Editorial
The Gist
- AI is its own era. Unlike Digital Transformation 1.0 or 2.0, AI marks a fundamental shift, not just an evolution.
- Words become data. GenAI transforms customer interactions from proxies and metrics into direct dialogue.
- Legacy weighs heavy. Old Martech stacks and outdated mindsets remain barriers to AI adoption.
- New infrastructure. LLMs, RAG, and AI agents form a foundation that is adaptive, human-like, and intent-driven.
Digital transformation has been reshaping businesses for decades. But AI marks a new era. It is redefining how businesses operate. This isn’t just evolution. It’s a revolution. LLMs aren’t simply smarter software. They’re a new infrastructure.
From Evolution to Revolution
With that, we’ve entered a new era of transformation. AI isn’t the next phase of digital transformation. It’s a shift of its own. Arguably, separate from digital. Where technology once supported humans with the "job-to-be-done," AI now executes the "job-to-be-done" for humans. This is a fundamental shift. Businesses move from plumbers to choreographers. From operators to moderators.
And there’s more. AI transforms customer interactions from monologues to dialogues. It’s as if AI turned on the sound in a world of silent films. With GenAI, words are the new data points. Businesses that once relied on metrics, indirect proxies for customer behavior (such as age, location, purchased items), can now listen to customers directly, in their own words. Literally.
But there is a caveat.
Most companies carry their marketing technology past, or martech past, like an anchor, weighing down their ability to embrace AI. It is not only the legacy software. It is their legacy mindset. A mindset that dates back to the 1990s.
Even today, we see old thinking from digital transformation putting up barriers to fully embracing AI.
We’ve identified five critical differences that help you understand how to fully embrace all the benefits from AI transformation and detect your outdated mindset.
Evolution of Digital Transformation
This comparison highlights the major shifts across three eras of digital transformation, from early software adoption to today’s AI-driven ecosystems.
| Difference | Digital Transformation 1.0 | Digital Transformation 2.0 | Digital Transformation 3.0 (AI Transformation 1.0?) |
|---|---|---|---|
| Time frame | >1990s | >2000s | >2020s |
| 1. Purpose | Companies use more software (internal efficiency, cost saving) | Companies become software (external efficiency, making money) | Companies engage like humans (external effectiveness, making money) |
| 2. Process | Managing units; internal, homogeneous users; collecting company data; limited, stable features | Managing preferences; external, heterogeneous users; collecting customer data; trend-sensitive features | Managing intent and context; external agents as users; words as data points; ever-learning features |
| 3. Architecture | Closed systems; limited and stable requirements; adoption through implementation, documentation, and training | Open ecosystems; atomized, fluid requirements; adoption via MVPs and training | Autonomous ecosystems; requires clean data and trained LLMs; adoption through deploying AI agents |
| 4. Operating | Implementing internal software (ERP, finance, logistics) | Adding customer tech (CRM, MAP, CDP) and customer portals | Running and orchestrating AI agents (IntentLLMs, AudienceLLMs, etc.) |
| 5. Technology | Mainframes; on-premise software | SaaS; cloud platforms | LLMs; RAG; AI agents; autonomous infrastructure |
Table of Contents
- Digital Transformation 1.0: 'Companies Use More Software'
- Digital Transformation 2.0: 'Companies Become Software'
- Digital Transformation 3.0: 'Companies Engage like Humans'
- Core Questions Around Embracing AI Transformation
Digital Transformation 1.0: 'Companies Use More Software'
The Legacy of Digital Transformation 1.0
Digital Transformation 1.0 started in the 1990s, but for marketers, it is the least interesting phase. This stage wasn’t about customers. It was about “getting the house in order.” Companies focused internally, using software to drive operational efficiency across ERP, finance, procurement, HR, logistics and warehousing systems. It was a necessary step, but a limited one, centered on saving money, not creating value for the customer.
The process around software was fully under the control of the company. It revolved around managing internal units. Employees, a largely homogeneous user group, worked with stable, clearly defined software features. Data was collected for internal reporting, not to better understand or serve customers. Technology was designed for predictability, not adaptability.
Architecturally, businesses operated closed, on-premise systems with vendor-defined requirements and stable infrastructure. Adoption followed a command-and-control approach: implement the system, train the staff and expect compliance. This shaped how boards and leadership came to view software as something that could be fully controlled, planned and managed.
