Digital business transformation (DBT)—the ongoing process of reimagining organizations through digital technology—has always been more evolution than revolution, despite how it's often portrayed. For over a decade, companies have been steadily digitizing operations, customer experiences and business models through carefully planned, multi-year journeys.

Now, AI has entered the transformation landscape, promising to accelerate and amplify these efforts. But this latest shift is both a new technology and an accelerator of new technology itself—an era of DBT that can’t be treated like others. Quarterly upgrades have become daily upgrades in the compression of time in the AI arms race.

In this article, we’ll explore how AI should evolve and enhance your DBT strategy across all functions, moving beyond cost-cutting to creating new value and reshaping customer engagement through conversational interfaces—all while building upon rather than replacing your existing digital foundations, data and domain expertise.

DBT powered by AI, also known as AI transformation, is often introduced into corporate strategy like a fire alarm. For example, the CEO returns from the season's must-attend tech conference, summons the leadership team and declares with barely contained urgency:

"We need to pivot to AI immediately."

The CTO exchanges knowing glances with the digital transformation lead. Here we go again.

Despite three years building digital infrastructure, modernizing legacy systems, integrating data warehouses and standardizing APIs, their robust transformation plan is rendered obsolete by a fear-based reaction to the latest technological revolution.

"But what exactly does 'pivot to AI' mean?" someone inevitably asks.

The ensuing chaos reveals an uncomfortable truth: every person has different ideas about what this pivot to AI means, nobody wants to admit the extent of their uncertainty and the pace of change at the individual level is untenable at the organizational level.

When it comes to AI, executive teams are often like planets orbiting different suns—each moving at their own pace, following different paths and occasionally colliding in meetings. This leadership alignment paradox is what happens when AI meets organizational reality.

Here's what happens: Instead of aligning on a “north star” vision for transformation with the help of AI, the leadership team begins to build a conflicting solar system. The CFO presents an AI vendor proposal promising 40 percent reduction in administrative costs. The COO argues they should wait for more proven solutions. Three department heads disclose they have already launched AI pilots without informing IT security.

This paradox isn’t lacking strategy. It's lacking alignment—a fundamental issue that has always existed in previous DBT initiatives but has now reached its boiling point.

In contrast to previous technology shifts where roles and responsibilities remained relatively stable, AI is redrawing organizational charts in real time. What does this mean? Leaders need to know more about their colleagues' functions, and complex technology, than ever before. The marketing director now needs to understand prompt engineering. The compliance officer needs to interpret emerging AI regulations. The customer service manager has to retrain staff to work alongside AI assistants. This unprecedented organizational transformation stems from AI's unique ability to evolve cognitive work across all business functions, creating necessary interdependencies between traditionally siloed departments.

So far, organizations have struggled. According to Publicis Sapient research, nearly 60 percent of CEOs believe chatbots and other generative AI tools will revolutionize customer service, compared to just 24 percent of customer service executives actually managing those functions.

Unlike the tangible applications and implementation pathways of the dot-com bubble, AI creates deeper misalignment between executives and operational leaders because its theoretical capabilities remain ambiguous and far ahead of practical implementation realities. This leads to inflated expectations at the top and pragmatic caution among those responsible for execution.

Here's the gist: Your corporate fantasy of "north star" planning will collide spectacularly with AI's ungovernable reality—rendering PowerPoint roadmaps instantly obsolete. Every organizational role will undergo reinvention, though with varying existential stakes, demanding a shared technological literacy that our siloed structures fundamentally resist. The cognitive dissonance needs to be acknowledged: executives speak confidently of strategic alignment while standing on constantly shifting ground. You’ll need to maintain a laser-focus on your “north star” vision, but the “north star” vision must be malleable enough to morph with the changing AI landscape.

Another shift in the AI-volution of DBT is the convergence of distinct customer channels into seamless conversations.

For years, enterprises have maintained separate teams for websites, mobile apps, call centers and physical locations. Each developed different metrics, technologies and leadership structures. Customer journey maps revealed numerous friction points where customers repeated information when moving between channels. The traditional response was optimization—making each channel incrementally better while working to reduce handoff friction. AI enables a fundamentally different approach: the dissolution of channels entirely.

What happens when there are no channels at all? What if every interaction is simply the continuation of an ongoing conversation?

While traditional digital applications could theoretically store customer data across channels, AI makes it realistic:

• First, AI can understand the semantic meaning and context of natural language interactions, not just store data points, allowing it to grasp nuanced customer intentions across different communication modes
• Second, AI can process unstructured data (conversations, emails, voice) in real-time and convert it into meaningful insights without requiring structured inputs or forms
• Third, AI enables a natural language interface that works consistently across all channels, eliminating the need for customers to adapt to different interaction models (like IVR menus, web forms or app interfaces) as they move between touchpoints, creating a truly unified, conversational experience rather than just connected—but  still distinct--channels

The most innovative organizations are implementing what could be called "continuous engagement" models. A customer begins a transaction through any entry point—voice, text, app or in person—and the context follows them seamlessly. An insurance claim started with a virtual assistant continues uninterrupted when the customer calls or visits an office.

