Tackling vendor and AI tool overload
The AI tools market is flooded. In 2025, $104 billion was raised by AI startups and the number of active AI startups operating globally varies depending on who’s doing the reporting, which is anything from 60-70,000. While this is dynamic and disruptive and innovative, the result is also a crowded and complex technology environment. CIOs and decision-makers are under pressure to deliver value with AI but there’s limited guidance and plenty of conflicting information.
There is a mismatch between the tools companies purchase and the outcomes they experience.
This article provides a practical lens for evaluating AI tools in a crowded space. The goal is to provide insights that take the conversation past feature comparisons and instead into how AI can be more strategically aligned with the business using examples from Mint’s industry deployments across manufacturing, healthcare and financial services.
What this article answers:
- How to evaluate AI tools based on business relevance.
- What a practical due diligence framework looks like for AI vendor selection.
- Why platform consistency, data readiness and time to value matter.
- How Mint uses four-week value delivery to drive AI adoption.
- Where South African enterprises are seeing measurable outcomes from AI.
Why is a business-first evaluation what matters in AI procurement?
Enterprise AI strategy in 2025 requires a move away from tool-first towards outcome-first thinking. Organizations that begin with a list of AI tools often end up with shelfware or disjointed point solutions. Instead, leaders need to begin with a clearly defined outcome. This might be margin recovery, safety tracking, reduced churn or faster access to insights.
Mint’s approach centers around what it calls a four-week value window. If the organization cannot demonstrate measurable impact within the first four weeks of deployment, adoption drops off sharply. For example, in manufacturing, Mint’s Vision AI platform has helped reduce daily theft from 20,000 to 2,000 loaves and improved productivity by 12% in bakery environments. This was not a generic tool, it was custom-built for a specific vertical, using video feeds to drive business outcomes.
When assessing AI vendors, you need to ask what problem does this tool solve, how quickly will we see results, and how closely is it tied to a measurable KPI?.
Why does architecture readiness matter?
AI integration is not plug and play. It depends on data quality, system interoperability and governance maturity. The Microsoft Digital Defense Report 2025 warns that poor integration and weak compliance controls are among the leading reasons AI deployments stall in enterprise environments.
In the public health sector, Mint digitized more than 800,000 patient records at Baragwanath Hospital. This work focused first on cleaning, sorting and structuring data, ensuring compliance with healthcare data regulations, and embedding this foundation before layering AI models that could generate summarized patient histories.
You need a clear picture of your company readiness before deploying AI. That includes ensuring the right data structures, establishing governance and security policies, and identifying the systems where AI tools will need to integrate directly.
Why is vendor alignment with your cloud platform important?
The best AI tools are not standalone. They are embedded within your existing platforms. This is where Mint’s Microsoft-only strategy has proven effective because, by aligning everything to Azure, Microsoft 365 and Dynamics 365, Mint avoids disjointed deployments and helps you manage data, governance and security across a unified environment.
When evaluating vendors, you need to assess whether the tool integrates with existing cloud platforms and workflows. Does it have built-in connectors to your current ecosystem? Will it require expensive middleware or custom APIs? How does it align with your licensing model, data policies and security baselines?
Platform-native tools not only reduce risk, but they also improve time to value and simplify long-term optimization.
What’s a practical vendor due diligence checklist?
Choosing the right AI partner starts with the right questions. These should include:
- Does the tool support our strategic goals?
- Can it deliver measurable value in four weeks?
- Is our architecture ready for AI deployment?
- Does the tool align with our existing cloud platform?
- Is the vendor equipped to support us beyond implementation?
In Mint’s AI deployments, these questions define the development of AI tools and solutions because they are not technology questions, they are business alignment decisions.
