If the past few years were about understanding what AI could do, we’re now entering the phase where it becomes inseparable from how we live and work.
In 2023, organisations were learning. In 2024, they were experimenting. By 2025, pacesetters began to realise value at scale. Now we’re in the embedding phase—where AI shifts from discrete projects to operational infrastructure woven into the fabric of business, reshaping industries, reimagining customer experiences, and redefining how people work.
Leaders are no longer asking whether to use AI, but how to build organisations ready to thrive when it underpins every process and decision. This maturity transition surfaces four critical readiness tests—dimensions where the gap between experimental and operational AI is widest, and where most organisations are unprepared.
#1: Are Your Interfaces Ready for Multimodal Work?
We’re entering a new era of work without boundaries — where the tools adapt to the way people think, speak, and create. For years, work was shaped by the limits of its interfaces: screens, keyboards, and endless tabs. Those limits are already beginning to fade.
The modern enterprise UI is becoming multimodal by design. IDC predicts that by 2028, 80% of enterprise AI systems will be capable of processing multiple types of input. Meanwhile, the London School of Economics recently found that Generation Alphas won’t be communicating with their managers by email by the time they enter the workforce. Whilst I don’t know that email is ready to go away just yet, communication — with each other and with AI — is definitely not restricted to text. Voice, image, clicks, text, and video can coexist in a single, intelligent workspace where every interaction feels natural, fluid, and intuitive. Instead of switching between disconnected apps, employees engage through one unified experience that understands context and intent.
This is already transforming how work gets done. Teams can brief projects through conversation whilst AI captures notes, updates documents, and builds visuals in real time. Service agents move seamlessly between chat and voice, whilst AI anticipates next steps and retrieves data instantly. Analysts can ask questions aloud, then explore insights visually, all in the same place.
This shift signals a new relationship with technology. When the interface itself becomes multimodal, AI doesn’t disappear; it integrates. It becomes part of the rhythm of work: accessible, actionable, and geared to the way we think and operate.
The Readiness Question: Can your current technology infrastructure support multimodal inputs across your core workflows? Are your teams prepared to work in environments where voice, text, and visual inputs coexist seamlessly?
#2: Can You Govern at the Speed of Innovation?
As AI becomes core to how organisations operate, leaders are facing a growing challenge: how to maintain trust without slowing down innovation. Across EMEA, this balance between governance and speed is already becoming the defining measure of AI maturity.
The EU AI Act marks a turning point that moves regulation from theory to practice. But rules alone won’t create responsible AI. The real test is how organisations translate compliance into everyday practice, embedding accountability and transparency into workflows, data, and decisions.
The University of Oxford’s Annual AI Governance Report 2025 found that leading organisations are embedding governance directly into workflows, not treating it as a compliance exercise. In doing so, they’re maintaining innovation speed whilst reducing AI-related risk.
The leaders who succeed are those who treat governance not as a brake, but as an engine of trust and resilience. They’re building cultures where transparency, explainability, and ethical use are built in, not bolted on. They use clarity to move faster, not slower. Doing this requires a central, single-platform lens of LLMs, AI agents and workflows.
This is what separates compliance from competitiveness. The strongest organisations are proving that trust and velocity can scale together, and that disciplined innovation is the foundation of sustainable growth. This transition demands both discipline and agility. AI must remain fast enough to drive innovation, yet be governed tightly enough to earn trust. The leaders who get this balance right are defining what operational AI maturity looks like, proving that responsible AI and rapid progress can coexist.
The Readiness Question: Is governance embedded in your AI workflows by design, or treated as a compliance afterthought? Can your organisation maintain innovation velocity whilst meeting evolving regulatory requirements?
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#3: Can You Manage Shadow AI Before It Becomes a Crisis?
As AI becomes more embedded, we’re seeing the rise of Agentic Platforms — networks of intelligence that blend human and machine work to drive speed, accuracy, and innovation. These agents are increasingly operating alongside people, managing workflows and simplifying complexity — not to replace human judgement, but to strengthen it.
Yet, as this new layer of work evolves, so does a new layer of risk. The challenge is shifting from “shadow IT” to shadow AI — models and agents developed outside governance frameworks. This creates vulnerabilities for compliance, privacy, and security. Although regulations are evolving across regions, innovation is already moving faster than policy. CIOs and boards need to anticipate, not react, staying one step ahead of regulatory change to avoid future disruptions. Agility is the differentiator.
The leaders who succeed are doing so by adopting flexible, adaptive platform architectures, able to connect data, governance, and decision logic by design. These platforms allow organisations to monitor, verify, and coordinate AI activity across every function, ensuring that trust, compliance, and performance advance together.
The Readiness Question: Do you have visibility into all AI agents and models operating across your organisation? Can you monitor, verify, and coordinate AI activity to prevent shadow AI proliferation?
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#4: Is Your Workforce Ready for Blended Human-AI Environments?
AI is no longer sitting beside work—it’s beginning to flow through it. What began as an extra layer of efficiency has evolved into operational intelligence, helping organisations move faster, make better decisions, and focus on what matters most.
This evolution isn’t about moving from ChatGPT on your phone to your enterprise desktop. It’s about learning to work in a blended environment, where AI is seamlessly integrated into daily tasks. This is dependent on identifying the right AI practice for each specific use case; and orchestrating your agents for each task to work together for real business outcomes, supported by the industry, context and personalised knowledge of your work. From summarising meetings to drafting insights or orchestrating across systems, people and AI are increasingly sharing the same workplace — each amplifying the other’s strengths.
There’s a great example of this shift already in progress in the life sciences sector. AstraZeneca, the leading biopharmaceutical company, has a bold ambition to launch 20 new medicines by 2030 and find remedies for rare diseases that affect millions worldwide. AstraZeneca is connecting every layer of work to AI — from lab operations to onboarding — freeing capacity, shortening discovery cycles, and accelerating time-to-market for critical therapies. The company is already saving over 30,000 hours each year, with tasks that once took half an hour now completed in seconds, speeding research and decision-making across the enterprise.
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For individuals, this shift is deeply personal. The most effective employees are becoming those who can work fluently with AI — managing their own agents, prompting effectively, and knowing when to trust, question, or redirect automated output. Many organisations are already recognising this by making AI proficiency part of performance and evaluation metrics.
For the new generation entering the workforce, this won’t feel new; it will feel natural. They expect to collaborate with intelligent systems as part of how work gets done. For organisations, success depends on how seamlessly they enable this human-AI partnership — not as a tool to adopt, but as an environment to master.
The Readiness Question: Are you developing AI fluency across your workforce? Is AI proficiency integrated into performance metrics, hiring criteria, and professional development programmes?
The Maturity Gap: Where Most Organisations Stand
These four readiness tests reveal a common pattern: most enterprises have succeeded at AI experimentation, but few have built the organisational infrastructure required for the embedding phase.
The organisations that close this readiness gap—by integrating multimodal interfaces, embedding governance into workflows, managing shadow AI proactively, and building AI-fluent workforces—won’t just adopt AI. They’ll operate in a fundamentally different way, with speed, trust, and capability advantages that compound over time.
The question isn’t what AI will do in the future. It’s whether your organisation is ready for what it’s already becoming.
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