AGI: How to prepare for General Artificial Intelligence

Artificial General Intelligence (AGI) is gradually moving from being a hypothesis to becoming a topic of discussion at decision-making tables. The race for this new level of intelligence is already mobilizing billion-dollar investments: just in 2024, Meta announced an investment of $15 billion to develop models with generalist capabilities, while companies like OpenAI, Google DeepMind, and Anthropic vie for the lead in creating systems with human-like reasoning.

The market has made its message clear: AGI is no longer a question of “if,” but “when,” and, above all, “how” it will transform businesses, operations, and strategies. Being prepared from now on is not an exaggeration; it is what will separate those who lead from those who chase in the coming years.

In this article, you will understand what differentiates AGI from traditional AI and how to evaluate your company’s readiness for this leap. We cover investment criteria, roadmap construction, areas with the greatest value potential, and metrics to measure real impact. Happy reading!

What differentiates Artificial General Intelligence from traditional AI?

Artificial intelligence has ceased to be a futuristic concept to become part of the daily lives of companies. However, as this technology advances, it is essential to make a distinction: the difference between traditional AI and the so-called Artificial General Intelligence (AGI).

These two types of AI are often put on the same shelf, but in practice, they operate with quite distinct logics, capabilities, and purposes.

Traditional AI: effective but with a defined scope

Traditional AI is what we widely use in the business world. These are systems developed to solve specific tasks, such as predicting customer behavior, analyzing documents, identifying fraud, or automating operational processes.

It learns from historical data, using algorithms and statistical models that work very well within the domain for which they were trained. For example, an AI that analyzes credit will not be able to understand natural language in a chatbot or optimize production in a factory by itself.

And here is the point: this specialization, while very useful, comes with some important limitations:

• The AI does not “think outside the box.” It follows pre-established rules and patterns;
• Changes in the environment or data often require adjustments or reconfiguration of the model;
• It depends on human supervision and does not learn autonomously in new contexts.

In other words, we are talking about functional intelligence, but strictly shaped to solve specific problems.

Artificial General Intelligence: the next big leap

On the other hand, Artificial General Intelligence is a greater ambition that is still under construction. Its goal is to create systems that learn like humans, with the ability to understand diverse contexts, solve complex problems, and adapt to new situations without depending on specific instructions.

Imagine an AI that not only answers questions but can learn a new language, suggest strategic solutions, or collaborate in business decisions, all based on continuous learning and logical reasoning.

We have not reached that point yet, but this is the frontier that companies like OpenAI, DeepMind, and Anthropic are exploring. AGI promises:

• To learn and adapt to unprecedented scenarios;
• To make decisions based on multiple contexts;
• To operate autonomously and creatively on interdisciplinary problems.

Why does this matter for your business?

For technology and innovation leaders, understanding this distinction impacts practical decisions, investments, and long-term strategies.

Traditional AI is already mature and delivers concrete value. It is with it that automation occurs, scale is gained, and operational efficiency is improved today. AGI, on the other hand, represents a future still under development, with disruptive potential, but one that requires caution, monitoring, and a clear strategic vision.

What criteria to consider before investing in AGI-based solutions?

More and more solutions are emerging that promise “general intelligence,” although much of these promises are still in research or development phases, it is natural for executives and innovation leaders to begin to ask: is it worth investing now in AGI-based technologies?

The answer depends on a thorough analysis. More than just following a trend, it is essential to understand the degree of maturity of the technology, the risks involved, and the alignment with the company’s strategic moment. Below, we highlight the main criteria that should guide this decision.

Real level of technology maturity

First of all, it is essential to investigate whether the proposed solution truly relies on concepts of Artificial General Intelligence, or if it is merely traditional AI with good marketing.

Ask:

• Is the technology capable of learning autonomously across multiple domains?
• Is there evidence of adaptive reasoning or merely pre-programmed task execution?
• Has the solution been validated in distinct contexts with consistent results?

Most current systems, even the most advanced ones, still operate within the limits of traditional AI.

