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What Does It Really Mean to Be Ready for AI?

Written by Filipa Peleja

being ready for AI

The rapid advances in Artificial Intelligence (AI)—especially in generative AI models for text and beyond—have raised an important question for organizations: What is truly required to be ready to embrace AI in a responsible, ethical, and effective way, while still contributing to business goals? It demands a fundamental shift in mindset—rethinking ways of working, ensuring infrastructure maturity, improving data availability and quality, and most importantly, empowering the people who will use it. In this article, we’ll explore each of these components and their impact on transforming ways of working as AI technology becomes a vital part of the organization’s value creation.

What does it mean to be ready for AI?

It is increasingly evident that organizations are actively examining the transformative potential of Artificial Intelligence (AI) in the workplace — influencing areas ranging from decision-making methodologies to the ways in which individuals acquire new skills and reimagine their professional roles. Not all organizations will follow the exact same path, and we could say some will be able to achieve success stories sooner than others, and behind that we have strategy vision as also, the organizartion’s strategic readiness.

As with any digital transformation, one of the most critical factors is people — fostering a growth mindset and openness to change. The adoption of AI is no exception. Another important consideration, especially given the intensity of current conversations around AI’s impact, is for organizations to position AI as an enabler and a supportive tool. When the focus is placed solely on workforce reduction or cost-cutting, it not only limits long-term growth but also risks creating a profoundly negative effect on employees. As with any technological advancement, people want to feel included, know they are progressing, be recognized and understood, and trust the communities and colleagues they work with.

A very important point to highlight is that being ready for AI is not about chasing trends; it’s about recognizing the value it can bring and developing a strategy-led plan to deliver meaningful impact. Equally important is maintaining a focus on the long-term value AI can offer. This means organizations must already be thinking about how AI can support their strategic objectives and actively creating space for people to contribute ideas — enabling the identification and pursuit of those with the greatest potential impact.

I’d like to bring us back to the importance of organization’s strategic readiness. Strategic readiness for AI starts with a deep understanding of its landscape. The breadth of AI applications is vast, and to fully capture these opportunities, organizations must align their infrastructure and tools to meet AI-specific demands. While AI capabilities continue to advance, one constant remains — data lies at the heart of it all. This isn’t a new idea; as Clive Humby said in 2006, “Data is the new oil”. To truly leverage AI, organizations need a strong, well-structured data foundation that supports long-term innovation – the diagram in the “Data Strategy Pillars” (see Figure 1) aims to illustrate the core pillars that must be met.

data strategy pillars

Figure 1: Data Strategy Pillars


Embracing AI to seize Agentic AI Advantage

Generative AI (Gen AI) has accelerated the adoption and deployment of AI solutions across organizations. One of its most significant potentials lies in its ability to democratize access to advanced AI technologies. This has led to widespread experimentation as organizations explore its true value and potential impact (see Figure 2). However, many challenges remain — chief among them is the difficulty in translating these experiments into measurable value. Most companies still struggle to report tangible contributions resulting from their Gen AI initiatives [3]. There are a couple of identified reasons behind these challenges, and for now I’d like for us to focus on the 3 below.

  • There is a lack of AI engineering talent, particularly MLOps (Machine Learning Operations) engineers, who are essential for properly industrializing, deploying, and maintaining AI solutions in production environments. This expertise becomes especially critical when organizations need to build customized solutions that go beyond out-of-the-box offerings, such as chatbots or coding assistants like GitHub Copilot.
  • Readiness across the key pillars of data strategy is still lacking in many organizations, affecting both data access and quality. This remains a fundamental challenge for most AI solutions — and Generative AI is no exception.
  • Many employees feel uncertain about how AI will affect their roles, leading to resistance that is largely driven by fear of disruption and a lack of understanding or exposure to the technology.

