The enterprise landscape is currently navigating a period of profound redefinition where the distinction between technology and business strategy is effectively dissolving. At the recent IBM Think summit, the conversation shifted from simple automation to a complete overhaul of the corporate operating model, signaling a future where digital agents are woven into the very fabric of institutional knowledge. Chloe Maraina joins us to share her perspective on this evolution, drawing from her deep expertise in data science and business intelligence to explain how legacy organizations are transitioning toward an agent-first economy. This dialogue explores the intricate balance of rearchitecting internal processes, the necessity of rigorous data hygiene, and the cultural shifts required to turn technological optimism into sustainable revenue. We delve into the specific strategies that global leaders are using to remove administrative toil and the ways in which human-centric design is becoming the primary driver for successful digital transformation.
Many leaders expect AI to become their core business model by 2030, yet few can pinpoint exactly where that revenue will originate. How should companies bridge this clarity gap, and what specific steps can they take to move from general optimism to a concrete profit strategy?
The disconnect we see today is quite stark, with nearly 79% of senior executives anticipating that AI will be a primary driver of their revenue by 2030, while only 24% can actually visualize the specific pathways to that profit. Bridging this gap requires moving past the idea of AI as a “bolt-on” feature and instead treating it as the foundational architecture of the business. To find that missing clarity, companies must first engage in deep human-centric design, mapping out every friction point in their current workflows before even touching a line of code. We see organizations like New York Life already doing this by fundamentally rewriting every job description to reflect a new partnership between human intellect and digital agents. By focusing on removing the “toil”—those repetitive, soul-crushing administrative tasks—companies can free up their most expensive assets to focus on high-value consultation and strategic decision-making. The concrete profit strategy emerges when you stop looking for a “silver bullet” application and start identifying the cumulative gains from streamlined internal operations and improved customer experiences.
Automating a flawed process often yields poor results, necessitating a ground-up redesign of operations and data hygiene. What are the primary risks of skipping this “clean-up” phase, and how do you decide which legacy data is worth salvaging for new digital agents?
The risk of automating a “broken” process is that you simply accelerate your mistakes, creating a high-speed engine for organizational chaos. We have seen instances, like at Providence Health, where leadership realized they couldn’t just layer an agent over their existing systems without first addressing thousands of open job postings and decade-old data that no longer served the business. Skipping the clean-up phase leads to “hallucinations” in the logic of your digital agents and erodes the trust of the very employees meant to use these tools. Deciding what to salvage requires a ruthless audit of utility; if data hasn’t influenced a decision in several years, it is likely more of a liability than an asset for a modern agentic workflow. Successful transformation involves a ground-up revision where you redesign the experience first, ensuring that the information being fed into the AI is accurate, contextual, and directly relevant to the desired outcome. This creates a sensory shift for the organization, where the data feels reliable and the processes feel lean rather than burdened by legacy baggage.
Job descriptions are being rewritten as digital agents take over administrative tasks like record requests and file reviews. How does this shift change the daily interaction between employees and their technology, and what unique human skills become most valuable once the “toil” is removed?
The daily interaction between a worker and their terminal is shifting from a “search and retrieve” hunt to a “command and oversight” role, where the employee acts as the conductor of an orchestra of agents. Imagine a claims manager who no longer spends their morning chasing physician records or verifying patient referrals because a digital agent has already surfaced those insights in a personalized command center. This reduction in cognitive load allows for a more intense focus on what we call the “soft touch”—the empathy, complex judgment, and nuanced communication that a machine cannot replicate. As we remove the administrative burden, human intellect is redirected toward strategic planning and high-stakes decision-making, which are often in short supply because people are currently too busy manipulating spreadsheets. The goal is to elevate employees into roles that only people can perform, turning them into strategic partners who design better work processes rather than just executing them. This transition fosters a work environment where technology feels less like a hurdle and more like a high-powered assistant that anticipates the next logical step in a project.
Reducing the time for internal hiring and transfers by nearly two weeks can significantly impact workforce morale. Beyond faster processing, how can organizations reinvest the cost savings from automation into upskilling entry-level workers for more specialized, higher-value roles?