The technical underpinnings of this transformation include advanced large language models (LLMs) with improved context windows, allowing them to maintain conversation history across days or weeks. Companies are implementing sophisticated "context stores" that maintain the full history of customer interactions across touchpoints, enabling any interface to pick up where another left off.

Here's the gist: In our algorithmic present, leaders will perform an organizational exorcism—banishing the haunting silos of website teams, call centers and app developers that have structured digital enterprise for decades. The conversation itself becomes sacred architecture, with customer voice as organizing principle rather than the channel bearing it.

This transformation demands ritualistic sacrifice of our long-fetishized metrics—conversion percentages and efficiency numbers—replacing them with holistic measurements capturing conversation quality and true resolution time across a single customer's digital pilgrimage.

It’s a perfect irony: deploying artificial intelligence to recover authentic human connection, transcending and deepening our entanglement with the technology that necessitated this recovery. 

AI transformation is an evolution not a revolution, and so is its technological architecture. Resilient companies aren't immediately taking sweeping actions to replace platforms but instead adding intelligent layers that work with existing systems.

When enterprises evaluate their technology readiness for AI, the assessment is typically sobering. Most legacy architecture resembles archaeological layers: mainframe systems from the 1980s, client-server applications from the 2000s and a patchwork of cloud services added over the last decade.

Conventional DBT wisdom suggests a massive modernization program—but that could take years most organizations don’t have. And given the more than daily advancements in LLMs and AI approaches, it would be counterintuitive to progress.

A more viable approach is what technologists call an "agent mesh architecture." Unlike traditional DBT efforts that require complete system overhauls, AI agents can be deployed as specialized intelligent layers that interface directly with existing infrastructure—from mainframes to cloud services—performing specific functions like optimizing routing or managing customer communication without disrupting core operations. One agent optimizes routing algorithms. Another predicts maintenance needs. A third manages customer communication.

These AI agents communicate constantly, creating an adaptive network that's greater than the sum of its parts. When a weather forecasting agent detects a storm, it automatically notifies routing and customer communication agents, which adjust accordingly.

This multi-model architecture where organizations maintain "agent libraries" alongside "model gardens” can include agents from vendors like Salesforce, Microsoft or Adobe, while others will be custom-built. The models themselves will vary based on the task—from large language models to smaller specialized models. All of these components need to be powered by high-quality data products, both first-party and third-party.

For example, a logistics company implementing this architecture by creating weather, routing and communication agents that work together could automatically reroute deliveries during storms. This could reduce weather-related delays within six months.

The results from early adopters are striking: system development time is decreasing with projects that once took quarters now completed in weeks. Incident response times and operational exceptions are dropping.

Here’s the gist: Between CTO aspirations and implementation reality emerges a new AI architecture doctrine: diverting innovation funds toward targeted projects promising visible results within months. This isn't abandoning those "north star" visions—it's subordinating them to the unglamorous work of cross-functional teams mapping intersections where algorithms might actually enhance existing systems and solve genuine problems.

The governance frameworks perfectly encapsulate our contradictory relationship with AI: structures acknowledging its uncontainable organic growth while simultaneously imposing necessary constraints of data quality and security—a choreographed dance of unleashing and controlling. As leaders, we have to celebrate incremental improvements while foundations shift beneath us, letting our domain expertise and data lead. 

While these DBT evolutions (north star leadership alignment, continuous engagement models and portfolio-based AI adoption) will deliver impressive operational gains, AI transformation also creates an additional challenge. When efficiency improves, the human dimension of business becomes critical.

Unlike previous technological advancements that merely facilitated human connection while keeping humans central to interactions, AI uniquely substitutes core cognitive functions and decision-making processes that form the basis of meaningful relationships.

This fundamental replacement capability creates an unprecedented tension where efficiency gains come at the perceived cost of authentic human connection, explaining why customers and employees fear losing valuable personal interactions during significant decisions rather than just worrying about technical accuracy.

DBT powered by AI isn't just about efficiency. It's about redirecting human energy toward our ethos, our ethical judgement and our strategic thinking.

The golden standard of AI transformation isn't just the most advanced AI technology. It's the journey of rediscovering what makes your company valuable in the first place and using AI to deliver that value more effectively than ever before.

DBT powered by AI: AI isn't changing what matters in your business—it's stripping away everything that doesn't matter and potentially gobbling up your own business model to focus on what does matter.

In the end, AI transformation is an evolution that builds upon digital foundations while revealing the essence of what makes organizations uniquely valuable: domain expertise and data. The winners won't be those who simply adopt the technology fastest, but those who use it to rediscover, evolve and amplify their fundamental purpose.