Digital Transformation has reached a point of no return, and anyone who still thinks it is just about new technology is missing the bigger picture.
What is happening right now inside companies is something much deeper: the processes that keep day-to-day operations running were built around the technological limitations of the past, and Artificial Intelligence is exposing that problem with uncomfortable clarity.
Think about it: what good does it do to accelerate a process with AI if that process was already broken from the start?
The answer is simple, but painful for a lot of people: it does no good at all — it only makes things worse.
And that is exactly where a role that has gained enormous strategic weight in recent years comes in: the CIO.
According to the J.P. Morgan Business Leader Outlook 2026, 62% of mid-sized companies are already using or plan to use AI-powered process automation. Meanwhile, the EY CEO Outlook 2026 revealed that 43% of CEOs cite operations optimization and productivity improvement as their number one transformation priority, with innovation in products and processes coming in third.
Those numbers say a lot about market appetite, but they say even more about the risks when the race starts before the planning does.
In this article, you will see how the most strategic CIOs are leading a shift that goes far beyond implementing tools — and why the work starts long before any line of code or AI agent enters the picture. 🚀
The CIO Has Become the Architect of Real Change
For many years, the CIO role was seen as more operational than strategic. This was the person responsible for keeping systems up and running, making sure infrastructure worked, and approving new software purchases when needed. But that landscape has changed dramatically — and it was not a gradual shift. It was a flip of the switch that happened alongside the explosion of Artificial Intelligence in the corporate environment.
Today, the CIO is called to sit at the table where business decisions are made, not just provide technical support for them. This role transition is what separates companies that are seeing real results from AI from those still stuck running pilot projects with no clear path forward.
Maria Cardow, CIO of managed security services provider LevelBlue, sums up this logic well. According to her, every business process reflects the constraints that existed when it was created, and those constraints usually stem from the technological limitations of that era. The result is that many corporate processes still involve workflows with manual actions or clunky jumps between multiple applications to complete essential tasks.
That is why Cardow argues it is incredibly important to reassess processes before trying to automate something that is probably already an outdated and ineffective process.
What makes this new role so complex is that the CIO needs to balance two fronts at the same time: the speed the market demands for adopting new technologies and the need for solid internal processes before any implementation begins. You cannot simply buy an automation platform and expect it to solve problems that have existed for years in the company culture and workflows.
In practice, Cardow’s IT team frequently discovers that corporate processes run through at least a dozen different systems and that the number of assumptions baked into the workflows are dependencies on technologies from two generations ago. This reinforces why CIOs must guide their organizations through an optimization phase before automating with AI.
Another factor that has elevated the CIO to the center of strategic discussions is data governance. Any serious Artificial Intelligence initiative depends on data that is well-structured, clean, and accessible. And guess who is the guardian of that structure inside organizations? Exactly — the CIO. Without reliable data, AI models deliver inconsistent results, and the entire digital transformation narrative starts to crumble.
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Cardow adds that the CIO is a natural fit for this work, since IT leaders have held responsibility for optimization since practically the beginning of the role. IT also brings objectivity to the process, since workers are often comfortable with the status quo and the tools they use, while CIOs and IT teams do not have that kind of attachment. 🧩
Process Optimization: The Work That Happens Before AI Steps In
Process optimization has always been a priority inside companies, but it has taken on a completely different dimension with the arrival of AI. In the past, optimizing a process usually meant reducing manual steps, better training teams, or adjusting approval workflows. Today, the concept goes much deeper.
You need to rethink the logic behind every process, question whether that sequence of activities still makes sense in the current context, and identify where automation can actually generate real value — not just surface-level speed. This work requires a combination of technological vision and deep business understanding, which is why the CIO needs to work side by side with other company leaders.
At LevelBlue, for instance, every time a technology is approaching license renewal, the team takes the opportunity to review any business process that depends on it. The goal is to determine whether the process is mature enough for optimization and transformation before renewing or replacing the existing technology — and before applying more automation or AI. That timing puts Cardow, as CIO, in a central leadership position for optimizing corporate processes.
One of the most common mistakes companies make on this journey is trying to automate processes that are, at their core, poorly designed. Imagine an expense approval workflow that goes through five layers of manual review, with rework in at least two steps. If you automate that with AI without first redesigning the flow, you are not optimizing anything. You are accelerating a problem.
