Insights & Checklists

Business Intelligence (BI) tools have transformed how organisations approach reporting and analytics. However, as with any new development, challenges can arise during implementation, and businesses may struggle to achieve the expected Return on Investment (RoI). In my experience, change management, early involvement of key users, and solid support from technology providers — including internal ICT — are key to success.

Many BI discussions and posts focus on dashboards, which are indeed important for communicating insights and performance, particularly at a strategic level. While that's valuable, I've found even greater impact from using BI tools for operational purposes — driving efficiency, accuracy, and cost savings.

Here are a few practical operational use cases where BI can make a real difference:

👉 I'd love to hear how others are applying BI tools in operational contexts. What are your favourite use cases?

Have questions or comments about using BI tools beyond dashboards? We'd love to hear from you.

Having successfully migrated the preparation of management accounts from Microsoft excel based templates and financial statements software to Microsoft Power Bi, the benefits that I have realised include:

• Saves time and effort in preparation – routine & complicated steps can easily be coded and then reused on future reports by simply refreshing the reports, reducing preparation time from days to a few minutes.
• Reports are generated in a consistent manner and therefore more accurate, and less prone to errors and omissions.
• Single source of truth as data is extracted from the same source and using the same parameters.
• We have configured different reports for different audiences by using different "views" of the same database. Switching between views and reporting dimensions requires minimum additional effort.
• Reports are interactive and users are able to customise the reports through self-service options.
• Users can drill down to underlying details and transactions from one portal as reports are generated from detailed ledgers and not from the TB. In our previous reporting process, we had to jump from one system to another, to "piece" together explanations for variances.
• Improved sharing and collaboration. Reports are published on the web and can be viewed and downloaded any time.
• We have started to "push" updated reports automatically to end users at scheduled intervals.
• Access to AI and more visualisation tools that offer more insights into our business and our data.

For a minimum investment, it is now possible to transform manual reporting processes.

Roadmap and Checklist for Finance Process Automation

Finance process automation can transform your operations, but success requires a structured approach. Too often, organizations jump straight to selecting technology without understanding their current state or clearly defining the problem they're trying to solve. This roadmap provides a practical checklist to guide your automation journey from assessment to successful implementation.

After years of implementing reporting, finance, and enterprise solutions, I'd like to share some of the lessons learned that have consistently been critical to successful finance and business transformation.

Most BI initiatives — as well as new system implementations and major upgrades — don't fail because of technology inadequacies. Large-scale projects fail when the design and implementation approach overlooks user needs and fails to get the basics right.

Organizations invest heavily in BI platforms, new systems, and system upgrades, yet adoption often remains low. Solutions look impressive, architectures are complex, and capabilities are advanced — but users quietly revert to spreadsheets, workarounds, or legacy tools.

The problem isn't the technology. It's the approach.

Successful rollouts are about having the right technology that solves both user and organizational needs, and tools that genuinely make life easier because they work reliably and users understand how to use them.

Here is what has worked for me.

✅ Be Clear About the Problem You Are Solving

Before building or deploying a new system, you need absolute clarity on a few fundamentals:

• What problem are we actually trying to solve?
• Is there agreement across the organisation on what that problem is?
• What does success look like, and how will it be measured?

An enterprise-wide view is critical. In some ICT-led projects — which ideally should not be the case — what ICT identifies as a problem may not be a problem for Finance, HR, or the broader business. Likewise, an issue experienced by a support function may not exist for end users at all.

When there is no shared understanding of the problem, systems are designed to address isolated pain points rather than real business needs. The result is predictable: solutions that are technically sound but poorly adopted.

If users don't understand why a new system or upgrade exists — and how it helps them personally — adoption will always be an uphill battle.

✅ Make It Easier, Not Harder: Involve Users From Day One

If a new system or upgraded solution is harder to use or access than what it replaces, users will default to what they know — and that's entirely rational.

Adoption improves dramatically when:

• End users help shape requirements
• Designs and workflows are validated early
• Prototypes or pilots are tested before full rollout

Building in isolation almost guarantees solving the wrong problems or introducing complexity that adds no real value.

✅ Get the Basics Right: Data Accuracy, UAT, and Sign-Off

Nothing destroys confidence faster than inaccurate data or broken processes.