Nonetheless, momentum has continued, and by 2025, we have seen employee capabilities significantly enhanced by the growing availability of Gen AI tools — such as GitHub Copilot for developers — along with experimentation-driven learning. Most importantly, this has led to a noticeable increase in AI familiarity across various organizational domains. As a result, there has been a stronger focus on building training capabilities in three key areas:

1. Prompt Engineering – as AI becomes more democratized, it is essential to lower the barrier to entry by enabling human-AI interaction through natural language. Non-technical users can learn effective prompting techniques to engage with AI systems productively.

2. Model Evaluation for Gen AI solutions – Gen AI can be difficult to trust without clear performance metrics. Organizations are increasingly prioritizing the ability to critically assess and validate these models.

3. Data Governance – while not new, data governance has taken on renewed importance in the Gen AI era, ensuring data quality, compliance, and ethical use.

Figure 2: GenAI has accelerated AI deployment Overall

How to be ready for AI Agents & Process Reinvention?

The first major breakthrough in Generative AI came from Large Language Models (LLMs). However, a key limitation is that they operate within ecosystems largely disconnected from an organization’s internal knowledge. As a result, their impact has been primarily limited to enhancing individual productivity in relatively isolated tasks. AI agents expand the potential of LLMs by enabling them to understand specific tasks, take action, interact with humans and other systems, and — most notably — adapt in real time with minimal human input.

AI Agents have introduced a new paradigm and to truly unlock its capabilities one must go beyond merely incorporating them into legacy processes. As while that may increase speed, it does not harness the full power of agents, who have the potential to reinvent processes that are typically designed around human constraints rather than the agents’ capabilities. In a recent report, “Seizing Agentic AI Advantage”[3], it is explored what this reinvented process can look like and respective impact for a call center scenario (see Figure 3).

Figure 3: Process reinvented for call center case study

How does this shift what we’ve discussed so far? From a technical standpoint, it emphasizes the importance of foundational model requirements for successfully deploying AI agents in production environments. LLMs used by agents must be capable of meeting several demanding conditions. First, when agents are introduced into systems that handle sensitive or public data, it’s essential to ensure compliance with regulations such as data sovereignty and privacy standards, depending on the nature of the data they access. Second, without proper architectural design and operational guardrails, LLMs can incur high costs and may struggle to scale effectively. This makes it necessary to adopt efficient, sparse architectures and to rely on infrastructure managed by experienced professionals. Third, many AI applications, especially at the edge, do not have access to extensive computational resources. To unlock the full potential of agentic AI in these scenarios, models must be lightweight enough to run directly on software or hardware with minimal compute and memory requirements. Finally, in enterprise environments, it is crucial to maintain precise control over how LLMs interact with internal knowledge, as also when real-time responsiveness is required, the models must be both efficient and tightly integrated into existing systems [3].

Figure 4: Requirements for Foundation Models for Agents

Ultimately, we return to where this article began: despite all the technical advancements, the most significant challenge may not be technological, but human. To truly unlock the potential of AI agents, we must rethink how humans and agents coexist — not just in terms of workflows, but in the very nature of collaboration. This involves redefining how we receive input from agents, determining when they should take initiative, and enabling more fluid, shared decision-making. Doing so will require a cultural shift grounded in trust — trust in how agents communicate and in how intuitively they can support and enhance human work in everyday contexts.

Summary

We are living in a time of rapid technological disruption, particularly in the AI landscape, which is impacting everyone’s lives in some way. In this article, we explored what it means for organizations to be prepared to embrace this AI transformation — including the critical requirements for success, the areas where resistance is most likely to emerge, and how those challenges can be addressed. Ultimately, thriving in a world of human–AI coexistence requires not just technical readiness, but a reinvention of how people work. By doing so, organizations can create value while empowering individuals and helping to democratize access to AI technology for all.

[1] In the Age of AI: Will Your Members Still Need you? From Transactions to Trust: Reinventing Member Relationships in the Age of AI, sheepCRM, June 2025

[2] Image “Data Strategy Pillars” from Key Considerations for your Data Strategy in 2025, Mick Wagner, December 2024

[3] Seizing the agentic AI Advantage, McKinsey Report,June 2025

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