From Buzzword to Tool: How AI Fits Into Real Software Projects

There is a version of the AI conversation that happens in boardrooms and at industry conferences, and there is a version that happens in development team stand-ups and code review threads. These are not the same conversation. The boardroom version tends to be about transformation, competitive advantage, and strategic positioning. The development team version tends to be about whether the thing actually works reliably enough to ship. 

Both conversations matter. But the second one is where the rubber meets the road, and it is the one that gets the least coverage. This piece is about what experienced developers actually think when they are asked to integrate AI into business systems: the ERP platforms, the automation pipelines, the operational tools that organisations depend on every day. 

The honest answer is more nuanced than either the enthusiasts or the sceptics would have you believe. 

Where AI Genuinely Earns Its Place in Development Work 

Ask a developer whether AI has changed how they work and the answer, in most teams, is yes. But the change is not the sweeping transformation the headlines suggest. It is more specific and more interesting than that. 

The area where AI tools have had the clearest impact is in reducing the friction of routine coding tasks. Writing boilerplate, generating unit tests, producing documentation, scaffolding new modules in an established pattern — these are tasks that experienced developers could always do, but that were time-consuming and not intellectually demanding. AI coding assistants handle much of this competently. The result is not that developers are doing less thinking; it is that more of their time and attention goes to the parts of the job that actually require thought. 

Debugging and code comprehension have also improved meaningfully. Being able to paste an unfamiliar codebase section and ask for an explanation, or describe an unexpected behaviour and get plausible hypotheses quickly, compresses the time spent on investigation. Senior developers particularly value this when working across multiple codebases or picking up legacy systems they did not write. 

In the context of business systems specifically, AI has proven useful in processing and transforming data between formats. ERP systems, in particular, tend to involve a lot of data with inconsistent structure, legacy schemas, and documentation that ranges from incomplete to actively misleading. AI tools can accelerate the work of understanding what data means, writing transformation logic, and producing the kind of intermediate scripts that are necessary but unremarkable. 

Natural language interfaces for querying business data are another area where developers have seen genuine value. Rather than writing SQL for every ad hoc analysis request that comes from a business stakeholder, a well-implemented AI layer can handle a meaningful proportion of these queries directly, in plain English. When this works, it removes a class of low-complexity, high-frequency requests from the development team’s queue and puts capability directly in the hands of the people who need it. 

Where It Still Falls Short 

Experienced developers will be equally direct about where AI tools do not deliver, and this is the part of the conversation that tends to get less airtime. 

Reliability in production is the central concern. An AI tool that produces correct output eighty-five percent of the time can be genuinely useful in a development assist context, where a human reviews every output before it is used. The same tool is a liability in a production system where outputs are acted on automatically. The failure modes of AI systems are different from those of conventional software: they fail silently, produce plausible-sounding incorrect results, and behave inconsistently across very similar inputs. These characteristics require a fundamentally different approach to validation and monitoring than most teams are used to. 

Complex reasoning over extended context is another persistent weakness. Business systems often require logic that chains together multiple conditions, applies rules in sequence, and handles exceptions in ways that have been built up over years of operational experience. AI systems struggle with this kind of deep, domain-specific reasoning. They can handle individual steps competently and then combine them incorrectly. They can apply a rule correctly ninety-nine times and misapply it on the hundredth in a way that is not obviously detectable. 

Integration with existing systems is harder than it looks. AI vendors tend to demonstrate capabilities in clean, isolated contexts. The actual work of integrating an AI component into a live ERP system, with all its historical decisions, legacy constraints, and operational dependencies, is substantially more complex. The integration work often takes several times longer than the AI component itself, and this is consistently underestimated in project planning. 

Developers also raise concerns about consistency and testability. Conventional software, given the same inputs, produces the same outputs. You can write deterministic tests, reason about behaviour formally, and trace failures precisely. AI components introduce non-determinism: the same prompt can produce meaningfully different outputs. Writing robust test suites for AI-dependent functionality is an unsolved problem in most engineering organisations, and the absence of good testing practices creates technical debt that accumulates quickly. 

How Development Teams Actually Evaluate AI Tools 

When a development team is assessing whether to adopt an AI tool, the criteria they use tend to be quite different from those in vendor marketing materials. 

The first question is almost always about reliability and consistency. Not “can it do this” but “does it do this reliably enough that we can depend on it.” A tool that impresses in a demo is not the same as a tool that performs consistently across the range of real inputs a production system will encounter. Good teams build evaluation datasets from actual production examples and test tools against those before committing. 