CIOs who truly understand this start every AI automation project with a robust mapping phase, where each step of the process is analyzed, questioned, and, if necessary, eliminated before any technology implementation begins. That methodological rigor is what separates a project that delivers ROI from one that just becomes a line item in the annual report.
There is also a human dimension to this process that cannot be ignored. Process optimization driven by AI inevitably changes how people work — and it often generates resistance. Teams feel their roles are being threatened, that decisions will be made by machines, and that the knowledge they have built over the years will lose its value.
It falls on the CIO, in partnership with HR leadership and senior management, to create a transition narrative that is honest, clear, and positions Artificial Intelligence as a tool for amplifying human work, not replacing it. Companies that invest in this internal communication see much better results when adopting new technologies. 💡
Agentic AI Changes Everything: Reimagining Complete Workflows
If automating individual tasks was already challenging, the arrival of agentic AI has raised the complexity to another level entirely. Now, it is no longer about optimizing one task here, another there. The game has shifted to reimagining entire workflows, end to end.
Doug Gilbert, CIO and Chief Digital Officer at Sutherland, a digital transformation services company, explains this shift directly. According to him, during previous waves of automation — like when RPA (Robotic Process Automation) first launched — CIOs focused on improving individual tasks. Now, they need to optimize entire processes and workflows to transform much larger swaths of the organization’s operations.
In Gilbert’s view, if you are looking at AI as a more complex RPA to solve tiny tasks and activities, you will fail. You need to look at how humans work across different systems and tasks, because today’s automation needs to flow through an entire process.
A concrete example of this is Sutherland’s Insurance AI Hub, a project launched and completed in 2025. Gilbert led the overall technology direction and architecture, overseeing the implementation of a complete ecosystem of domain-trained AI agents for underwriting, claims adjudication, and policyholder servicing across multiple lines of business. All of it built on solid master data foundations, embedded governance, observability, and human-in-the-loop controls from the start.
The result? A production-scale agentic AI system delivering up to 30% reduction in claims cycle time, lower financial leakage, higher satisfaction, and more robust compliance.
Another example is Sutherland’s agentic AI platform for healthcare provider credentialing. This multi-agent system reimagines a highly complex, regulated, and cross-functional process at the macro level, automating and contextualizing information across many previously siloed processes, internal systems, and external sources. What used to be a slow, manual process taking several weeks has been transformed into a fast, accurate, and governed operation.
Agentic AI — the ability of LLMs to actually take action — is significantly raising the bar. CIOs can no longer optimize in silos or layer intelligence on top of broken processes.
Intelligent Automation: When AI Finally Takes the Stage
Once processes are mapped, redesigned, and validated, that is when automation with Artificial Intelligence steps in with its full potential. And what happens at this stage is quite impressive when the foundation has been properly built. Productivity gains show up quickly and measurably, teams can focus on higher-value strategic activities, and decisions start being made based on real, up-to-date data — not gut feelings or outdated reports.
This is the moment when digital transformation stops being a concept and becomes a reality felt across the entire organization.
A brilliant example comes from Boston Consulting Group. Merim Becirovic, CIO, Managing Director, and partner at BCG, shares that the consultancy’s employees create more than 30 million slides per year, making it a process that consumes a massive amount of work time. Instead of focusing on incremental gains by using generative AI for specific tasks — like drafting text for individual slides — BCG’s teams reimagined the entire presentation creation lifecycle.
The result was Deckster, an internal AI tool that, using the OpenAI API alongside a curated library of BCG templates, produces fully formatted, client-ready slides in just three seconds instead of the 15 minutes it used to take. Now BCG is bringing agentic AI capabilities to Deckster to optimize and transform the process even further.
The most common use cases that CIOs are prioritizing in 2025 also include automation of financial processes like payment reconciliation and anomaly detection, customer service automation with conversational AI agents, intelligent supply chain management with demand forecasting and inventory optimization, and automated analysis of contracts and legal documents.
In all of these scenarios, what makes AI effective is not just the technology itself, but the fact that it is operating on processes that have already been cleaned up and well-defined. The quality of the input determines the quality of the output — and that is a truth that does not change no matter which AI model you are using.
It is also worth noting that intelligent automation is not a project with an end date. It is an ongoing journey of improvement. AI models need to be monitored, adjusted, and retrained as the business context evolves. The processes that were optimized today may need another round of review six months from now. And that is why the most mature companies on this journey are already building internal AI governance structures, with dedicated teams to monitor model performance, ensure regulatory compliance, and identify new opportunities for application. 🔍
The Challenges Standing in the Way
Expectations for transformation are high, but the challenges that CIOs and their C-suite peers face are enormous as well.
