Agentic AI Hype vs Reality: Can Your Business Afford to Get It Wrong?

This is an important question that every business leader is considering today.  Underneath it sits critical decisions.  Where on the adoption curve should we be? How will competitors respond? How do we avoid investing too early in technology that isn’t ready yet, or too late, when the competitive advantage has already been lost?

We’ve seen this pattern before. When SaaS first arrived, it promised lower costs, greater flexibility, and faster innovation than traditional on premise software. Those promises were largely real, but adoption was uneven. Some partners pushed aggressively, incentivising sales teams with higher bonuses for SaaS deals. The result? Many companies adopted too soon, found the platforms immature, and paid a price.

That memory lingers. Some leaders are understandably wary of making the same mistake with AI. Yet the risk now is two sided, move too fast and you may waste millions, move too slowly and you risk being overtaken by competitors who deploy AI pragmatically to create real value.

The short answer:

The short answer is that you should be doing something, formulating the right strategy for you based on your unique circumstances.

In the long run, AI will do the vast majority of work that is performed by humans today, both white and blue collar. The disruption will arrive sooner in white collar roles, which are primarily knowledge based and therefore easier to automate through software. Blue collar roles will follow more gradually, as they require AI embodiment in the physical world, robotics, autonomous vehicles, and smart machines.

This is difficult to fully grasp, and many leaders will instinctively resist the idea. Yet the direction of travel is clear, companies that embrace AI will deliver work at a fraction of today’s cost whilst also offering faster, more personalised customer experiences.

I recently read The Diary of a CEO: The 33 Laws of Business & Life by Steven Bartlett, the entrepreneur, investor, and podcaster

Law 5 is: “You must lean into bizarre behaviour.”

When something feels unfamiliar or uncomfortable, resist the instinct to dismiss it. Leaning out is often a sign of arrogance, assuming you already know better. Leaning in, by contrast, is the path to learning and growth.

This is exactly where we are with AI. It can appear bizarre, unbelievable, even unsettling, and that causes many leaders to lean out. This, though, is precisely the moment to lean in. The companies that embrace the strangeness of AI now, rather than resisting it, will be the ones that win.

Why this matters:

Every major technological shift creates winners and losers, delay and denial comes at a cost.

• Kodak was once worth nearly $9 billion before digital photography made its core business obsolete, even though it invented the first digital camera. By 2012, it had filed for bankruptcy.
  

• Blockbuster, valued in the billions at its peak, failed to adapt to streaming and filed for bankruptcy in 2010.

AI represents a shift of even greater scale. The pace of change is faster, the level of global investment unprecedented, and by its very nature AI lowers the barriers to entry. New “AI-first” businesses will emerge rapidly and disrupt incumbents who fail to adapt.

We are already seeing the beginnings of this shift, with companies deploying AI to transform customer service. Klarna now resolves two thirds of its customer chats through an AI agent, doing the work of 700 full time workers, cutting average handling times from 11 minutes to under 2 and saving an estimated $40 million annually. Similarly, Vodafone uses AI powered digital assistants to handle millions of queries each month across multiple languages, reducing costs whilst improving customer satisfaction.

We are now seeing clear evidence that white collar roles, especially entry‑level positions, are being disrupted by AI. Graduate hiring at the Big Four accountancy firms has declined sharply.  KPMG’s intake fell by 29%, Deloitte by 18%, EY by 11%, and PwC by 6%, driven in part by AI automating routine tasks previously assigned to junior staff.  BT has announced plans to cut up to 55,000 jobs by 2030, with around 10,000 of those roles expected to be replaced by AI and automation, especially in call handling and diagnostics functions.  At IBM, AI now manages 94% of routine HR tasks, enabling the automation of hundreds of HR roles, and while the company’s overall headcount continues to grow, hiring for replaceable back‑office roles has been paused or reduced in favour of strategic roles requiring critical thinking.

We stand on the verge of an Agentic AI revolution.  A step beyond today’s copilots and chatbots. Unlike traditional AI systems that wait for prompts and respond reactively, Agentic AI takes initiative: it can plan, make decisions, and carry out complex tasks across different tools and environments with minimal human input. These systems combine reasoning, memory, and action, allowing them not just to answer questions, but to orchestrate workflows, negotiate with other agents, and drive outcomes end to end. This shift promises to transform knowledge work, as software evolves from a passive assistant into an autonomous collaborator.  Think of Agentic AI as digital workers.

