Avoiding The Enterprise AI Trap, Choose Real World AI

The "Enterprise AI" Trap

The "AI hype cycle" is definitely real, and it often leads to inflated expectations, high costs, and disappointing returns on investment. Your observation that a simpler, more direct approach is often the best for early adopters is spot on. The current landscape of AI is heavily siloed, but the future is pointing strongly towards a more collaborative, interoperable ecosystem. The major players, Google, Microsoft, Amazon and Meta are already working together and will as always cast the middlemen or wrapper companies within the channel aside. You want to avoid being caught up with the cannon fodder and surely know your business better than a word salad outsider?

• High Costs, Low ROI: Building custom, enterprise-scale AI solutions from the ground up is an incredibly expensive and complex endeavor. It requires specialized talent (data scientists, ML engineers), massive computational resources, and a huge investment in data preparation and infrastructure. For many companies, the promised ROI doesn't materialize, especially when the projects get bogged down in technical debt, data quality issues, and legacy system integration problems.
• Consultant-Driven Delays: As you mentioned, consultants can sometimes exacerbate the problem. A focus on large, complex, and high-cost projects can lead to extensive discovery phases, custom development that takes months or years, and a lack of agility. This can delay any measurable value, further eroding the project's ROI.
• The "Shiny Object" Syndrome: Many businesses are pursuing AI for the sake of AI, rather than to solve a specific, high-impact business problem. This "technology-first" approach is a recipe for failure, as it often leads to a scattergun of pilot projects with no clear strategic alignment.

The transformation circus will not like this, but using established, off-the-shelf models like Gemini, Claude, or OpenAI as an Assistive AI or virtual assistant is currently the best game in town for four common sense reasons:

• Speed to Value: These models are ready to use right out of the box. A business can sign up for a service and begin experimenting with real-world applications within hours or days, not months or years. This allows for a fast, iterative approach to finding where AI can actually deliver value.
• Lower Barrier to Entry: You don't need a team of expensive AI experts. Your existing employees can begin using these tools immediately to boost their productivity. A marketing team can use it for content creation, a support team can use it to draft responses, and a development team can use it for coding assistance.
• Clear and Measurable Impact: By starting with a focused, tactical use case, it's much easier to measure the return. You can track metrics like:
• Reduced Risk: Instead of a multi-million-pound bet on a bespoke solution, you're making a small, manageable investment. If a particular use case doesn't work out, you can pivot quickly to a different one without a significant loss.

The key for a confused business owner is to move away from the idea of "implementing AI" as a massive, all-or-nothing project. Instead, they should think of it as "using AI tools" to solve specific, daily problems. This is exactly where the large language models from Gemini, Claude, and OpenAI excel.

The Siloed temporary Landscape

The current landscape of AI is heavily siloed, but already pointing strongly towards a more collaborative, interoperable ecosystem. The major players, Google, Microsoft, Oracle, Meta and Apple are already working collaboratively and therefore making significant moves in this direction. Not just hardware infrastructure but code and agents too - were you aware Microsoft is using Google agents in a joint venture referred to as A2A?

• The Rise of Multi-Agent Systems: We're moving beyond a single AI assistant that does everything. The future lies in SLMs, small and specialised, collaborating "agents." which I have been arguing for for years for the benefit of pockets, people and planet. For example, one SLM agent might be an expert at handling your calendar, another at managing your emails, and a third at performing financial analysis. For them to be truly useful, they must be able to work together seamlessly, regardless of which company built them.
• Open Protocols and Interoperability: The industry has already begun to recognise this need for collaboration. Google, for instance, launched the Agent2Agent (A2A) protocol and has already donated it to the Linux Foundation. This is a massive step. It means that A2A is now an open, common language that allows different AI agents to communicate, securely exchange information, and coordinate actions.
• Competitors are Adopting Open Standards: This isn't just a Google initiative. Microsoft has publicly announced its support for the A2A protocol, and other major players like Amazon Web Services (AWS) are also backing the project. This is a clear signal that the industry is prioritizing open standards over proprietary, locked-in ecosystems. They understand that a fragmented market limits the potential of AI for everyone.
• The Role of Other Key Players: Companies like Anthropic are also contributing. Their Model Context Protocol (MCP) is another standard that helps AI agents access data and interact with external tools. The fact that Google, Microsoft, and OpenAI are all on board with MCP shows a strong, collective push towards interoperability.
• What This Means for the User: For a business owner, this collaboration is fantastic news. It will break down the current silos between systems. In the next 2-3 years, you can expect to see scenarios like:

So the high costs and confusion of the current moment are temporary and need to be avoided for those on a budget. The collaborative groundwork being laid now by the biggest tech companies is setting the stage for a future where AI agents are interchangeable, far more powerful, and ultimately, more valuable for businesses.