Transparency and explainability

Solutions that approach AGI tend to be complex and often opaque. This can create both operational and regulatory risks. Therefore, prioritize technologies that offer transparency in decision-making processes, audit logs, and the ability to explain how conclusions were reached.

Adherence to your business problem

Not every company will benefit from an AI with generalist reasoning. The most important thing is to assess whether the technological proposal solves a real, relevant, and priority problem for the business.

Useful reflections:

• Does this solution resolve something that traditional AI couldn’t?
• What additional gains does it offer (efficiency, innovation, personalization, decision-making)?
• Is there a measurable ROI in the short or medium term?

How to assess the organizational readiness level for adopting Artificial General Intelligence?

As organizations advance in their digital journeys, interest in AGI (Artificial General Intelligence) solutions is growing. However, there is a gap between technological ambition and the actual capacity for implementation.

According to the AI Readiness Gap report from Huble, although 57% of companies report strong confidence in their AI strategy, only 28.5% consider themselves moderately prepared from a data perspective and an even lower number, 8.6%, believe they are fully ready.

This data makes it clear that before investing in technologies with the level of complexity and autonomy of AGI, it is necessary to assess organizational readiness.

Below, we explore the main criteria for making this diagnosis with objectivity.

Digital and technological maturity

Companies still facing challenges with decentralized data, legacy systems, or low automation will hardly be able to derive real benefits from solutions with a high degree of autonomous intelligence.

Signs of readiness:

• Unified and updated data infrastructure;
• Flexible and integrable technological architecture;
• History of successful adoption of solutions based on traditional AI.

Culture oriented to experimentation

The adoption of AGI involves uncertainties, learning cycles, and constant adaptations. An organizational culture averse to risk or highly hierarchical tends to stifle such initiatives.

What to look for:

• Environments that encourage innovation and controlled testing (e.g., sandboxes, labs);
• Active collaboration between business and technology areas;
• Tolerance for error as part of the evolution process.

Governance and responsibility over AI

The greater the autonomy of AI, the more robust the governance structure accompanying it should be. It is essential that the organization has clear policies concerning ethical use, security, and compliance regarding artificial intelligence.

Key elements:

• Committees or teams responsible for responsible AI;
• Regular assessments of ethical and social impact;
• Adoption of frameworks such as explainable AI (XAI).

Leadership capability and mindset

Decision-making about AGI needs to be in the hands of leaders who understand, at least conceptually, the limits, potentials, and risks of this technology.

Assess whether:

• Leaders are up to date on the topic;
• There is investment in ongoing training regarding AI and innovation;
• Leadership can translate technological opportunities into business strategies.

How to build a roadmap for AGI adoption?

Building an AGI action roadmap is not about implementing the impossible, but rather creating the conditions for your organization to evolve consistently toward the next generation of artificial intelligence.

Below, we show how to structure this path progressively, realistically, and aligned with your business maturity.

Phase 1: Simulate, do not implement

At this moment, most so-called “AGI” solutions are, in practice, language models with generalist characteristics, such as multimodal LLMs. The first phase of the roadmap should not seek full adoption, but instead simulations of scenarios with a high degree of complexity and ambiguity — where AGI would have real value.

Practical example:

• Run a pilot with an LLM to act as a decision advisor in areas with a high volume of interdependent variables (e.g., global supply chain, mergers and acquisitions, regulatory scenario planning).
• Measure: the model’s ability to transfer learning between domains, handle contradictions, explain decisions.

Deliverable: report on the AI’s generalization capacity vs. real business expectations.

Phase 2: Redesign data pipelines for cognitive flexibility

AGI relies on more than just “good data” — it requires relational, interconnected data, interpretable across different domains. This calls for an architecture that supports cross-learning.

Critical decision here:

• Adopt semantic layers that allow for different sources (ERP, CRM, unstructured documents, IoT sensors) to be understood by the same system.
• Start with “data stories,” not just “data lakes.”

Deliverable: new data architecture blueprint oriented towards cognitive interoperability.

