AI is everywhere. As a new frontier of technology, it seems to have suddenly moved from a possible future consideration into a present day requisite. It is widely seen as a tidal force reshaping industries—though its divergence and limitless prospects make it tricky to grasp its very frame. What levels of impact, directions of change, degree of disruption, or scales of influence should we start calibrating for? All of it, possibly. But how do we organize our starting points and know the progress we’re making?
These can be difficult questions to answer, yes—and even crippling, if seen as a requirement before jumping in or taking action. We may find that a significant population remains steady in position, waiting at the starting line. But delaying a start can be dangerous; not only because you’ll have to catch up later on, but also because you’ve missed out on valuable, first-hand learning along the way.
This is especially true in the context of medium to large enterprise organizations, where workflows, motivations, metrics, and value delivery take time to reshape. AI is transforming how we work, what we build, and how we deliver—in real time—and will continue to do so regardless of our readiness or resistance.
So where should organizations begin their AI exploration and adoption? We’ve come up with an AI Horizons Model that helps unlock action and clarifies the road ahead. With three overarching levels of impact: Optimize, Differentiate, and Reimagine—the model supports a holistic approach to AI, enabling intentional decisions and avoiding aimless experimentation. The goal is to have enough framing to begin folding in AI into the everyday, keeping in mind that AI itself is on the move.
Optimize: AI as a way to gain efficiencies & manage costs
A fundamental benefit accessible and executable by almost any organization is that of optimization. How can you do what you do, only faster, cheaper, more efficiently? Today, AI has proven to be most immediately and widely applicable in this realm. Several examples of quicker progress, reduced timelines, and faster results have emerged: AI triage bots handling the first level of customer service requests support quicker turnaround times, AI-produced brand content and newsletters reduce production time, AI-enabled CRMs and sales cut note-entry time, AI-generated CSS snippets and responsive layouts help designers move into development faster, AI scans of monthly invoices reduce close cycle time, and so on.
Duolingo, a game-like language learning company, is utilizing this approach with notable rewards. It took them 12 years to develop their first 100 courses, and just one to develop their last 148. They are leveraging AI and the reservoir of content they have previously created to generate even more, allowing them to focus on ensuring quality content, as well as freeing up resources to focus on expanding available content to additional languages with strong demand.
Recognize and Define Your Culture and Priorities As An Organization
These AI-enabled gains are valid, and useful, to the organization and bottomline. But how much of it is accidentally effective in the context of the culture, people, behaviors, and priorities of each organization? Behavioral tendencies developed by large organizations influence how teams collaborate, make decisions, handle risk, and deliver outcomes. One company may prize speed and experimentation, while another leans on rigor and consensus. Some default to top-down directives; others encourage grassroots innovation.
These patterns aren’t always intentional—but they deeply impact how work gets done, how change happens, and how new tools like AI are adopted. Recognizing and working with (or against) these ingrained behaviors is often the difference between strategy on paper and actual, sustainable impact. It is worthwhile recognizing an organization’s behavioral tendencies in identifying, deploying, and evaluating AI tools.
Identify The Most Valuable—not only Impactful—Use Cases for AI Deployment
Taking this clarity further means defining the most valuable use cases for AI deployment across the org. Individuals exploring their AI passions independently and discretely across business units does not necessarily line up with the best way to bring value to the company. Bringing a cross-functional understanding of AI benefits not only with internal processes, but also of experience delivered to customers—is crucial. An organization can save time and money by AI triage bots deployed in customer service, yes, but does that improve or detract the customer experience? Instead, might it be more valuable for the organization to deploy AI in personalizing marketing messaging at scale? Proceeding without thinking holistically can be risky.
Redefine Feasibility and Workflows with AI as an Operational Team Member
Even beyond gaining efficiencies in time, money, and staff, AI has expansive potential in this realm that is yet to be realized. We have to consider the shift from AI as a tool to AI as an agent, an operational ‘member’ of the team. In the future, these agents will have an employee ID, permissions, access to tools, and will work in tandem with other humans. How well equipped are we to welcome this future? Who or what is redefining the meaning of feasibility with this new workforce? Which workflows need to be reworked and refined for most efficiency?
Answering these questions necessitates hands-on experimentation and integration with emergent AI tools and technologies. Generating an org-wide view or scorecard of AI experimentation and performance is a good starting point. Further on, boosting the experimentation with pilots—for AI agents, for example, would strategically enable organizations in this space.
