Business Value and ROI

Right, let's talk money. Because that's what this really comes down to. You can have the cleverest technology in the world, but if you can't show finance why it's worth spending on, you're going nowhere.

The good news is that Now Assist, when appropriately implemented, delivers genuine, measurable value. The challenge is articulating it in a way that secures budget approval and keeps stakeholders happy throughout implementation.

The Problem with AI Business Cases

Here's what usually happens. Someone gets excited about AI, puts together a business case full of buzzwords and optimistic projections, gets it approved, implements it poorly, and then wonders why adoption is rubbish and nobody can point to concrete benefits.

Sound familiar?

The problem isn't the technology. It's how we approach the business case. Too many organisations treat AI like magic. They expect instant transformation without doing the hard work of understanding where value actually comes from.

Now Assist isn't magic. It's a tool that augments what your people already do. The value comes from doing those things faster, better, or with fewer resources. That's it. Not revolutionary, just a genuine operational improvement that adds up when scaled.

Where the Real Value Lives

Let me show you what works. Forget the generic claims about "AI transformation" and "digital innovation". Look at specific, measurable improvements in actual work.

Incident Management

Take incident summarisation. Your agents spend time reading through mountains of work notes, updates, and comments to understand what's going on. That's time. Time costs money. Time also delays resolution, affecting your SLAs and frustrating users.

Put a number on it. If an agent spends an average of 2 minutes reading the case history per incident and handles 30 incidents a day, that's 1 hour a day. Multiply that across your team. Suddenly, you're looking at significant capacity that could be redirected to solving problems rather than just understanding them.

Now Assist gives you incident summaries instantly. Those two minutes become five seconds. That hour a day becomes available for productive work. That's not theoretical. That's a measurable efficiency gain.

And it compounds. Faster understanding means faster resolution. Faster resolution means better SLA compliance. Better SLA compliance means happier users and fewer escalations. Fewer escalations means your senior engineers can focus on complex issues instead of babysitting tickets that should have been closed already.

Real organisations are seeing 40-50% reductions in average incident handling time. That translates directly to either handling more volume with the same team or reducing team size whilst maintaining service levels.

Knowledge Management

Your knowledge base is probably a mess. Most are. Articles get created once and never updated. People can't find what they need. Agents end up solving the same problems repeatedly because documenting the solution properly is too much faff.

Knowledge article generation changes this. An agent closes an incident, clicks a button, and gets a draft article that actually makes sense. They review it, publish it, and are done. What used to take 30 to 45 minutes now takes five.

More importantly, it actually happens. When creating articles is easy, people do it. When your knowledge base is current and comprehensive, self-service actually works. When self-service works, you deflect tickets before they're even created.

Run the numbers on ticket deflection. If 20 per cent of your incidents could be resolved through self-service, and each incident costs you £15 in handling costs, the maths gets interesting quickly when you're dealing with thousands of incidents per month.

HR and Employee Experience

Employee onboarding queries. Leave requests. Policy questions. Every HR team drowns in repetitive questions that could be answered by good self-service if only people could find the right information.

Now Assist in Virtual Agent handles these conversations naturally, not with rigid flows and keyword matching, but with an actual understanding of what people are asking. Your HR team stops being a help desk and starts being strategic.

One organisation I worked with saw its HR service desk volume drop by 35 per cent within three months. Same team, handling a third fewer tickets, because users were getting answers themselves. The HR team redirected that capacity toward proactive work that actually improved the employee experience, rather than just reacting to queries.

Building Your Business Case

Here's how to approach this properly. Start with your pain points. Where are your teams struggling? What's taking too long? What's costing too much? What's frustrating users?

Then map those pain points to Now Assist capabilities. Don't try to implement everything at once. Pick three to five use cases where the value is obvious and the implementation is straightforward.

For each use case, define your baseline. What does the current state look like? How long does it take? How much does it cost? What are the quality issues?

Then project your future state. Be conservative. Don't promise 80 per cent improvements. Aim for 30 to 40 per cent and you'll look brilliant when you exceed it. Better to overdeliver than underdeliver on promises.