The real magic happens when you take the tangible gains—like the 12 days saved in the internal transfer process at Providence Health—and convert those hours and dollars into a formal reinvestment in human capital. If an organization can save over a million dollars by stripping away transactional work, those funds can be directly funneled into training diagnostic imaging technicians, medical assistants, or specialized tax directors. This creates a powerful cycle of internal mobility where entry-level workers see a clear, technology-enabled path to more sophisticated, higher-paying roles within the same company. It transforms the narrative of AI from one of replacement to one of promotion, where the “saved” time is used to mentor juniors or engage in the apprenticeship models that have long been the backbone of firms like EY. By reinvesting in talent, organizations ensure they don’t lose the institutional knowledge held by long-term employees, even as their daily tasks evolve into something entirely different. This strategic reinvestment is what ultimately distinguishes a company that is merely “cutting costs” from one that is “building a future-ready workforce.”
The traditional user interface for core systems like CRM and ERP may eventually give way to agent-to-agent communication. What does an “interface-less” work environment look like for a typical employee, and how must software providers evolve to stay relevant in an agent-first economy?
In an interface-less environment, the typical employee might never actually “log in” to a CRM like Salesforce in the way we do today, because they will be interacting with a digital agent through natural language or voice commands. The agent becomes the bridge, handling the “button-pushing” and data entry in the background while the human stays focused on the high-level conversation or creative problem at hand. This means that platform companies must reinvent themselves; they are no longer just “screens” for people to look at, but must become robust engines for agent-to-agent transactions and data orchestration. Software providers will need to focus on how well their systems can communicate with other autonomous agents under clear, governed rules rather than just providing a pretty dashboard. The core value of these systems will shift from the user interface to the quality of the underlying data and the ease with which it can be accessed and acted upon by an AI layer. It’s a move toward an ecosystem where technology is invisible but omnipresent, working silently to ensure that insights are surfaced at precisely the right moment.
In highly regulated sectors, certain decisions must remain under human oversight to ensure compliance and a “soft touch.” Where exactly should the line be drawn between automated approvals and human judgment, and what governance frameworks ensure that AI does not overstep?
The line is often drawn at the intersection of volume and complexity; for instance, a standard maternity claim that is approved 99.9% of the time is a prime candidate for automated approval because it improves the customer experience without introducing significant risk. However, in highly regulated environments like State Street or EY, any decision involving nuanced interpretation of law, ethics, or high-stakes financial risk must maintain a “human-in-the-loop” to provide the necessary oversight. Governance frameworks must be built into the operating model as a “safety mechanism,” much like the traditional apprenticeship model where a junior’s work is always reviewed by a senior partner. These frameworks should include clear rules for orchestration, ensuring that AI agents can coordinate tasks but cannot finalize a complex judgment without an explicit human sign-off. This “soft touch” is not just a regulatory requirement but a competitive advantage, as it ensures that the final output of any process still carries the weight of human accountability and empathy. By setting these boundaries early, companies can leverage the speed of AI while maintaining the trust and integrity required in their specific industries.
Many employees feel anxiety about multi-year technology shifts and what they mean for their long-term job security. What communication strategies effectively maintain trust during a transformation, and how do you foster a culture where AI is viewed as a tool rather than a replacement?
Trust is maintained through radical transparency and constant, multi-channel communication that emphasizes the long-term vision rather than just the immediate technological milestones. Leaders must be honest about the fact that this is a multi-year journey and that while job descriptions will change, the value of the individual’s intellect and experience remains the priority. A successful strategy involves showing, not just telling; when employees see that a tool like ChatGPT is as common and helpful as a stapler, the fear of the unknown begins to dissipate. We foster this culture by creating “transformation offices” where employees are encouraged to experiment with AI in their own daily work and share those successes with their peers. It’s about moving away from the “black box” approach and toward an environment where AI is seen as a way to reduce cognitive load, making the work day feel less like a series of administrative hurdles. When people feel that the technology is there to serve them—to make them a more strategic partner in the business—they move from a defensive posture to one of active adoption.
What is your forecast for AI transformation?
I believe that the next few years will see a shift in focus from the “intelligence” of models to the “orchestration” of entire ecosystems, where the winners are not the companies with the most tools, but those that have completely redesigned how work gets done. By 2030, we will see the emergence of the truly AI-native enterprise, where the operating model is built from the ground up to allow humans and digital agents to jointly plan, execute, and govern every business function. We will move away from the era of “sprinkling AI” on old workflows and into a period of bold architectural rebuilding that places the human intellect at the center of a much more efficient, automated world. While the intersection of this high-tech vision and current reality will take time to fully align, the organizations that are thinking big and bold right now are the ones that will define the new economy. Ultimately, AI transformation will be measured not by the complexity of the algorithms, but by how effectively it shrinks the friction of daily processes to lift the potential of every person in the workforce.
