The AI race is evolving, and a winning strategy must, too
Moveo.AI Team
1 de setembro de 2025
in
✨ AI Deep Dives
The recent headlines are grim: AI stocks are volatile, and a study from MIT reports a staggering 95% of generative AI pilots are failing to deliver significant financial returns.
Yes, this might feel like a bubble bursting, but it is, in fact, an incredible opportunity. The initial gold rush is over, and now is the time for clear-eyed, strategic action. The foundational technology has matured, and a playbook for success has been revealed.
There is a short window to leap forward from the competition who might be caught in the headlines and missing the substance.
The much-hyped competition among tech giants has reached a point of convergence, not a breakaway. The anticipated leap of GPT-5 did not materialize as a revolutionary advancement. Instead, major LLMs are clustering with specialized differentiations (e.g., coding, multi-modality), not core functionality.
The early sprint is over, giving way to a more disciplined, long-term marathon. This new phase means the time for "waiting for the next big thing" is past. The technology is mature enough; the key is how it is applied.
The agentic advantage: why "buying" is more successful
The most critical finding from the MIT study is not about the technology itself, but about the implementation model.
The report reveals a powerful trend: the "buy" approach beats "build," with purchased tools from specialized vendors succeeding at a 67% rate compared to just one-third for internal projects.
This disparity isn't a coincidence. It reflects the value of specialized solutions that are designed from the ground up to solve a single, clearly defined problem, rather than trying to adapt a general-purpose model to a complex business workflow.
This strategic approach, which is often powered by agentic, task-specific AI, is what separates the successful pilots from the rest.
Prompting isn't production
For mission-critical operations like financial services and debt collection, the simplistic "prompt and pray" approach is a liability. LLMs are extraordinary probabilistic engines, but they are not deterministic workflow engines.
Embedding business logic in natural language is a risk that cannot be afforded. This method:
Introduces ambiguity and catastrophic hallucinations.
Fails to enforce a sequence of critical steps.
Lacks the audibility and control required for regulated industries.
Is notoriously difficult to maintain and scale.
The hybrid imperative: from Black Box to Blueprint
The winning strategy for enterprise AI lies in a multi-agent hybrid architecture.
This model separates the probabilistic from the deterministic. Specialized LLMs are used for what they do best: understanding, planning, and responding to complex inputs. Traditional software, flows, rules, and policy gates are used for what they do best: ordering, verification, and ensuring compliance.
This approach combines the human-like intelligence of AI with the unwavering reliability of traditional code.
The finish line is closer than you think (and what to do next)
The market is ripe for disruption, not by a single new model, but by companies bold enough to implement a smarter, more targeted AI strategy right now.
The foundational technology has matured, and the playbook for success has been revealed. The opportunity isn’t about waiting for a new breakthrough, but about seizing the one that’s already here: strategically applying proven, agentic solutions to unique data.
But organizations still need to close the internal divide between commercial strategy and technical execution: very often, commercial leaders are investing in AI without fully understanding the technology's breaking points, and technical teams are building solutions without clear, business-driven guardrails.
In order to tackle this problem and cross this divide, we will be introducing a blog series designed for that purpose. We'll show you why simply "prompting" a model is a dead end and how to build a robust, multi-agent system that delivers on AI's promise.
AI Deep Dives Series
For commercial leaders
This part of the series "AI Deep Dives" is for you. We'll cover the business challenges and strategic questions you must ask to ensure your AI investment pays off.
Chapter 1: Quick background: What RAG is (and isn’t)
Key Theme: Retrieval-Augmented Generation (RAG) improves LLM answers by grounding them in a company's own documents.
Enterprise Impact: While RAG enhances freshness and relevance, it doesn't guarantee correctness or compliance, making it insufficient on its own for critical business tasks.
Chapter 2: The great AI debate: Wrappers vs. Multi-Agent Systems in enterprise AI
Key Theme: The two main ways to integrate LLMs are as a single wrapper or as a component in a multi-agent system.
Enterprise Impact: The wrapper approach, which tries to do everything with one large prompt, is fragile and risky, whereas a multi-agent system with specialized agents offers planned, observable, and governable outcomes.
Release: September 5th
Chapter 3: The Problem with Prompt & Pray
Key Theme: Relying solely on a "smarter" LLM is a flawed strategy because these models are black boxes that can hallucinate and cannot reliably encode complex business processes.
Enterprise Impact: This approach is unacceptable for critical functions where accuracy must be near 100%, leading to a loss of trust, regulatory scrutiny, and catastrophic errors.
Release: September 10th
Chapter 4: Debt Collection: Why sensitivity and structure matter
Key Theme: Regulated and emotionally charged domains like debt collection require strict enforcement of tone, policy, and process.
Enterprise Impact: The "prompt and pray" method can easily lead to compliance failures, such as using coercive language or skipping required steps, resulting in complaints, fines, and reputational damage.
Release: September 10th
For decision-makers who need to understand the "How"
This part of the series is for the builders. We’ll dive into the architecture, best practices, and why traditional methods fall short in enterprise environments.
Chapter 5: From Prompt & Pray to “Tools & Pray”
Key Theme: While function calling allows LLMs to interact with tools, it still relies on the model to select and use them correctly, a process that lacks deterministic control.
Enterprise Impact: This approach is insufficient for multi-step, high-risk processes, as it cannot guarantee the correct order, validation, or compliance of actions like payment creation or authentication.
Release: September 10th
Chapter 6: How function (tool) calling works (and where it breaks)
Key Theme: Function calling works well for simple, low-risk tasks but fails in enterprise settings due to its inability to handle complex workflows, enforce argument correctness, and maintain a strict sequence of events.
Enterprise Impact: Businesses cannot rely on function calling alone for critical operations that require strict sequencing and validation, as the model might bypass essential steps like authentication or consent.
Release: September 17th
Chapter 7: The Moveo.AI approach (deeper)
Key Theme: A hybrid architecture that combines specialized LLMs with deterministic dialog flows offers the best of both worlds.
Enterprise Impact: This system provides a safe, auditable, and maintainable framework by using separate agents for planning, response, compliance, and insights, while flows enforce the business logic and guardrails.
Release: September 17th
Chapter 8: Conclusion: From “smart prompts” to smart systems
Key Theme: The fundamental shift needed is from viewing LLMs as a complete solution to using them as one component within a sophisticated software system.
Enterprise Impact: Adopting a multi-agent, hybrid architecture is essential for any enterprise that needs to deploy AI with the reliability, security, and governance required for high-stakes business functions.
Release: September 17th
Start building your AI strategy now!
As we've seen, the key to success isn't waiting for a miracle technology, but rather intelligently applying the solutions that already exist.
The winning strategy goes beyond simple prompting and the "all-in-one" approach. It lies in adopting a hybrid, multi-agent architecture that combines the intelligence of LLMs with the reliability of deterministic systems.
It's time to move from theory to action. Organizations that grasp the importance of a strategic, targeted AI approach will be the ones to stand out. If you're looking to go beyond the basics and build robust, secure, and manageable systems, we invite you to dive deeper with our "AI Deep Dives" series.
Discover the "how" with our chapters for business leaders and explore the architecture and best practices in the chapters for developers. The future of your company's AI success depends on your ability to act now.