like in practice?
Instead of looking at ROI generated by AI itself, look at your existing business processes and experiment with AI as an enabler, a force-multiplier, and see where it takes you. Automating routine tasks is one area. Working to optimize processes is another.
However, think of AI as an expansion of your workforce, where one business unit can achieve significantly more.
Measurements, like the digital transformation itself, are dynamic. When these expectations are baked into a digital transformation journey, the opportunities to create real, meaningful value increase.
In contrast, rigid ROI measurements based on pre-defined assumptions can easily undermine an organization’s competitive edge.
Four Strategic Areas for AI Impact
Dubey outlined four domains where AI is delivering outsized returns:
- Operational Efficiency – AI is “eating software and labor together,” which can drive cost reduction while reshaping internal processes. Tech stacks and routine tasks are a great place to start with AI adoption.
- Revenue Growth – New opportunities emerge to “sell better, market better,” and even create entirely new revenue streams. How can AI scale up your commercial operations? How can it enable new product lines and services?
- Customer Experience – Companies can now understand and engage customers in ways “not even possible before.” Better meet customer expectations with personalization and targeted marketing.
- Innovation at the Edges – Dubey highlighted examples where AI-generated insights created entirely new product lines—sometimes worth more than the original business. This is a golden opportunity for tech companies if they loosen the reigns and embrace AI solutions.
3) Prioritize Data Governance
For companies unsure where to begin: start with your data.
“Data is the fuel that drives AI.”
Without good data, AI systems will be limited. Data engineers will be more critical than ever in today’s digital age.
Most companies are dealing with imperfect data, and the following challenges are not uncommon:
- Raw data is not prepared or structured correctly
- There is no single source of truth, undermining the credibility of data driven insights
- Out-of-the-box models do not properly secure sensitive customer data
- Real time data analysis is difficult to capture and expensive to manage
While AI highlights the challenge, AI can also be a solution.
AI can help clean and structure imperfect datasets, making them usable for further applications. In fact, to manage data is a strong use case for AI and an easy win to start with.
From there, cross-functional collaboration is key—especially between IT and business stakeholders. Together, they can establish rules and standards for data collection, management, and usage. Consider the various funnels in which data is brought into the organization.
Prioritizing data governance ensures the accuracy, consistency, and security of data used in AI solutions. This is crucial as companies rely on AI to make important business decisions.
Additionally, investing in a strong data governance strategy can help mitigate potential ethical concerns surrounding AI. By establishing clear guidelines for how data is collected and used, companies can build trust with their customers and stakeholders.
Leverage Data Analytics for Business Operations
Data analytics serves as a powerful tool to optimize business operations and drive efficiency. By analyzing large volumes of structured and unstructured data, AI can help organizations uncover patterns, predict outcomes, and make informed data driven decisions.
This capability enables businesses to identify inefficiencies in workflows, streamline processes, and allocate resources more effectively. Furthermore, advanced analytics can provide actionable insights into customer behavior, helping companies personalize their offerings and improve customer satisfaction. Which can then be executed with AI solutions.
When implemented strategically, data analytics not only enhances operational performance but also fosters innovation, giving businesses a competitive edge in the marketplace.
Data is cyclical in this sense. It informs the development of AI, and AI can then analyze historical data drive digital transformation further innovation.
4) Foster a Culture of Innovation for Future Trends
To fully embrace AI, companies must foster a culture of innovation. This means encouraging experimentation and risk-taking within the organization.
Leaders and end users alike should become hands-on users themselves, pooling learnings together to identify the best opportunities for AI integrations.
Before scaling AI, digital leaders must understand the material, day to day impacts of intelligent automation, natural language processing, etc. Many students already use generative AI daily, but only a small percentage of business leaders do. Closing that gap could be the unlock for broader organizational adoption and true AI transformation.
But this culture must come from the top down.
Artificial intelligence and machine learning are not new. Neither is digital transformation. The pace and urgency of AI developments today are.
Empowering AI Evangelists
AI evangelists can drive the adoption of new technologies and help your organization more broadly integrate AI. They can support leadership in encouraging adoption on the frontlines, providing valuable insights based on their direct experience in using and experimenting with tools.
These individuals can serve as the bridge between technical teams and broader business units, advocating for AI adoption and its potential to drive efficiency and growth. Like wildfire, adoption sparks from one victory to the next. Automating repetitive tasks becomes the stepping stone to automate processes of increasing complexity–evangelists will vocalize their gains and draw the attention of their colleagues on the ground level.
Leadership must create space for these evangelists to test, and to even fail, in pioneering machine learning and AI in their areas of focus and passion.
Provide them with access to training and resources to deepen their understanding of AI tools and applications, ensuring they are equipped to lead informative discussions and workshops.
Additionally, foster a collaborative environment where AI evangelists can share insights, successes, and challenges. Encourage them to partner across departments to unearth new use cases for AI and develop pilots or proof of concepts. Your teams should be empowered to problem solve together.
Addressing immediate pain points is one of the most effective ways to get buy in and quickly generate ROI on a much larger scale.
By giving these advocates the support they need, including executive sponsorship and recognition, their efforts can inspire others and drive a culture of innovation that maximizes AI’s impact across the organization.
5) Start Forecasting Future Trends, But Keep the Fundamentals
As Dubey shared, the companies that start experimenting now will be in a far stronger position to adapt to future advancements.
“The people who are ahead of the curve are already gaining phenomenal value… People who start two years from now… may have a hard time catching up.”
And with AI accelerating development timelines—solutions that used to take years can now be replicated in weeks—the time to act is now.
As such, digital transformation will need to move at this rapid pace as well. That means flexibility, resilience, risk-taking, and ingenuity will be key.
On the horizon, organizations will see the rise of technology such as:
- Agentic AI
- Digital twins
- Advanced Low Code / No Code
- Fully automated workflows
- Deep research
- Advanced Generative AI
- Augmented reality
And with it, new models, tools, and technologies will be developed. The future can be overwhelming and uncertain, and many organizations will hold back to evaluate risk as early adopters test new solutions in the market.
But organizations cannot wait if they want a competitive advantage. Even more critically, AI talent is in short supply. Orgs cannot expect to quickly staff up on AI expertise once the gains are monetarily apparent. Integrating AI into the organization now is the best way to build expertise internally.
As such, organizations should keep a pulse on new developments and be willing to try them out. But do not wait for the technology to be perfected, or for the ideal solution to materialize. Now is the time to take up the mantle of AI.
Maximize your Digital Transformation Initiatives
AI is not just a technology shift—it’s a business shift. And while disruption is inevitable, Dubey’s optimism is grounded in opportunity:
“You just have to step out of that comfort zone… You will be surprised how easy it is to really start leveraging AI without knowing any tech.”
Organizations are positioned to benefit, provided they move quickly, focus on outcomes, and align their business and technology teams. As Dubey puts it, “get your businesses to buy in… and try these projects for real value.”
To sum up, the critical elements of digital transformation when it comes to AI may be different than those in the past. Incremental improvements are not the benchmark for success. The ROI of AI is self-evident when your business embraces a more dynamic approach.
Additional Resources to Learn More
- Maximize EBITDA with an Effective AI Roadmap: With this eBook, learn how to leverage Generative AI for product development and strategic growth—https://info.kms-technology.com/optimize-ebitda-with-effective-ai-roadmap
- Agentic AI: The Next Evolution in Intelligent Aggregation: Understand the next phase in emerging technology: Agentic AI.—https://kms-technology.com/emerging-technologies/ai/agentic-ai-the-next-evolution-in-intelligent-automation.html
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