The vision is strong, but the question is how mature is Agentic AI today? While the promise is enormous, most current systems remain fragile outside of controlled environments, however they are catching up very fast. They can plan and act, but often struggle with reliability, context switching, and sustained autonomy over long or complex tasks. Early enterprise pilots show value in narrow domains like data retrieval, workflow automation, and customer support, but scaling to mission critical operations is still limited by accuracy, security, and integration challenges. In short, Agentic AI is real and advancing quickly, separating hype from reality means recognising it is still in an early phase, but progressing rapidly.

Agentic AI providers can be divided into three groups:

• LLM Innovators:  Frontier labs like OpenAI, Anthropic, Mistral, and Meta are pushing the boundaries of raw intelligence and setting developer standards.
  

• Agent-First Disruptors:  Start-ups such as LangChain, LlamaIndex and Manus are pioneering applications and experimenting rapidly with new use cases.
  

• Enterprise Providers:  Giants like Microsoft, SAP, Salesforce, Oracle, and Workday are embedding AI agents into trusted workflows and ERP and scaling adoption across huge customer bases.

For business, the most likely source of practical, usable solutions will be the enterprise providers. They already control the systems companies rely on every day, ERP, CRM, HR and finance.  they have the compliance, security, and integration capability needed for large scale deployment. History shows Oracle, SAP, and Salesforce in particular are also highly acquisitive, we should expect them to buy into the agent first ecosystem to accelerate their capabilities.

All of this points to 2026 as the breakthrough year for Agentic AI. Enterprises are currently in pilot mode, but the timelines line up.  IBM expects meaningful benefits from agentic adoption within 18–24 months, Microsoft is weaving agents into Office and MS Dynamics, and Salesforce has announced agentic copilots across its platform. With billions in investment, maturing infrastructure, and incumbents ready to acquire and scale the most promising start-ups, 2026 is shaping up as the year when Agentic AI moves into the enterprise mainstream.

There are lots that companies can do to get ready for this, ensuring that when agentic AI is more mature they can move as quickly as possible to take advantage of it and create a competitive advantage:

• Map opportunities and pain points:  Identify repetitive, cross-system processes where autonomous agents could deliver quick wins.
  

• Data readiness:  ensure data is clean, structured, and accessible so agents can act on it reliably.
  

• Engage with existing providers:  Work with your current solution providers to understand their roadmaps, how they plan to deliver Agentic AI and when.
  

• Run controlled pilots:  Experiment with agent first start ups or sandbox tools to build internal capability, enthusiasm and to learn fast.
  

• Evaluate integration points:  Map where agents will need to connect across ERP, CRM, HR and other core systems.
  

• Upskill teams:  Build literacy in agentic AI among business and IT leaders to accelerate adoption when solutions mature in 2026.  Identity enthusiastic talent.
  

• Model business cases:  Quantify potential cost savings, productivity gains, or new revenue streams to prioritise use cases.  Define and secure funding.
  

• Culture:  Foster a culture where AI is embraced as a tool for empowerment and growth, positioning it as an opportunity to enhance people’s work and create new possibilities, rather than a threat to jobs.

Agentic AI is coming faster than many expect. The direction is clear: by 2026, autonomous digital workers will be embedded into the core platforms businesses already rely on.  Leaders cannot afford to sit on the sidelines. The winners will be those who prepare now, mapping opportunities, cleaning their data, running controlled pilots, and engaging with providers to understand how Agentic AI will be built into their existing systems. Equally, they must invest in people: upskilling teams, nurturing AI champions inside the business, and preparing governance frameworks that balance innovation with control.

Every technological shift creates winners and losers. History shows that waiting until the dust settles is not a strategy, it is a slow path to irrelevance. The companies that act today, leaning into the discomfort and building readiness, will be positioned to move fastest when Agentic AI reaches maturity.

At Tyde Consulting, we’ve helped organisations through major technology shifts, from digital and cloud to today’s AI revolution. Please get in touch to discuss how we can help, www.tydeconsult.com