across Fortune 500 companies, executives are grappling with the same question: How do we harness AI’s potential without falling behind competitors who seem to be moving faster? The AI discourse presents conflicting signals: some experts warn of over-hype while vendors flood the market with agent platforms and vertical AI solutions. Job displacement predictions swing wildly from 50% of white-collar jobs being eliminated to zero jobs lost.
The answer lies in understanding a critical distinction that most leaders are missing: the difference between two fundamentally different approaches to AI adoption.
Efficiency AI: the safe path of automating existing workflows and boosting productivity. Think co-pilots, automated summaries, and process automation. These deliver measurable but incremental gains, typically 10-50% productivity improvements in specific tasks. This makes sense as a starting point because it’s ripe ground for experimenting with new technology.
Opportunity AI: using artificial intelligence to solve previously impossible problems and create entirely new business and operating models. This isn’t about doing what you do today, only faster. It’s about making today’s approach obsolete. For senior leaders, this represents both the greatest risk and the greatest opportunity of the digital age.
Why Are Incumbents Vulnerable to Invisible Competitors?
A critical threat to established enterprises isn’t coming from known competitors, it’s emerging from companies that don’t exist yet or are invisible today. These AI-native startups carry no legacy baggage.
If you’re an incumbent, you have hundreds of people working in a tangle of legacy systems, antiquated processes, and inefficient workflows. Meanwhile, an AI-native company designs systems, processes, and organizations that bypass and leapfrog these inefficiencies entirely.
Initially, your moats might seem insurmountable. But over time, AI natives will create new, valuable services where margins are higher, while incumbents get stuck with low-cost, commoditized base services.
Consider an internal planning team. At an established company, the planning and analysis team spends weeks pulling data from siloed ERP and CRM systems to build a quarterly forecast. They use an AI co-pilot to speed up their spreadsheet work, a classic efficiency play that shaves a few days off a painful process. Meanwhile, an AI-native competitor could have no “quarterly forecast cycle.” Its architecture is a unified data graph where AI agents continuously monitor granular data. Instead of reacting to last quarter’s numbers or doing simple CAGR projections, the system identifies a leading indicator, like a dip in user engagement with a new feature, and immediately models its future revenue impact, drafts a reallocation of marketing resources, and assigns a decision to the relevant lead. This is an Opportunity play. The incumbent is optimizing the past; the AI-native is autonomously acting on the future.
How Can Established Companies Think Like AI Natives?
1. Rewrite your Architecture as an AI-Native would
Over time, most processes start to serve the process itself, with the original end goal buried under layers of accumulated complexity. Instead of optimizing these fragments, redefine the end goal and redesign the entire value chain as an AI-native startup would.
Legacy systems were designed around human limitations. Our need for aggregated summaries, sequential processing, and simplified interfaces. AI-native architecture inverts these assumptions entirely.
Take data analysis and planning. Today’s analysts gather data from multiple sources, aggregate it into digestible summaries, then multiple analysts coordinate and then generate insights to drive decisions. This creates three critical problems: data sits in disconnected silos, analysis is reactive rather than predictive, and every insight requires manual synthesis.
An AI-native approach flips this sequence. Instead of aggregating first then analyzing, it processes granular data directly and aggregates only for human consumption.
Consider how these systems handle revenue decline differently:
Legacy: Sales drop 15% → Analysts investigate → Discover enterprise churn → Find implementation issues → Q4 pipeline already affected
AI-native: System monitors disaggregated signals → Detects support ticket sentiment decline → Correlates with implementation delays → Flags at-risk accounts → Triggers proactive interventions before churn
Traditional insurers exemplify this gap. They spend weeks processing claims through legacy systems, with agents manually transcribing calls and entering data into forms. An AI-native insurer will deploy voice agents that capture details during customer calls, automatically structure data, and populate multiple systems simultaneously.
For decades, business intelligence promised to connect organizational dots but failed due to rigid, pre-programmed logic. AI agents can maintain context across hundreds of data sources and adapt analysis in real-time, making organizational intelligence possible at unprecedented scale and speed.
2. Make AI a 100x Multiplier for Previously Unsolvable Problems
In the current efficiency paradigm, AI’s multiplier effect is 1:1. Co-pilots are perfect examples of this. Depending on the area, productivity boosts range from 10-50%. Even if AI fully replaced a user’s work, that’s still 1:1 leverage, just solving problems already being solved today, just faster or cheaper.
We need to use AI to solve the unsolved problems. Think of challenges that need large numbers of people working together, but where two failure modes occur: either there’s no funding to pull enough resources together, or process friction scales exponentially as more people are added, so the problem never gets solved.
These are places where AI can provide 100x or 1000x leverage. Human experts can orchestrate teams of AI agents to attack problems in parallel, not in sequence. This transforms the speed of complex problem-solving.
From Serial to Parallel Problem-Solving. Consider the realm of strategic foresight and innovation, traditionally constrained by human bandwidth. A strategy team might spend a quarter modeling just two or three potential futures. With AI, they can run thousands of market simulations to wargame competitive responses, model the impact of geopolitical events, or test supply chain resilience, moving from a handful of static scenarios to a dynamic, living map of risks and opportunities. This same multiplicative power applies to ideation. Instead of a brainstorming session limited by the four people in a room, AI can be tasked to embody a diverse array of personas, e.g. a skeptical CFO, an early-adopter customer, a cautious regulator, a rival CEO and pressure-test a new product idea from every conceivable angle. This isn’t merely accelerating an existing process; it’s multiplying the cognitive diversity available to a team by orders of magnitude, unlocking a new scale of strategic thinking and creativity.