A strong User Acceptance Testing (UAT) process ensures:

• Data and outputs are accurate and complete
• Business logic aligns with how teams actually operate
• Users understand how to interpret and use the system correctly

Formal business sign-off creates accountability and trust — while also serving as practical, hands-on training.

✅ Adequate, Timely Support and Training

First impressions matter.

If users struggle with access, performance, or understanding a new system in the early days, they will immediately revert to familiar tools and processes.

Successful implementations include:

• Clear, well-communicated support and escalation channels
• Timely resolution of user queries
• Positive feedback and visible success stories from early users, with pilot participants becoming ambassadors for the new tool or upgrade

When users know help is available — and that issues will be resolved promptly — confidence grows quickly. Without this, even well-designed solutions lose credibility.

📈 Monitor Tool Adoption and Usage Feedback

Usage metrics and user feedback reveal the reality of adoption:

• What features are actually being used?
• Where are users struggling or disengaging?
• Which capabilities deliver real value — and which do not?

Monitoring adoption allows teams to remove friction, refine functionality, and provide targeted training where it's needed most. Solutions improve when feedback is acted on, and users can see that their input drives real change.

✅ Adopt a Phased, Pilot-First Approach

Adoption should be earned, not enforced.

Users are drawn to systems that clearly make their work easier and decisions better. Rushing large-scale deployments creates resistance, damages trust, and often introduces more problems than it solves.

A pilot-first approach:

• Proves value early
• Surfaces real-world issues
• Incorporates feedback before scale
• Builds confidence organically

Technology should work with the business and its people — not against them.

🔍 Switch Off the Old Tool at the Right Time

Once the new system or upgrade is stable, trusted, and users are trained, make a clear decision: retire the old solution.

Running parallel systems creates confusion, inconsistent outputs, and slows adoption.

Have questions or comments about ensuring successful BI adoption? We'd love to hear from you.

Implementation of a BI project, like any other new technology platform or tool, has its risks and challenges which in my view include:

To ensure success, organisations should consider the following:

What has been your experience?

AI is here. Everyone is talking about it, and it is unlikely to go away.

As with any major technological shift, AI brings a mix of excitement, expectation, and hype. The challenge for finance leaders is to filter out the noise and focus on what will genuinely create value for their teams and organizations—both now and in the near future.

Ignoring AI altogether is not a low-risk option. It can mean missed opportunities to operate more efficiently, weaker decision support, and erosion of competitive advantage over time. The question is not whether to engage with AI, but how to do so in a way that is measured, pragmatic, and aligned with business priorities.

Before embarking on large-scale AI initiatives or making significant investments, it is critical that organizations and finance teams prepare for AI implementation. Preparation increases the likelihood of success and helps avoid costly investments in technologies that are not fit for purpose.

For me, the critical factor is awareness. Understanding what AI technologies exist, how they are being applied in practice, and what lessons and experiences are emerging from early adopters. This awareness enables leaders to make informed, timely decisions that are appropriate for their specific context, risk profile, and operating environment.

First Steps May Include:

I hope you find this roadmap useful. I'd be interested to hear about your experience with AI in Finance so far.

There is a lot of interest and excitement around the opportunities AI has to offer, and there's no doubt that AI is going to have a significant impact on how we do things. Any new technology brings both opportunities (such as gaining a competitive advantage) and risks (like not achieving the expected rate of return). At the very least, finance leaders should understand what AI is and the opportunities it presents for current and future business processes.

AI has been around for a while, with past use cases including search engines, spell checks, and navigation tools. However, recent advancements in AI have expanded its capabilities beyond these traditional applications. These improvements have also made AI more affordable and accessible for everyday use by a larger portion of society (as long as one has internet connectivity and a smart device).

A good friend of mine challenged me to make greater use of AI—specifically, ChatGPT—and I am excited about what it has to offer. For me, AI functions like a core worker or expert that I can call upon anytime.

My use cases, so far, include:

One of the greatest advantages of AI is its speed and ability to provide real-time feedback, which significantly boosts productivity.

There are plenty of learning materials available on how to use AI tools. While I haven't enrolled in a complete course yet, I find that learning AI is like learning a language—you get better with time and practice, and there are multiple pathways to achieve the same result.