Phase 3: Introduce microAGIs in limited contexts

Before thinking of an AGI capable of operating broadly, it is possible to introduce “microAGIs” — agents with restricted autonomy, but capable of learning, adapting, and interacting with multiple systems.

Practical example:

• Create an agent that learns to coordinate different forecasting models (demand, weather, logistics availability) and automatically adjusts operational parameters.
• Unlike an RPA, this agent decides based on multiple sources and criteria it learns itself.

Deliverable: first operational autonomous agent validated by humans.

Phase 4: Establish a cohabitation model between humans and AGI

The critical point of AGI adoption is not technical, but organizational: who decides what? How to ensure shared responsibility? How do humans supervise something that reasons by itself?

This phase involves:

• Creating simulations of shared decisions (real human-in-the-loop);
• Defining permitted vs. blocked action zones;
• Establishing protocols for when the AI presents unexpected behaviors.

Deliverable: structure of “AGI Governance Zones” — a matrix that defines the scope, limits, and responsibilities of the AI.

Phase 5: Evolve from experimentation to systemic influence

The roadmap does not culminate in the replacement of human decisions but with the systemic influence of AGI on organizational intelligence. This means AI begins to act as a continuous organizational learning agent, proposing adaptations, optimizing complex decisions, and challenging operational biases.

This phase relies on:

• Feedback loops between AI, data, and human decisions;
• Tools to measure impact not just financially, but adaptively (resilience, agility, innovation).

Deliverable: AGI maturity scorecard, with qualitative and quantitative impact indicators.

Which business areas can generate the most value with AGI applications?

AGI will be most valuable where traditional AI fails: in ambiguous, interdisciplinary environments, with a high volume of variables and little predictability. Areas like strategy, supply chain, R&D, customer service, and engineering are natural candidates, as they combine cognitive complexity with direct impact on business performance.

These functions require adaptive reasoning, contextual interpretation, and the ability to learn beyond historical data. Below, we detail five areas that can generate more value with AGI applications. 

Strategic planning and corporate risk management

Today, complex strategic decisions, such as market expansion, mergers, dynamic pricing, or management of systemic risks, require human analysis, as they involve ambiguous, unstructured, and interdependent variables.

AGI could cross economic, political, environmental, and market behavior variables in real-time, learning from previous events and proposing adaptive and unprecedented strategies based on general reasoning.

Operations in high variability environments

Global supply chains deal with multiple dynamic factors: weather, demand, politics, transportation, pricing, geography. AGI could operate with real autonomy, learning from the environment and replanning routes, stocks, or suppliers in the face of unexpected disruptions — without relying on reprogramming. This shifts the supply chain role from reactive to proactive.

Research & Development (R&D) and product innovation

Innovating requires crossing knowledge areas, testing hypotheses, and adapting solutions.
AGI could act as a digital researcher, connecting insights from different areas (biology, materials, design, market) and proposing innovations based on patterns that were not explicitly taught. This accelerates innovation cycles and reduces R&D time.

Customer service with personalization and continuity

Today, automated customer service is limited: chatbots do not maintain context for long periods and fail in more emotional or ambiguous interactions. AGI could understand a customer’s entire history over the years, interpret emotions, resolve out-of-script issues, and maintain natural conversations creating truly consistent, empathetic, and personalized experiences.

Systems engineering and solving complex problems

Engineers today use GenAI to generate code or review snippets, but the systems architecture process, solving interdependent bugs, or complex technical decisions still requires human analysis. AGI could understand the system as a whole, identify bottlenecks, suggest complete refactoring, and design technical solutions based on business objectives rather than just code.

What metrics to track to measure impact and scalability of projects with Artificial General Intelligence?

Strategic value generated (business impact)

AGI projects should be evaluated not only for operational gains but for the value they generate in decisions, innovation, or competitive advantage.