But this phase left a hidden legacy. It planted the idea that all aspects of software, including user behavior and adoption, could be engineered. It was reinforced by a rigid, cost-focused doctrine: technology was primarily seen as a tool for operational savings, not for creating future customer value. Costs show up on the balance sheet. Missed opportunities do not. That’s unfortunate for marketing, and so the hopes were high for the next transformation.
Related Article: Is It Time to Retire the Term Digital Transformation?
Digital Transformation 2.0: 'Companies Become Software'
The Pitfalls of Digital Transformation 2.0
If Digital Transformation 1.0 was about getting the house in order, Digital Transformation 2.0 tried to open the windows to the outside world. Starting in the early 2000s, companies shifted their focus to external effectiveness. They started digitizing customer interactions at scale using CRM, MAP, CDP and CDW solutions. Additionally, they also built their internally built customer portals with low-code or no-code platforms, allowing customers to renew products, submit support tickets or sell data subscriptions. This new software stack promised deeper customer connections.
With the rise of portals, companies didn’t just use software anymore. They were becoming software. The process moved from managing internal units to managing external customer preferences. Users became external and heterogeneous, demanding personalized experiences across a plethora of channels. Data exploded, moving from simple internal reporting to endless streams of individual customer data points. But while the data sources multiplied, so did the complexity. Features became trend-sensitive, driven more by engineers than by actual customer value.
Architecturally, companies embraced open ecosystems. SaaS and cloud platforms replaced on-premise systems. Requirements became fluid, mirroring customer preferences that shifted faster than companies could react. Adoption strategies also changed: instead of large rollouts, companies launched minimum viable products (MVPs) and targeted use cases. Iteration became the new strategy, at least in theory.
On the seller side, the mission was clear: add customer technology, and add it fast. CRM systems, marketing automation, customer data platforms, anything that promised a fuller view of the customer. Yet despite all this, most technology served internal reporting needs more than it created real customer value.
We could argue that Digital Transformation 2.0 fell into a dangerous trap. It continued the 1.0 mindset of measuring success through software adoption and utilization rates. That metric made sense internally, but not externally. You cannot surprise and delight customers by counting logins. You cannot create customer loyalty by tracking module usage. Digital 2.0 expanded the stack but left the true opportunity of building real relationships largely untouched.
Digital Transformation 3.0: 'Companies Engage like Humans'
Customer Interactions: From Monologues to Dialogues
If Digital 2.0 digitized customer interactions, Digital Transformation 3.0 is reshaping the very foundation they sit on. It’s no longer about stacking more software. A company stack is no longer a set of tools. A stack is a set of capabilities: a highly focused subset of requirements, features and use cases. In Digital Transformation 2.0, we learned that 20% of the features add 80% of the company value, but with the 1.0 mindset, we did not benefit from it.
AI Transformation 1.0 is about orchestrating intent and context through an entirely new infrastructure. Large language models (LLMs), retrieval-augmented generation (RAG), and AI agents aren’t just new tools. They are dissolving the boundaries between data, software, and action.
The process moves from managing preferences to managing intent and context. It’s not about collecting more data points. It is about understanding meaning through language itself. Words are the new data. Customers no longer need to fit into rule-based, rigid segments and workflows. Instead, external agents, buyer-side and seller-side, interact directly, fluidly and continuously learn from each encounter.
Architecturally, companies are moving from open ecosystems to autonomous ecosystems. Clean data and trained LLMs are the new infrastructure requirements. Success is no longer about deploying another system or MVP. It is about deploying AI agents as micro-SaaS into real-world business cases, buyer agents, seller agents and support agents, each operating independently but aligned with brand intent.
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On the seller side, the focus has shifted again. It’s no longer about implementing software or integrating applications. It’s about orchestrating AI agents that can interpret, negotiate and execute tasks across the entire customer journey, without handholding and without rigid logic trees.
For the first time, companies have the chance to move beyond proxy metrics and static workflows. That is, if they can remove the rigid, cost-focused doctrines we inherited from Digital Transformation 1.0, and the internal-focused adoption metrics from Digital Transformation 2.0. In Digital Transformation 3.0, the infrastructure itself becomes adaptive, human-like, and built around real customer intent, not just technology adoption.
Core Questions Around Embracing AI Transformation
As companies shift from digital transformation to AI transformation, leaders must examine whether their mindset and measures of success have kept pace. These questions help uncover where legacy thinking still holds back progress and where AI can create real value.
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