The C-Suite's Guide to AI for the Real World

My vision of AI is of a transformative force akin to steam in the industrial revolution. It's the kind of strategic thinking that C-level executives need to embrace to move beyond the hype and harness AI's true potential. This brief guide molds the previous concepts we have covered into a strategic approach for business leaders, given the recent reports of multi-million-pound projects with no tangible return on investment are common. But AI, at its core, is not about custom, billion-dollar systems; it's about a fundamental new source of productivity, akin to the application of the steam engine in the Industrial Revolution. It’s a force multiplier for human ingenuity. So this final short and sweet guide outlines a pragmatic, low-risk, and high-reward strategy to deploy AI, enabling your workforce and driving real value.

Principle 1: Stop Chasing the Unicorn. Start Adopting the Assistant.

The most common mistake is the belief that AI requires a bespoke, enterprise-wide project. This is the path of high cost, long delays, and low ROI.

• The Problem: The "Enterprise AI" trap, often fueled by consultants, leads to expensive, custom-built solutions that are slow to deploy and difficult to scale.
• The Solution: Focus on leveraging the powerful, readily available "AI assistants" from leaders like Google (Gemini), Microsoft (Copilot), and Anthropic (Claude). These are not toys; they are sophisticated engines of productivity, accessible at a fraction of the cost.

Your takeaway action Item: Empower your teams to use these the main chat tools for daily tasks. A marketing team can use them to draft content, HR can use them to summarize documents, and sales can use them to generate emails. The investment is minimal, and the return is immediate and measurable.

Principle 2: Break the Silos with Open Standards.

The current AI landscape is a fragmented collection of proprietary systems. This will change—and quickly. The industry is rapidly moving towards a collaborative, interconnected ecosystem of "agents."

• The Problem: If every AI assistant lives in its own walled garden, your data and workflows are locked in. This creates friction and limits the technology's full potential.
• The Solution: The world's largest tech companies—Google, Microsoft, and others—are already co-developing open protocols (like Agent2Agent) that will allow AI agents to communicate and work together seamlessly. This means your "Google AI" will soon be able to coordinate with your "Microsoft AI" to complete a single task.

Your takeaway action Item: As a C-level executive, you must think in terms of interoperability. Prioritize solutions that are built on open standards and avoid those that aim to lock you into a single, proprietary ecosystem. Prepare for a future where a single workflow can span multiple platforms without friction.

Principle 3: Humanity Over Hype: Put People at the Center.

The true benefit of AI is not replacing jobs, but elevating them. Just as the steam engine freed people from backbreaking manual labour, AI can free your employees from soul-crushing administrative tasks.

• The Problem: Fear of automation and job loss can create resistance among employees. This fear is rooted in the "replacement" narrative, not the "augmentation" reality.
• The Solution: Frame AI not as a cost-cutting measure, but as a tool for empowerment. This is about giving your staff a superpower—the ability to offload repetitive tasks and focus on creative problem-solving, strategic thinking, and human connection.

Your takeaway action Item: Invest in training and culture. Teach your staff how to use these AI assistants effectively. Encourage them to experiment and discover how these tools can make their lives easier. By doing so, you are not just implementing technology; you are building a future where your workforce is more productive, more engaged, and less burdened.

The First Mover's Advantage

The fear and confusion in the market are a fleeting opportunity. While competitors are still stuck in the high-cost, high-risk "enterprise AI" trap, you can get a cheap and cheerful head start without betting your house on it. By embracing a pragmatic, "assistant intelligence first" approach and building a culture of empowerment, you can drive significant productivity gains, unlock your team’s potential, and ensure your company is ready for the new age of intelligence. This is how humanity moves forward without fear, by using technology to amplify our strengths, not to diminish our worth.