Your AI Probably Failed. Here's Why That's Good News.
- Severin Sorensen
- 1 day ago
- 4 min read
If you've championed an enterprise AI initiative that went sideways, you're in good company. You saw the promise, secured the budget, and picked a "best-in-class" vendor. The pilot looked great. Then you tried to scale.
Workflows sputtered. Performance was throttled to the speed of a "dot matrix printer" during peak demand. The API went down during a critical window. The quality of the model seemed to degrade over time. The promised ROI never materialized, leaving you with budget fatigue and a deeply skeptical organization.
Sound familiar? A recent Gartner study found that just 15% of IT leaders are actively considering or deploying their AI Agent technology.
Here’s what the study doesn’t say: The problem wasn't the technology. It was the strategy you were sold.
The Illusion of the "One Best AI"
For the past several years, the dominant strategy, pushed by vendors and consultants, was to find the single "best" AI model and align your entire organization to it. This made sense in a consolidating market where one or two players seemed destined to win.
But that market is gone. The question "Which AI is best?" is now fundamentally flawed. The right question is, "Which AI is best for this specific task?"
We've entered a new, polycentric AI landscape where different models have developed highly specialized, best-in-class strengths. Relying on a single model today is like building a house with only a hammer: you sacrifice the precision, efficiency, and superior results that a full toolkit provides.
From deep research to autonomous action, the 2026 AI landscape has evolved into specialized archetypes. Whether it’s The Scholar’s vast knowledge or The Executor’s hands-free task completion, these models represent a shift from general assistants to elite, purpose-built experts tailored for every professional demand.
Your past implementation failures weren't a sign that AI is a dead end. They were an early warning signal that a strategy based on vendor dependency is inherently fragile.
From Fragile to Antifragile: A New Strategic Playbook
The companies quietly pulling ahead aren't just using AI differently—they are thinking about AI differently. They've stopped betting on a single horse and have started building a stable.
They are adopting an AI-agnostic, multi-model portfolio strategy.
Think of it like your investment portfolio. You would never bet your entire retirement on a single, volatile stock. That's speculation, not strategy. You diversify. You build a portfolio of assets that balances risk and optimizes returns across different market conditions.
Why should your AI strategy—a core driver of future growth—be any different?
An AI portfolio strategy does two things:
1. It builds resilience (your insurance policy).
When you have multiple providers, a single point of failure—an outage, a rate limit, a quality decline—is no longer a catastrophe. It's a manageable event. Workloads can be seamlessly shifted to a better-performing alternative. This diversification is your insurance against the inherent volatility of the AI market. But unlike a traditional insurance policy, which is a pure cost, this one also improves performance.
2. It creates competitive leverage (your offensive advantage).
Once you have resilience, you can go on offense. An AI-agnostic strategy allows you to route every task to the objectively best model for the job. You can have Claude Opus 4.5 draft the code, Gemini 3 Pro analyze the multimodal inputs, and an execution-focused agent like Manus assemble the final report—all at the same time. This parallel, specialized approach provides a multiplicative, not just additive, productivity gain.
The Market is Already Validating This Shift
The market is providing clear signals:
Market Share is De-Concentrating: Over the past year, OpenAI's dominant traffic share has eroded from approximately 90% to 70% as specialized competitors gain ground, according to data from SimilarWeb. The market is voting for specialization.
Big Tech is Buying, Not Building: Meta's recent acquisition of Manus, a proven multi-model agentic platform, is the ultimate validation. Recognizing the limitations of a single-model approach for enterprise use, Meta chose to buy a multi-model orchestration strategy rather than risk building on a single foundation.
You Don't Need to Rip and Replace. You Need to Augment and Diversify.
For leaders who have been burned, the thought of another massive AI initiative is exhausting. But the portfolio approach doesn't require starting from scratch. It's an incremental strategy:
Start Small: Identify one high-value, low-risk use case where your current AI solution is underperforming.
Augment: Introduce a second, complementary model specifically chosen for its strength in that use case.
Measure: Track the performance gains and resilience improvement. Prove the model with a quick, contained win.
Scale: Use that success to build momentum and gradually diversify your AI portfolio across other use cases.
Modern orchestration platforms can abstract away the back-end complexity, allowing your team to interact with a single, unified interface while leveraging the strengths of multiple providers.
The New Competitive Divide
The strategic divide of the next decade will not be between companies that use AI and those that don't. It will be between companies that remain dependent on a fragile, single-vendor strategy and those that build a resilient, multi-model AI capability.
The Meta-Manus acquisition (Dec 2025) was the "shot heard 'round the world" for enterprise AI. It signaled that even the biggest tech giants realize they can't just build a better chatbot; they need specialized agents that can act autonomously.
Your past failures weren't the end of the story. They were the prologue. They taught you a critical lesson that your competitors may still be learning: betting on a single vendor is no longer a viable strategy. Building a diversified, intelligent portfolio of AI tools is the anti-fragile strategy.
A Final Thought
If you're a leader who is rethinking your AI strategy after past disappointments, I'd welcome a conversation. I'm particularly interested in connecting with executives who have experienced implementation challenges and are now exploring how to build a more resilient and high-performance AI capability.
Comment below or send me a direct message. I'm happy to share a simple framework for assessing whether a portfolio approach makes sense for your organization.
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