Rethinking digital transformation for the agentic AI era

Digital transformation enables growth, creates efficiencies, improves experiences, and develops competitive advantages. A primary objective is evolving business models as technology, data, and AI rapidly change customer expectations and market opportunities.

Technological paradigm shifts and disruptive global forces require CIOs to rethink their digital strategies every two years. In 2020, it was the pandemic, 2022 brought recession fears, and 2024 ushered in the generative AI era.

Two years ago, I shared how gen AI impacts digital transformation priorities, focusing on data strategies, customer support initiatives, and AI governance. Last year, I wrote about generating business value from gen AI by targeting benefits other than just productivity improvements. Other articles have focused on gen AI’s impact on the future of work, identifying foundational AI investments, and targeting business-impacting gen AI opportunities.

The advice offered in these articles has zeroed in on how generative AI changes digital strategy and priorities. Here, I turn attention to how generative AI impacts the organizational model for delivering those digital strategies and priorities.

Siva Ganesan, head of the AI and data business unit at TCS, believes the next transformational era will be defined by businesses that augment humans with agentic, generative, and predictive AI capabilities.

“In this model, organizations are investing in creating architectures for intelligent choices and using technology to augment people, not automate tasks, transforming the entire value chain,” he says.

CIOs should consider how agentic AI and other emerging AI capabilities enable the creation of intelligent organizations. Three areas CIOs should focus on include renewing customer centricity’s importance, evolving business engagement practices, and refining their organization’s digital operating model.

Reimagining product design and CX processes

Every customer experience (CX) strategy will require overhauling as customers expect agentic AI to be at the forefront of their interactions. B2C industries such as retail, media, healthcare, and personal banking — where personalization is a service differentiator — will undergo this paradigm shift first.

But John Mazur, CEO of Chatmeter, points out a huge opportunity to use AI on customer interactions to realize deeper organizational benefits. For example, by analyzing customer feedback, including unstructured data such as reviews and social media comments, “AI helps organizations operationalize that feedback to improve training, policies, and hiring,” Mazur says.

Moreover, organizations can leverage generative AI to help evolve their design thinking, prototyping, piloting, and testing practices. AI agents can accelerate the design process, facilitate more testing scenarios, and integrate customer interactions to ensure the process is more agile and iterative. AI can also help with customer pilots by, for example in the pharmaceuticals industry, improving patient recruitment and communications during clinical trials.

“AI is uniquely positioned to help us reshape how we design products, streamline operations, and enhance experiences,” says Satyajith Mundakkal, CTO of Hexaware. “By rapidly generating multiple design prototypes and automating extensive testing processes, we drastically reduce time to market, fast-tracking the journey from concept to reality.”

CIOs should organize a cross-functional leadership team to revolutionize their organization’s approaches to R&D, market research, design thinking, and customer piloting. AI agents will have roles in improving productivity in each of these disciplines, but advantages will emerge for organizations that rethink the entire design process.  

Accelerating agile change management

Agile methodologies, product-based IT, low-code development platforms, and citizen data science have driven several paradigm shifts in how business, data, and IT teams collaborate on innovations. Employees are already experimenting with LLMs and uncovering ways to adapt their work with agentic AI. CIOs can leverage these experiments to accelerate change management in their more strategic digital transformation initiatives, as connecting gen AI experimentation with small, substantive changes will help shift people’s thinking toward more iterative, feedback-driven practices.

“While many companies are working on massive deployments that are costly, lengthy, and highly disruptive, some of the most impactful outcomes and ROI stories are occurring with small deployments at task levels,” says Rob Scudiere, CTO at Verint. “In customer contact centers for example, companies are realizing millions of dollars in savings or incremental revenue generation, along with measurably enhanced employee and customer experiences, just by automating a single micro-workflow with AI-driven specialized bots.”

But mobilizing business units on gen AI-enabled workflow changes risks being hampered by a lack of organization-level communication about initiatives, changes, collaborations, and best practices. While CIOs should want departments and teams to work independently, they must centralize information and create top-down collaboration to ensure the changes align with and accelerate digital transformation objectives. 

“Identifying transformational use cases depends on your ability to get a full view of teams, projects, and the overall organization,” says Jon Kennedy, CTO at Quickbase. “Business leaders need a consistent and accurate view of information across the organization, regardless of where the data resides. Without that clear view of each team, project, and stakeholder, you can’t see the redundancies, overlaps, and productivity gaps that slow down projects and make decision-making difficult.”

Teams working independently and without collaboration can inadvertently create gray work, the time and resources lost hunting for information needed to keep projects moving and make decisions that drive impact and outcomes. CIOs recognizing the excitement and strategic importance of developing value from gen AI will promote agile and change management with teams, then expand the agile PMO’s mission to address communication and collaboration gaps.

Reinventing the digital operating model

Most CIOs already recognize that generative AI presents a significant evolution in how IT departments can deliver innovations and manage IT services. 

“Gen AI isn’t just another technology; it’s an organizational nervous system that exponentially amplifies human intelligence,” says Josh Ray, CEO of Blackwire Labs. “Where we once focused on digitizing processes, we’re now creating systems that think alongside us, turning data into strategic foresight. The CIOs who thrive tomorrow aren’t just managing technology stacks; they’re architecting cognitive ecosystems where humans and AI collaborate to solve previously impossible challenges.”

IT service management (ITSM) is a good starting point for considering gen AI’s potential. Network operation centers (NOCs) and site reliability engineers (SREs) have been using AIOps platforms to correlate alerts into time-correlated incidents, improve the mean time to resolution (MTTR), and perform root cause analysis (RCA). As generative and agentic AI assists more aspects of running IT operations, CIOs gain a new opportunity to realign IT ops with more proactive and transformative initiatives.

“We focus on use cases that result in better customer outcomes and free up bandwidth for our engineers,” says Michael Trkay, CIO at FICO. “Opportunities such as gen AI for hotfix development and predictive AI to identify, correlate, and route incidents for improved incident response are transforming our business, resulting in improved customer satisfaction, revenue retention, and engineering efficiency.”

On the dev side, copilots writing code have received the most attention, with DevOps teams accepting between 20% and 35% of the code recommendations. Coding benefits are just the beginning, as AI agents have capabilities across the software development lifecycle, including developing requirements, writing test cases, and maintaining documentation.

“Organizations should seriously evaluate gen AI’s potential, not only in coding and testing but also in the often-overlooked requirements phase,” says Andrea Malagodi, CIO at SonarSource. “By using AI with well-crafted prompts that leverage historical data, teams can accelerate the creation of robust requirements, ultimately reducing delivery cycle times.”

A third area where gen AI provides capabilities is in organizational design, team formation, and communications.

“AI acts as a career coach and mentor, helping employees grow by analyzing job architecture, corporate goals, and individual strengths to help guide employees on their desired career path,” says Ed Frederici, CTO at Appfire. “It improves productivity by forming optimal teams, matching the right skill sets to solve complex problems, and streamlining communication by summarizing messages, drafting emails, scheduling meetings, and booking travel.”

CIOs focusing only on productivity gains from gen AI may miss larger opportunities to transform their organizations. As technology changes rapidly, CIOs must invest time in learning vendors’ agentic AI capabilities, reviewing how employees use today’s AI tools, and refining the organization’s digital operating model.