What is Transformation in the AI Era?
Transformation is one of the most overused words in business. When companies announce they are “in transformation,” it evokes a range of reactions—from enthusiasm to anxiety to sheer fatigue. Some companies even hire a full-time Chief Transformation Officer (CTO) to establish singular leadership. Yet, the core question remains: What exactly is transformation in the AI era?
To understand the challenge, we must first look back at the IT era. That transformation meant moving from human-based workflows to digitized ones. We inserted software into existing, linear workflows: paper ledgers became digitized accounting systems, and labor-intensive ad campaigns moved to programmatic platforms. Even recent billion-dollar cloud migration projects were largely software implementation. The critical point is that these efforts simply maximized speed and efficiency based on pre-defined rules. The business process itself was never fundamentally challenged; human agency remained entirely intact. This existing expectation around what business transformation means is precisely what sets up the inability to transform in the AI era.
I recently heard Jensen Huang highlight why AI transformation is different:
“Software was pre-complied and compute needs were not very high. For AI to be effective, it has to perform context aware intelligence at the moment; you cannot compute in advance and retrieve it, that’s called content.”
To elaborate, traditional software was customized to implement existing process rules. The AI paradigm is completely different, it offers to make machines intelligent. But intelligent machines that can respond to evolving context in real-time demands vast computational power and, more importantly, a new relationship between the machine and the organization. This cannot run on IT pre-compute infrastructure, across the cultural split between business and IT organizations, or with governance models run in silos.
This AI paradigm requires three immediate, mission-critical commitments from leadership: First, the need for CEO and Board level sponsorship is non-negotiable. Second, data governance and responsible use must be a starting point, not an afterthought. Third, AI without job recomposition is vanity AI; it will not yield durable long-term value.
It is precisely this logic that makes the MIT study revealing 95% of companies did not find value in AI unsurprising to many executives. We are at the dawn of the AI era, and it requires a serious rethink of company transformation strategy, deeply intertwined with their AI strategy. This new strategy must provide an investment guide to buy vs. building AI competency, clear value mapping of business outcomes, AI infrastructure investments, governance system, and talent strategy that directly addresses job recomposition opportunities.
How Can Enterprise Adapt for this AI Paradigm?
Since the internet era, the digitization of workflows ensured that most jobs in business functions did not dramatically change. For example, a team that built promotional offers simply had to be trained to use the new tool. The transformation failed to fundamentally explore:
What if we do not need to create promo offers every planning cycle?
What if, with AI enabled personalization, we could create a one-of-a-kind, context-aware offer in real-time, eliminating the twelve-week lead time for approvals?
The transformation we need now focuses on determining the discreet tasks necessary to serve our customers. These tasks must be evaluated to leverage AI and agentic solutions to design real-time, context-aware solutions. This means all the rules in the legacy software system need to be challenged and considered for elimination.
This could mean the offer team shrinks, or perhaps grows to make room for new job functions, all focused on developing a highly personalized offer that can be game-changing and create unique competitive advantage. The team needs to think about how to break down and recompose every job, determining where tasks are entirely owned by AI, shared between a person and AI, or where new jobs need to be created. This also means rethinking how consumer data is stored and accessed in privacy-preserving ways by machines to prevent biased recommendations propagating via AI agents.
Such a team will need dedicated resources to focus on governance and safety on one hand, along with dedicated resources focused on testing and iterating on personalization offer strategy on the other. Both of these are new job functions that must be designed for employees to transition into, but it is equally important to design these new job functions to be augmented with AI from the get-go. At the same time, the team that creates promotion copy may use copy agents, and team-level recomposition decisions could then rebalance the optimal mix of human and AI skills required.
Therefore, this AI-fueled transformation will be a task force of strong leadership in data, software, AI, business function, and HR, backed by legal, privacy, security, and compliance teams. In this model, IT is not merely the doer, business is not merely the user/customer, and data and AI are not afterthought. Instead, this is truly a task force working in weekly sprints with a singular goal: Can we serve our customer in a more context-aware, personalized way than was ever possible?
This task force must explicitly address key questions surrounding this new paradigm:
What new and differentiated value does this generate for our consumers and shareholders that was not possible before?
What are the associated costs for such deep personalization?
What resource and financial productivity gains can we achieve?
How will this fundamentally change job composition?
How do we plan for AI literacy, upskilling, and hiring in this paradigm?
The necessity of this integrated model spotlights two crucial stakeholders historically invited late to the table when the work was IT-driven: the Chief Data & AI Officer (CDO/CDAO) and the Chief Human Resources Officer (CHRO).
I recently moderated a panel on Building and Navigating a Transformation Roadmap at the UCI Center for Digital Transformation Annual Conference, a shortened version is included here.
As Robin Gordon, CDAO at Hippo Insurance, astutely points out, governance is a critical enabler in this AI era and workflow is non-linear when AI is in the mix, requiring a workflow design rethink. This transforms the traditional planning system into a continuous, self-optimizing, and human-governed model for AI. It treats strategy not as a static document, but as a living model that is constantly tested, refined, and validated by machine execution, establishing reliable control over the decentralized autonomy needed to compete in the AI era.
Mary Burke, SVP HR at Experian, correctly calls out the need for the CHRO to be at the table from the get-go because we are looking at employee upskilling, reskilling, job recomposition, along with talent acquisition and retention strategies simultaneously. And it’s not just about talent; it’s a culture redesign focused on how we communicate and tell our story in this machine-augmented future. This is as monumental as starting an HR organization from scratch while supporting everyday company and employee needs.
Therefore, the AI Era transformation must start with CEO-level sponsorship and then define a governance structure that brings key stakeholders to the table from the get-go, enabling the rapid integration of AI as a core driver of productivity and growth.
Next month, I will continue this discussion on Transformation in the AI Era and share additional insights I have been uncovering through my work.
About the Author: Meghna Sinha is Chief AI Officer Kai Roses, Inc. She specializes in developing business-aligned AI strategies and guiding organizations through comprehensive implementation plans.
About Kai Roses: Kai Roses was founded to incubate innovation and introduce springboard learning opportunities to young people so they can thrive during the 4th Industrial Revolution.
We are driven by challenges that create the greatest net positive impact on the world. We believe a future where creators being pushed out of the economy by AI-generated content is unsustainable. Our product mission is focused on solving this pain point; our consulting and foundation pillars support the company’s growth and mission.
Product: With the rise of AI content production and blockchain, we’re focused on building essential tools that help creators organize, protect, preserve, and monetize their work on their own terms.
Consulting: Dedicated to accelerating the responsible adoption of AI, our consulting practice guides businesses on their AI journey. We specialize in developing business-aligned AI strategies and comprehensive implementation plans to empower our clients to build lasting AI competency.
Foundation: We are deeply committed to AI literacy and responsible implementation. To further this commitment, Meghna serves as a Distinguished Fellow with AI2030 - a non-profit dedicated to mainstreaming responsible AI. She is also a Senior Industry Fellow at UC Irvine Center For Digital Transformation, researching sustainable AI competency and the societal and economic impact of AI-driven workforce augmentation.
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