Put actual numbers to it. If you're looking at incident summarisation:

Current state: Average 2 minutes per incident to read case history. 500 incidents per day. 1,000 minutes (16.7 hours) daily across the team. At £40 per hour loaded cost, that's £668 per day in time spent just understanding incidents.

Future state: Average 5 seconds per incident with AI summarisation. 42 minutes daily. Saving 958 minutes (16 hours) per day. £640 per day in recovered capacity. Over £160,000 per year in value.

That's before you account for improved resolution times, better SLA compliance, and enhanced user satisfaction.

What to Measure

You need metrics that actually matter. Here's what works:

Efficiency metrics tell you if people are getting work done faster. Average handling time. Mean time to resolution. Time to first response. These should improve measurably.

Quality metrics show whether the outputs are good enough. Resolution accuracy. Knowledge article usage rates. User satisfaction scores. First contact resolution rates. These prove your AI isn't just fast but actually helpful.

Adoption metrics reveal whether people are using it. Skill invocation rates. Feature utilisation. These tell you if you've built something people value or something they're ignoring.

Business impact metrics connect to what leadership cares about. Cost per ticket. SLA compliance rates. Ticket deflection percentages. Employee satisfaction. These demonstrate tangible business value.

Track these from day one. Establish your baseline before you implement anything. Then monitor continuously and report regularly. Nothing kills momentum like not being able to show progress.

The ROI Timeline

Be realistic about timing. You won't see the full value immediately. AI implementations need time to mature.

Month 1 to 3: Pilot phase. Limited deployment. Focus on proving the concept and refining your approach. Value is minimal, but learning is maximum.

Month 4 to 6: Expanded rollout. More users, more use cases. Value starts becoming visible. Early adopters are seeing benefits.

Month 7 to 12: Full deployment. Organisation-wide adoption. Value compounds as usage increases. This is where you hit your projected ROI.

Year 2 onwards: Optimisation and scaling. You're not just maintaining value, you're finding new ways to leverage the capabilities. ROI continues improving.

Most organisations see payback within 12 to 18 months. That's conservative. Do it well, and you'll see positive ROI much sooner.

Securing Buy-In

Different stakeholders care about various things. Tailor your message.

Finance wants ROI and cost savings. Show them the complex numbers. Capacity recovered. Costs avoided. Revenue is protected through better service levels.

IT leadership wants operational efficiency. Show them improved service levels, reduced escalations, and better resource utilisation.

Business units want better outcomes. Show them faster resolution, happier users, improved productivity.

Platform owners want successful implementations. Show them how Now Assist integrates with existing investments and enhances platform value.

Make sure your business owner and platform owner are aligned and jointly driving the initiative. This needs to be in both of their opportunity portfolios as a top priority. Otherwise, it becomes a nice-to-have project that never gets the attention it needs.

Common Pitfalls

Don't oversell. Promising the moon and delivering a molehill kills credibility. Better to be cautiously optimistic and exceed expectations.

Don't forget change management. Technology is easy. Getting people to change how they work is hard. Budget for training, communication, and ongoing support.

Don't ignore data quality. AI is only as good as the data it works with. If your incident notes are rubbish, your summaries will be rubbish. Factor in time and cost for data cleansing.

Don't assume full adoption immediately. Some people will embrace it. Some will resist. Plan for gradual adoption and support those who struggle.

The Bottom Line

Now Assist delivers value when you implement it thoughtfully, measure it properly, and manage expectations realistically. The technology works. The business case is solid. The ROI is achievable.

But it requires discipline. Clear use cases. Conservative projections. Proper measurement. Continuous optimisation.

Do this right, and you'll have stakeholders queuing up to fund phase two. Do it badly, and you'll struggle to maintain support for phase one.

The difference between success and failure isn't the technology. It's how you approach the business case and deliver on your promises.

Right, now you understand the value proposition. Let's look at how all the pieces of Now Assist actually fit together.