By Aashay Tattu, Senior AI Automation Engineer, IT Max Global

Over the last quarter, we’ve heard a version of the same question in nearly every client check-in: “Which AI use cases have actually made it into day-to-day operations?”

We’ve built strong pilots, including copilots in CRM and automations in the contact centre, but the hard part is making them survive change control, monitoring, access rules, and Monday morning volume.

The ‘last mile’ problem: why POCs don’t become products

The pattern is familiar: we pilot something promising, a few teams try it, and then everyone quietly slides back to the old workflow because the pilot never becomes the default.

Example 1:

We recently rolled out a pilot of an AI knowledge bot in Teams for a global client’s support organisation. During the demo, it answered policy questions and ‘how-to’ queries in seconds, pulling from SharePoint and internal wikis. In the first few months of limited production use, some teams adopted it enthusiastically and saw fewer repetitive tickets, but we quickly hit the realities of scale: no clear ownership for keeping content current, inconsistent access permissions across sites, and a compliance team that wanted tighter control over which sources the bot could search. The bot is now a trusted helper for a subset of curated content, yet the dream of a single, always-up-to-date ‘brain’ for the whole organisation remains just out of reach.

Example 2: 

For a consumer brand, we built a web-based customer avatar that could greet visitors, answer FAQs, and guide them through product selection. Marketing loved the early prototypes because the avatar matched the brand perfectly and was demonstrated beautifully at the launch event. It now runs live on selected campaign pages and handles simple pre-purchase questions. However, moving it beyond a campaign means connecting to live stock and product data, keeping product answers in sync with the latest fact sheets, and baking consent into the journey (not bolting it on after). For now, the avatar is a real, working touchpoint, but still more of a branded experience than the always-on front line for customer service that the original deck imagined.

This is the ‘last mile’ problem of AI: the hard part isn’t intelligence – it’s operations. Identity and permissions, integration, content ownership, and the discipline to run the thing under a service-level agreement (SLA) are what decide whether a pilot becomes normal work. Real impact only happens when we deliberately weave AI into how we already deliver infrastructure, platforms and business apps.

That means:

• Embed AI where work happens, such as in ticketing, CRM, or Teams, and not in experimental side portals. This includes inside the tools that engineers, agents and salespeople use every day.
• Govern the sources of truth. Decide which data counts as the source of truth, who maintains it, and how we manage permissions across wikis, CRM and telemetry.
• Operate it like a core platform. It should be subject to the same expectations, such as security review, monitoring, resilience, and SLA, as core platforms.
• Close the loop by defining what engineers, service desk agents or salespeople do with AI outputs, how they override them, and how to capture feedback into our processes.

This less glamorous work is where the real value lies: turning a great demo into a dependable part of a project. It becomes a cross-functional effort, not an isolated AI project. That’s the shift we need to make; from “let’s try something cool with AI” to “let’s design and run a better end-to-end service, with AI as one of the components.”

From demos to dependable services

A simple sanity check for any AI idea is: would it survive a Monday morning? This means a full queue, escalations flying, permissions not lining up, and the business demanding an answer now. That’s the gap the stories above keep pointing to. AI usually doesn’t fall over because the model is ‘bad’. It falls over because it never becomes normal work, or in other words, something we can run at 2am, support under an SLA, and stand behind in an audit.

If we want AI work to become dependable (and billable), we should treat it like any other production service from day one: name an owner, lock the sources, define the fallback, and agree how we’ll measure success.

• Start with a real service problem, not a cool feature. Tie it to an SLA, a workflow step, or a customer journey moment.
• Design the last mile early. Where will it live? Is it in ticketing, CRM, Teams, or a portal? What data is it allowed to touch? What’s the fallback when it’s wrong?
• Make ownership explicit. Who owns the content, the integrations, and the change control after the pilot glow wears off?
• Build it with the people who’ll run it. Managed services, infra/PaaS, CRM/Power Platform, and security in the same conversation early – because production is where all the hidden requirements show up.

When we do these consistently, AI ideas stop living as side demos and start showing up as quiet improvements inside the services people already rely on – reliable, supportable, and actually used.