Recommended metrics:

• Decision Quality Score: assesses the impact and consistency of the decisions generated or supported by AGI over time.
• Adaptive response time: how long it takes for AGI to propose a solution in response to a new scenario (not foreseen in the original data).
• Innovation Cycle Time: reduction in the time required to generate, test, and validate new ideas, products, or hypotheses.
• Return on Intelligence – RoI: combined metric of value generated by AGI’s autonomous decisions or solutions relative to the system’s cost.

Capacity for generalization and knowledge transfer

One of the main promises of AGI is learning in one domain and applying it in another. This generalization needs to be measured objectively.

Recommended metrics:

• Cross-domain learning rate: speed at which the system learns and applies acquired knowledge in one context to solve problems in another.
• Zero-shot Performance: AGI’s ability to execute tasks for which it was not explicitly programmed or trained.
• Cognitive elasticity: variation in performance in response to changes in environment, rules, or objectives — the more stable, the more scalable.

Operational and technical scalability

Even with great potential, a project with AGI will only be viable if it can be scaled in a controlled, safe, and efficient manner.

Recommended metrics:

• Cost per assisted/autonomous decision: compares the operational cost of AGI with the value of the decisions produced or supported by it.
• Progressive autonomy rate: increasing percentage of tasks or decisions made without human intervention.
• Integration time with new systems or domains: measures the speed at which AGI can be applied in new areas of the company.
• Computational consumption per resolved task: technical efficiency, essential for evaluating long-term scalability.

Traditional metrics like ROI, cost reduction, or productivity still hold value, but are insufficient to capture the real impact of AGI. The focus should be on the cognitive quality of AI, its adaptability, and how much it increases the collective intelligence of the organization.

Successful AGI projects will be those that, besides delivering immediate value, learn continuously, integrate into new contexts, and operate sustainably at scale.

How to turn AGI into a competitive advantage?

Most companies are looking at AGI as a way to do better what they already do today. But the real competitive advantage will not lie there. It will emerge when AGI is used to create new ways to compete, new offers, new business models, and new ways of thinking strategy.

Below are three paths that remain overlooked — but can generate unique and hard-to-copy advantages:

Build organizational intelligence as a proprietary asset

AGI allows something that has never been possible at scale: accumulating, crossing, and evolving the company’s knowledge over time, in a living way. If well used, it can become a unique corporate “brain”, trained with history, decisions, learning, business context, culture, and strategy — to which no competitor has access.

This intelligence refined over the years will be more valuable than any off-the-shelf AI model on the market.

Competitive differential: While other companies use AGI as a tool, you transform the organization’s very intelligence into a proprietary strategic asset.

Shift the competition axis to cognitive speed

Currently, companies compete on price, scale, brand, or efficiency. With AGI, a new axis emerges: the ability to understand and react to the environment faster than others based on autonomous analyses, adaptive reasoning, and scenario simulations.

AGI makes it possible to test ideas in minutes, adjust decisions in real-time, and anticipate changes before the market reacts.

Competitive differential: The speed of learning and response becomes an advantage in itself — and companies that operate with shorter cognitive cycles will dominate more volatile markets.

Create invisible and sustainable asymmetries

The most powerful advantages are those that competitors do not see or cannot explain. By deeply integrating AGI into decision-making, relationships, and innovation processes, your company starts to act differently, more intelligently, more quickly — without appearing to have changed much on the outside.

This asymmetry, based on invisible intelligence, is the type of advantage that is hardest to imitate, because it is not in the product, nor in marketing — it is in the way the company thinks and decides.

Competitive differential: AGI enables a pattern of operation so unique and intelligent that the market takes time to perceive — and by the time it does, it is already behind.

AGI can be used to improve what already exists, but the companies that will truly stand out are those that change the way they compete with it. These are the new frontiers of competitive advantage and those who start to explore them now will have a space that will be much harder to reach later.

Ready to explore the potential of AGI in your business?

Objective can help your company build this path with clarity, security, and strategic focus. We operate with specialized teams in applied AI, software engineering, and value-oriented innovation.

Talk to our specialists and discover how to turn intelligence into a competitive advantage.