Change is intimidating, and the threat of being replaced or made non-essential is an understandable one to want to avoid. But distancing ourselves from AI will only ensure we are replaced by it. Embracing this new technology keeps us embedded in the process and helps us be a part of the change.
Differentiate: AI as a way to gain a step ahead in the market
AI has proven highly useful in not only helping organizations operate better, but also delivering customer-facing value that enhances core product or service offering(s). AI-powered offerings are quickly becoming an expectation for customers and users—and these expectations aren’t going to turn back. This second horizon of AI possibilities will be about gaining a step ahead of competitors.
Several success stories have emerged in this space. The Nike Fit, an AI-powered feature in their app addresses customer frustration with online purchases because of size variations across shoe styles. A smartphone camera or in-store scanner captures 13 data points per foot to generate a ‘digital fit profile’ and suggests the right shoe size across all Nike shoe models. This has led to higher trust in online purchases, higher conversion rates in mobile and web shopping, and reduced returns.
Walgreens has been using AI to boost medication adherence in new ways: it predicts when a customer may miss a refill or stop taking medication based on their prescription fill patterns, historical behavior, and medical guidelines. This has meant an increase in auto-refills and pharmacy service usage through reminders via app notifications, texts, or pharmacist outreach.
In the case of Duolingo, personalized learning paths have enhanced value for their users. AI algorithms support tailoring of lessons and real time adjustments that target areas that users would benefit from improving. Even further, app users have access to AI tutors that simulate more natural conversation akin to what a real life teacher might provide. These adaptive offerings clearly differentiate Duolingo not just from many competitors, but also from their own previous way of working.
Identify Net New Innovations for Current Customers
These success stories highlight how AI can independently lift the game for any organization, supporting new competitive advantages and unique value propositions. But how does one find the right use case for such an impact? It begins with a return to customers’ newest context, challenges, and desires. How does the changing AI landscape evolve their expectations of your product? What new things are they doing in the context of their work and life that demands new functions from your products? Force-ranking possible use cases from an outside-in point of view helps define net new innovations for current customers.
Boost Experimental Mindsets through New Metrics, KPIs, and Currencies
Defining net new innovations is one thing, delivering them is another. Supporting quick experimentation and enabling a mindset shift to work quickly and scrappily with new technology can be hard in most enterprise companies. How might an organization persuade its workforce to work differently in the context of accelerating technological change? Defining new metrics, KPIs, and currencies that tap into individual motivation can prove game-changing.
Being fluent in AI tools becomes a résumé asset for individuals, while at a team level, being seen as ‘future-ready’ both inside and outside the company boosts brand standing. Early users of emergent technology often get seats at the table to shape org-wide AI norms and playbooks—attractive to ambitious mid-level leaders. New currencies will be revealed and codified as AI becomes embedded into organizational realities, and can be used to amplify interest in AI integration.
Deploy New Operating Models for Your Organizational Context
Codifying the developing interest in AI experimentation and exploration is a natural next step—through Councils, Task Forces, Centers of Excellence, Champions, or ‘Garage’ teams. The chosen scaffolding for ownership and process can set up the organization for quick delivery of customer or user value and truly leverage this moment in time to gain a competitive edge.
Seeing AI as an enabler of new customer value can be highly beneficial to the business, though the opportunity is even bigger: to not only enhance today’s core offer, but also shift the very identity and scope of the offer. Net new additions to your portfolio are inevitable—mainly through value delivered by or unlocked through AI—which is just now possible. AI seems to be moving from being just an enabler of your offer to becoming a core part of the offer itself.
Developing a customer-centric AI strategy is step one here—whether at a functional or org level—by returning to customer/user context and understanding how their world is evolving. Defining new value propositions, quick prototyping, and customer-facing experiments will ensure quick movement while reducing risk of placing the wrong bets.
Reimagine: AI as a way to rethink or redefine your business
This horizon is the most transformative, where AI is not just an enabler or product, but a driver of entirely new business models, markets, and strategic identities. In this realm, AI is applied not to a process or a product, but to existential planning and decision making. This sounds rather speculative, and it is worth wondering if this line of questioning is warranted. We think it is, yes: because AI is not going to stay the tool that it appears to be right now—it is fundamentally going to reshape the organizational and corporate landscape.