This isn’t about making one person more productive, it’s about solving problems that were previously impossible due to coordination complexity or resource constraints.
3. Transform AI from Great Thinker to Great Doer
Most organizations are still thinking of AI as primarily “thinker”: a tool for analyzing data and making recommendations. The third vector provides AI with the right tools to actually go ahead and “do” the job. This area is in its infancy, but AI labs are investing enormous energy here.
The Autonomous Response System: For very specific use cases where guardrails can be strongly defined, AI moves from advisor to executor. Instead of alerting you that supply chain disruption is likely, the system automatically reroutes shipments, adjusts inventory levels, updates customer communications, and modifies production schedules, all before human managers finish processing the initial alert. Similarly instead of generating an Opex report, provided with the right tool, AI can make Opex budget reallocations for lower risk areas.
The key is creating clear boundaries and verification systems. AI operates autonomously within defined parameters but escalates decisions that exceed its authority.
4. Make AI the Ultimate Silo Breaker
One of the biggest challenges in any organization is silos. They exist because individuals and groups are constrained in their capacity to absorb massive context and connect dots across functions. Both are things AI excels at.
No problem is ever just a sales problem, or just a product problem, or just a finance problem. They’re all business problems. To solve business problems, you need to look at all aspects, draw linkages, infer true pressure points, and design holistic solutions.
Cross-Functional Intelligence: AI systems can simultaneously maintain awareness across sales performance, product usage patterns, customer support volumes, financial metrics, and operational data. When customer acquisition costs spike, instead of treating it as a marketing problem, AI can identify whether the root cause lies in product-market fit, competitive positioning, operational inefficiencies, or market timing; and then coordinate responses across all relevant functions.
Where Should Leaders Start?
Navigate the Complex Build vs. Buy Landscape
The current vendor landscape disappoints in three critical areas: surface-level capabilities (most are just interfaces with basic AI summarization), point solutions that ignore interconnected enterprise problems, and limited ability to factor in organizational nuances.
However, the integration challenge cannot be underestimated. Many industries with complex legacy infrastructure like financial services or insurance require sophisticated middleware that can read from and write to multiple systems simultaneously. This integration complexity often becomes the primary moat as foundation models commoditize.
Start by identifying high-friction, high-value processes and building focused capabilities internally. This develops understanding of value levers, infrastructure requirements, and organizational changes needed. Only then can you effectively evaluate external platforms or build the integration layer that makes AI transformation possible.
Start with High-Value Wedges, Not Broad Transformations
The most successful AI-native companies won’t try to replace entire systems overnight. Instead, they identify high-friction, high-value workflows where they can capture data at the point of creation, upstream of existing systems of record.
Focus on workflows where most valuable interactions happen through voice, email, or messaging. These represent opportunities to capture and structure data that currently gets lost or requires manual entry into legacy systems. For example, customer service calls that generate insights never captured in CRM systems, or sales conversations that provide competitive intelligence buried in call summaries.
The key is building integration capabilities alongside your AI solution. Without seamless read/write access to existing systems, even the most sophisticated AI remains a disconnected tool rather than a transformative platform.
Redesign Roles and Cultivate New Competencies
For many jobs, core tasks will fundamentally change. A financial analyst won’t primarily crunch numbers, they’ll look at numbers, make connections, and drive strategic changes. We’re entering an age of builders and scaled executors, moving from report generation to action enforcement.
The Omni-System Organization: We’re moving toward functionless and omni-system organizations. Imagine teams and individuals owning the full stack of business problems, not just functional slivers. AI agents become the functional workers; humans become orchestrators and bosses of these agents.
The AI System Designer: It’s going to be hard for LLMs to self-architect perfectly in every organizational context. So analysts who understand company data and constraints become AI System Designers. They define systems of AI Agents, Data Sources, Tools, and verification rubrics. Under these constraints, agents get to work.
These professionals manage dozens of such systems—very similar to managing multiple Excel workbooks and sheets today, but exponentially more powerful.
Reimagine Your Economics
Prepare for a fundamental shift from heavy OpEx to a more CapEx-like environment. CapEx on technology, CapEx on building agents that amortize over time.
Digital Labor as Asset Class: “Digital labor”—AI agents acting as workers—could become a huge new asset class. Instead of renting human labor continuously, you invest in building intelligent systems that improve over time. Unlike employees who require ongoing salaries, these digital workers represent capital investments that scale without proportional cost increases.
This creates entirely new competitive dynamics. Organizations that invest early in sophisticated AI systems build compounding advantages as their digital workforce becomes increasingly capable.
The Choice That Defines Your Future
The window for strategic AI positioning is narrowing rapidly. Companies focused solely on efficiency gains will find themselves outflanked by competitors who’ve embraced opportunity thinking. The pace of change means waiting six months allows competitors to build use cases, infrastructure, and policies that create sustainable advantages.
The future of work implications vary dramatically by function and industry, with repetitive, knowledge-work-intensive sectors facing the greatest transformation potential. For senior leaders, the strategic imperative is clear.
The defining question is no longer ‘How can AI make us faster?’ The question that will determine competitive advantage for the next decade is: ‘What can we do now that was previously impossible?’ Organizations that act now to build AI-native capabilities will create sustainable moats. Those that wait will find themselves competing on commoditized services while AI-native companies capture the most valuable opportunities.
Shreshth Sharma is a Business Strategy, Operations, and Data executive with 15 years of leadership and execution experience across management consulting (Expert PL at BCG), media and entertainment (VP at Sony Pictures), and technology (Sr Director at Twilio) industries. You can follow him here on LinkedIn.