Latency is evaluated carefully for any user-facing application. AI inference takes time, and the response times that are acceptable in a chat interface are not always acceptable in a business application where a user is waiting for a screen to load or a process to complete. Developers will often prototype the performance characteristics of an AI component early, specifically to discover whether the latency is workable before significant integration work is done. 

The failure mode question gets serious attention. How does the system behave when the AI component produces a wrong answer? Can the system detect and handle this gracefully? Is there a fallback path? In business systems where AI outputs feed into consequential decisions — inventory levels, financial calculations, customer communications — the answer to these questions needs to be robust before anything goes to production. 

Cost at scale is modelled explicitly. A team that evaluates an AI API at low usage and finds it affordable needs to model what the cost looks like at full production volume, with realistic usage patterns including peak loads. The economics that work during testing do not always hold at scale. 

Finally, development teams pay attention to vendor stability and the long-term picture. Model versions get deprecated. APIs change. Providers adjust pricing. A dependency on a specific model version or a specific vendor’s API is a form of technical risk that needs to be managed. Teams that have been burned by platform changes in other contexts ask these questions early. 

The Crucial Difference Between Experimentation and Production 

One of the most important distinctions in AI development work is the difference between something that works in an experiment and something that is ready for production. This distinction is not always well understood by the business stakeholders commissioning the work, and misalignment around it causes significant friction. 

Experimentation is how you discover what AI can and cannot do for a specific problem. A developer can build a prototype in a few days that demonstrates a compelling capability. That prototype might handle eighty percent of cases correctly, produce outputs that look good in a demo, and show genuine promise. None of that means it is ready to run in a production environment where it affects real customers or real data. 

Getting from a working prototype to a production-ready system typically requires work in several areas that are invisible in the experiment: error handling, logging and monitoring, graceful degradation when the AI component underperforms, security review of what data is sent to the AI provider, performance testing under load, and the integration work required to connect the AI component to surrounding systems in a reliable way. 

This gap is consistently larger than non-technical stakeholders expect. It is not that developers are being slow or overcautious. It is that the production requirements are genuinely more demanding than the experiment requirements, and AI components introduce additional considerations that conventional software does not. Organisations that understand this gap plan their AI projects realistically. Organisations that do not tend to find themselves with a demo they cannot ship and a team fielding questions about why it is taking so long. 

How Businesses Should Work with Technical Teams on AI Initiatives 

The businesses that get the most value from AI development work tend to share a few consistent characteristics in how they structure the relationship between technical and non-technical stakeholders. 

They involve developers early in the problem definition, not just in the solution building. When a business stakeholder brings a problem to a development team and says “we want AI to solve this”, the most valuable thing the team can do is ask careful questions about what the problem actually is, what the failure modes would look like, and whether AI is genuinely the right approach. This requires the business side to welcome that scrutiny rather than treating it as resistance. 

They are clear about what success looks like in measurable terms before development starts. “Use AI to improve customer support” is not a success criterion. “Reduce the average handling time for routine query categories by thirty percent while maintaining a customer satisfaction score above current baseline” is. The specificity matters because it determines what you build, how you test it, and whether you know when it is working. 

They allow realistic time for the production-readiness work that follows a successful prototype. A rule of thumb that experienced teams use is that the prototype is ten to twenty percent of the total work. The rest is hardening, integration, testing, and monitoring. Timelines that do not account for this create projects that are perpetually “nearly there” and teams that are perpetually under pressure. 

They create feedback loops between operational users and the development team. AI systems that are deployed in business operations perform differently over time as the data they encounter drifts from their training distribution, as edge cases accumulate, and as the business context around them changes. The development team needs visibility into how the system is actually performing in use, not just in test conditions. Organisations that build good feedback mechanisms catch problems early. Those that do not tend to discover them when they have already caused damage. 

The Perspective That Gets Overlooked 

The developer perspective on AI in business systems is neither the uncritical enthusiasm of the vendor pitch nor the blanket scepticism of those who dismiss the technology entirely. It is something more calibrated: a recognition that the tools are genuinely useful in specific contexts, a clear-eyed assessment of where they are not yet reliable enough for serious work, and a set of professional standards for what it means to ship something that people can actually depend on. 

That perspective is an asset to any organisation trying to navigate AI adoption sensibly. The businesses that treat their technical teams as partners in that navigation rather than as implementation resources for decisions already made tend to end up with AI systems that actually work. 

That might sound like a straightforward point, but in practice it requires a different kind of conversation between technical and non-technical parts of the business than many organisations have historically had around technology. Getting that conversation right is, in many cases, the most important factor in whether an AI initiative succeeds.