Catherine Malkova, Senior Vice President at Kyndryl Consult and Practices US, points out that the focus now is on business workflows — not just taking old processes and making them more efficient, but recreating them from scratch as an agentic AI workflow. It is about building an AI-native enterprise, and the agentification of workflows is what will drive the next wave of AI adoption.
The Kyndryl Readiness Report 2025 reinforces the scale of this expected transformation: 87% of the 3,700 business leaders surveyed across 21 countries said AI will completely transform professional roles and responsibilities in their organizations this year.
Kyndryl’s research identifies five specific readiness challenges:
- Building solid technology foundations
- Managing global data efficiently
- Evolving the workforce
- Pressure to scale AI pilots
- Aligning leadership around a shared vision
Beyond that, rigid workflows, fragmented systems, lack of trust in AI, shortage of necessary skills, and resistance to change also represent significant barriers. Others point to the lack of good documentation for many processes as yet another obstacle, along with concerns about data security, privacy, and AI hallucinations.
Dom Profico, CTO of consulting firm Bridgenext, which advises CIOs, warns that corporate ambition itself can be part of the problem. Executives and teams may rush to adopt AI without doing the necessary work to transform the processes that AI is supposed to help optimize.
According to Profico, AI makes automation so easy that the risk of automating a bad process increases considerably. And since everyone feels like they are behind in the AI era, that pressure raises the likelihood of moving too fast.
The Skills CIOs Need to Master Right Now
The current landscape demands more from CIOs than at any other point in the history of the role. Being a good technology manager is no longer enough. You need to master a combination of skills that span from the technical to the strategic.
Doug Gilbert lists the essential competencies for the CIO of the AI era:
- Deep fluency in process intelligence platforms and data mining tools
- Data strategy expertise, especially master data management, lineage, and context engineering
- AI governance and risk management, particularly around autonomous agents and accountability
- Advanced change leadership, designing new operating models where humans and agents collaborate effectively
- Business translation skills, the ability to connect technical decisions directly to financial outcomes and customer experience results
In Gilbert’s words, technical depth is still essential, but the real differentiator today is the ability to think systemically about human-plus-agent workflows and to lead at the intersection of strategy, technology, and risk.
Profico reinforces that systems thinking is an indispensable skill for CIOs, as it enables a holistic approach to problem-solving — seeing how different parts interact within a larger whole. This requires CIOs to deepen and broaden their knowledge of the business, of how work gets done, and of what truly adds value.
Another important indicator is the growth of investments in upskilling and technical team development within companies. CIOs are realizing that hiring external talent for every AI need is not scalable, and that building internal capabilities is the more sustainable path. This ranges from training data analysts to work with generative AI tools to equipping business leaders to interpret and question model outputs.
Artificial intelligence only delivers value when the people working with it understand its limitations, its biases, and its real potential. 💡
The CIO as Instigator, Influencer, and Innovator
The landscape taking shape for the coming years brings growing pressure on CIOs to deliver concrete, measurable results from their AI and automation initiatives. Boards of directors and CEOs no longer accept transformation projects that stay in the abstract. They want to see impact on EBITDA, reduced operating costs, improved customer experience, and revenue growth.
Becirovic, from BCG, has a very clear perspective on this. For him, what CIOs do now is not about routing people through different systems. It is about transforming the journey and the experiences within that journey. It is about connecting the dots and connecting the systems that create those experiences.
This is a natural evolution for the CIO, according to Becirovic. Technology provides a lens to see this work and gives access to products that can break down organizational silos.
Gilbert moves in the same direction, stating that the CIO now owns end-to-end process intelligence, ensuring that the underlying data is clean and contextualized, that governance is embedded, and that the optimized process actually delivers reliable business outcomes. The CIO needs to sit at the strategy table and, in large part, drive it from here on out.
And the CIOs who can translate the value of technology into that business language will be the ones with more autonomy, more budget, and more influence to lead truly meaningful changes within their organizations.
In Becirovic’s words, today’s and tomorrow’s CIOs need to be much more aggressive in seeking change and driving change. The CIO needs to be the instigator, influencer, enabler, collaborator, and innovator to make it happen. 🚀
Digital transformation is not about technology. It is about the courage to rethink how a company operates, with technology as the greatest ally on that path.
