The AI industry is experiencing a gold rush.
Every week, hundreds of new AI startups emerge, each promising to revolutionise business operations, eliminate manual work, and transform productivity. For organisations trying to adopt AI, the challenge is no longer finding AI products. The challenge is identifying which products will create long-term value and which ones will simply consume time, money, and attention.
Not all AI products are created equal.
Some represent genuine technological innovation. Others are little more than clever packaging around existing AI models. Understanding the difference can save organisations from costly mistakes and help them focus on technologies that deliver sustainable competitive advantage.
If you want to start the journey toward becoming an AI-enabled organisation, get in touch. I am Victor, pictured above. Let us have a quick chat.
1. LLM Wrappers
The first category to be aware of is the LLM wrapper.
An LLM wrapper is a product that sits on top of an existing large language model such as ChatGPT, Claude, or Gemini and presents itself as a specialised AI solution.
For example, imagine a chatbot that claims to be an expert in a particular field such as marketing, real estate, legal drafting, or business coaching. In many cases, the product is simply a standard LLM with a carefully designed prompt that restricts its behaviour to a specific domain.
Rather than adding new capabilities, these products often remove capabilities. They take a general-purpose AI and narrow its scope.
This is not necessarily a bad thing. Some organisations benefit from simplified interfaces and domain-specific experiences. However, businesses should understand that many of these products provide limited intellectual property beyond prompt engineering.
A useful question to ask is:
What does this product do that I could not achieve with a custom GPT, a system prompt, or a well-designed workflow?
If the answer is unclear, you may be looking at a wrapper rather than a genuine AI platform.
2. Intelligence-Injected SaaS
The second category is intelligence-injected SaaS.
These are traditional Software-as-a-Service products that have integrated AI features into existing workflows.
Examples include CRMs that generate emails, accounting systems that summarise transactions, or project management tools that create task descriptions.
These products are often valuable because they improve existing processes. However, the AI component is usually an enhancement rather than the foundation of the product.
The core software existed before AI, and the business value still comes primarily from the underlying workflow, data model, and operational processes.
When evaluating these products, organisations should assess the SaaS platform first and the AI features second.
Ask:
If the AI functionality disappeared tomorrow, would the software still be valuable?
If the answer is yes, you are likely looking at a SaaS product enhanced by AI rather than an AI-native platform.
3. MCP Bridges and Aggregators
A newer category is emerging around MCP (Model Context Protocol) bridges and aggregators.
These products connect AI models to multiple external systems through MCP servers, allowing a single AI assistant to interact with many different applications and data sources.
In many ways, these platforms are similar to LLM wrappers. The difference is that they focus on connectivity rather than expertise.
Instead of building their own intelligence, they provide access to tools, databases, CRMs, ticketing systems, document repositories, and hundreds of other integrations.
Some offer additional value through:
- Access control
- Security policies
- Auditing and compliance
- Industry-specific workflows
- Prompt libraries
- Governance and guardrails
These platforms can be extremely useful, but businesses should recognise that the underlying intelligence often still comes from the connected LLMs.
The key question becomes:
What unique value does the platform provide beyond simply connecting existing MCP servers to existing AI models?
4. Narrow AI Products and Feature Companies
Another category consists of AI products designed to solve a single, very specific problem.
Examples might include:
- Meeting summary generators
- Email subject line generators
- Social media post creators
- Job description writers
- SEO title generators
- Ticket classification tools
These products can provide immediate value when the problem is repetitive, high-volume, and expensive to perform manually.
However, organisations should be cautious about building long-term AI strategies around highly specialised tools.
Many of these products are not really companies; they are features packaged as products. The pace of AI development is extraordinary, and capabilities that justify an entire startup today may become standard functionality inside mainstream platforms tomorrow.
Before adopting a narrow AI product, ask:
Is this solving a business problem that requires a dedicated platform, or is it simply a feature that Microsoft, Google, OpenAI, Salesforce, or another major platform could add within the next year?
If the answer is unclear, the long-term value of the product may be limited.
5. Agent Washing
Just as the technology industry experienced "cloud washing" in the past, we are now seeing a wave of "agent washing."
Everything is suddenly being marketed as an AI Agent.
Examples include:
- Chatbots being rebranded as agents
- Prompt libraries being called agents
- Workflow automations being called agents
- API integrations being called agents
A true AI agent usually possesses some combination of:
- Memory
- Planning
- Tool usage
- Multi-step execution
- Reasoning
- Autonomy
A chatbot that simply answers questions is not automatically an agent.
Businesses should look beyond marketing terminology and understand what the system actually does. If the "agent" cannot remember, plan, act, or execute tasks independently, it may simply be a chatbot with a new label.
The important question is:
What makes this an agent rather than a conversation interface connected to an LLM?
6. Open-Source Repackaging
A growing number of AI products are built by taking an existing open-source project, changing the branding, and charging a monthly subscription fee.
Common examples include platforms built on top of:
- Open WebUI
- Flowise
- Langflow
- Dify
- CrewAI
- AutoGen
- n8n
There is nothing wrong with using open-source software. In fact, many excellent products are built on open-source foundations.
The important question is whether the vendor has added meaningful value beyond installation and branding.
Examples of genuine value include:
- Enterprise security
- Governance and compliance
- Monitoring and observability
- Industry-specific workflows
- Scalability improvements
- Support and maintenance
- Proprietary orchestration capabilities
Without these additions, organisations may find themselves paying recurring fees for software they could deploy internally with minimal effort.
The question to ask is:
What value has been added beyond the underlying open-source project?
If the answer is "a nicer interface and a logo," the product may not justify its long-term cost.
7. The AI Consultancy Trap
Perhaps the most dangerous category is not a product at all.
It is the growing number of AI consultancies and solution providers that promise to solve virtually any problem with AI.
Their websites are filled with AI-generated graphics, AI-generated marketing copy, and presentations produced in a matter of hours. Their teams often describe themselves as AI experts despite having limited practical experience delivering production-grade AI systems.
The warning signs are usually obvious:
- They promise everything.
- They specialise in everything.
- They have no working demonstrations.
- They have no measurable outcomes.
- They cannot provide customer references.
- They do not offer a trial environment.
- They rely heavily on presentations rather than working software.
A genuine AI company should be able to show functioning systems, measurable business outcomes, and real customer success stories.
The most important question is simple:
Can they demonstrate a working solution today?
If the answer is no, proceed carefully.
What a True AI Product Looks Like
A true AI product is not defined by whether it uses AI.
Almost every software company now uses AI somewhere.
Instead, genuine AI products tend to possess several characteristics:
- AI is central to the product, not an add-on feature.
- The product creates capabilities that were previously impossible.
- The value extends beyond prompt engineering.
- The system improves through data, workflows, or learning.
- The product integrates deeply into business processes.
- The vendor can demonstrate measurable outcomes.
- Customers become more capable because of the platform, not merely more dependent on it.
The question is not whether a product contains AI.
The question is whether the AI creates a lasting competitive advantage that cannot be easily replicated by the next model release.
As AI becomes increasingly accessible, the winners will not be those who simply package intelligence. They will be those who build systems, workflows, and capabilities that turn intelligence into real business value.