Traditionally, size has implied power in the industry: more people, more capital, more reach. But AI is equipping startups to hold the same power, and without the constraints of a large, slow-moving creature. AI-native alternatives can unbundle functions once considered core: strategy, R&D, marketing, compliance, legal, etc., to become fully automated services. Proprietary datasets no longer present an advantage. Boards and executives at many large firms continue to struggle with an AI strategy paralysis: is AI a threat, a tool, or the core business model?
The existential question isn't: should you adopt AI? Instead, it is: Are you still relevant in an AI-powered economy?
Let’s circle back to Duolingo. How will this organization—one that has been an early adopter since the beginning—shift or expand in the future? Might they expand from a product to a pervasive ecosystem of offerings? Whether by integrating more formal learning programs across educational institutions, or redefining how the world even assesses knowledge and fluency in any given language—Duolingo has strong advantages for redefining this entire space. Or maybe they will expand their scope beyond learning, and make a move into communications. Imagine Duo the owl, the official mascot of Duolingo, providing real time translations that facilitate immediate connection regardless of language spoken. Each of these shifts would entail a dozen disruptions to the industry vertical, including to Duolingo itself. Each speculation comes with real business implications.
The urgency and scale of change is real. But the fear needn’t be. Permanent decisions are not being made today; the urgency lies in simply beginning the experimentation. Painless thought-exercises on a five year outlook offer topics for clarity: Will your business serve the same customers or users you are serving today? If your current customers’ lives are being disrupted by AI, what place will you have in their daily routines? Who will be your future competitors? These might be startups that aren’t even launched yet—but have the capability to outmaneuver incumbents like you. So how do you begin preparing for a possibility of being disrupted?
Uncover AI Disruptions to Customers, Competitors, and Markets
You begin by uncovering how your customers, users, and competitors are evolving. This is less about asking people what they want, but about understanding how contexts, motivations, habits, fears, and needs are being reorganized. This inquiry can come from a defensive posture or from an exploratory one: either you’re open and able to adapt to the changing landscape, or you’re digging in your heels to defend your product, customer, and value proposition.
Market disruptions are not new: in the face of new technology, several organizations have become cautionary tales in the modern history of business. Kodak, Polaroid, Blockbuster, Encyclopedia Britannica, hotels, and so on, have experienced the side effects of native and user-centered applications of emerging technology.
Build Disruption Roadmaps to Redefining Your Business Model
With the recent acquisition of Jony Ive’s startup, io, by OpenAI, we may have a world of new possibilities to look forward to, especially in the realm of AI-powered wearables. Other experiments include Rabbit Inc.’s R1, a personal assistant device framed as an intuitive companion, and Humane Inc.’s AI Pin, which promises to free you from your smartphone. While these two products didn’t gain mass appeal, it is only a matter of time until we see new products being used en masse.
This then becomes everybody’s business—because as an organization, you’re either delivering your offer through these new solutions, or you’re designing your products to communicate or compete with them. Apple’s App Store changed the game once; another change is coming. While we’re waiting, it is important to begin building disruption roadmaps that help you identify possibilities for future business models. More and more, organizations will become product-led, channeling technological innovation into their core offers.
Define Governance, Risk, and Quality Models for AI-based Operations
Soon, AI is likely to be in a position to support experimentation with the business itself. Organizations can take risks within the safety of AI, and gain insight to how various decisions may impact their business short and long term. Trends and variations of the market can be paired with internal data on performance, revenue, or output, and identify opportunities where potential may exist. A framework to organize and prioritize these disruptive opportunities—or bets—is critical for decision-making.
Further on, in order to operationalize these explorations or bets, new governance structures, risk assessments, and quality models need to be developed. We are all in an experiment mode, yes, but assuring quality is a part of every organization’s brand, and an oversight will cost dearly in the long run. These business model experiments could be contained in a ‘lab’ environment, enabling true investigation, but also safe distance from today’s working systems. If successful, experiments could be spun-off or integrated into the larger organization.
With any disruptive technology comes hesitation, anxiety, and uncertainty. This, however, is paired with potential and vast opportunity. We all have a responsibility to embrace AI in our work, whether we are just beginning to integrate it into our day-to-day, or are projecting growth and disruption across our entire business or market. Our ways of working have already begun to evolve, and will continue to do so. As an organization, or as a human, do you plan to shape what’s next with AI, or be shaped by it?
