Is AI the future of shared services?

Reading Time: 4 minutes

AI has the potential to complement human activities in Shared Services, driving efficiency for organizations on a massive scale.

Currently being used to automate mundane tasks in this sector, the long term projection is that AI will become a critical tool for delivering innovation in Shared Services. In order to facilitate this movement, we need to go back to basics and focus on one crucial ingredient – data. Without accurate data, AI will fail to deliver on its promise.

What was ‘the buzz’ at SSOW 19?

We recently attended the annual Shared Services and Outsourcing Week’s Autumn event, held in Prague. It is one of Europe’s longest-running business services events, with over 200 attendees and 70 speakers coming together in talks and roundtable formats.

It was great to catch up with old friends and make some new ones, too. This year, the event aimed to explore intelligent automation and data-driven service innovation (amongst other things). This was an invaluable opportunity to listen to delegates, understand their needs and propose solutions that can complement the systems they have already invested in, as well as hear from thought leaders and experts in Shared Services from a variety of companies across Europe. 

At SSOW 19, efficiency remained the watchword for organisations seeking to grow their profitability in a mature and fiercely competitive market, whether that be with AI or with more established Shared Services technologies. 

Shared services – an evolution.

Shared Services aim to make data processing more efficient, saving organisations time and money. But they also yield other benefits, enabling managers to resource their people more strategically. By centralising and (where possible) automating mundane tasks, management can focus on adding real value to the business via human-centric activities such as sales and relationship management. And thanks to greater specialisation and oversight, Shared Services reduce costly errors that can harm the wider brand. 

Shared Services used to be cost centres, but over the years they’ve evolved into profit centres. They no longer fight fires for the business – they optimise processes that drive revenue and ultimately, growth. Those with spare capacity even provide services to other organisations. The imperative to generate net margin for the wider business had led to managers thinking more strategically, with longer-time horizons. And a critical piece of this puzzle is data acquisition.

Accurate data is everything

Traditionally, the focus in Shared Services has been on processing documents coming into the business, for obvious reasons. This was especially true when technology was in its infancy, but in recent years, the focus has shifted to data analysis of documents such as orders as well as invoices, to optimise the wider supply chain ecosystem. Truth be told, as all of these processes are interrelated, organisations need to be holistic in how they address them – a siloed approach is ineffective when pursuing optimisation. 

We’ve seen successful management teams adopt tactical and strategic approaches in order to capitalise on short-term opportunities, whilst driving sustainable growth over the long term. But in order for decision makers to adopt this holistic approach, forecasting, target setting and ongoing monitoring are imperative, particularly when it comes to cash flow and tax considerations.

All of this is impossible without accurate data. And the impact of bad data downstream can be huge. For example, the true cost of processing invoices is much higher than most people think, because of the overheads associated with acquiring and passing through accurate data. Invoices processed with old technologies like OCR often require augmentation using human labour, with staff manually correcting the errors in the data. Even then, keystroke errors are inevitable, meaning that costs pile up further. The bottom line is that relying on old technologies like OCR and human labour is a false economy.

Looking to the future

At SSOW 19, we heard that Shared Services are changing, something we at CloudTrade knew already. Who knows what this dynamic and competitive field will look like in ten years, or even five years time? AI promises to reshape the industry, but it should be focused on addressing mundane and repetitive tasks and complementing value added human activities, rather than replacing them. Furthermore, data analytics raises questions over data privacy and ethics if used for purposes other than those for which it was intended.

What we do know is that all innovation, all growth, will be built upon the quality of data that managers can leverage in order to drive improved customer acquisition and retention. AI promises to cross boundaries and connect business ecosystems – for example, by connecting buyers with suppliers, and then connecting with sellers’ manufacturing processes and supply chains. 

To benefit from the long term promise of AI, companies should first focus on the quality of their data acquisition, so that any investment in machine learning further down the line is built on solid foundations – and solid data. Bad inputs render AI worse than useless. A lack of accurate data risks turning huge opportunities into huge risks, by giving management an inaccurate picture of how the business is performing and where it’s headed, making it impossible to deliver the required business outcomes.

CloudTrade can help businesses large and small to get their data acquisition 100% accurate. Our stand at SSOW was inundated with enquiries from delegates (as experienced at the recent Ariba Live conference). One potential partner told us that data capture with 100% accuracy was exactly what he was looking for, but that he simply didn’t believe we could deliver. He quickly changed his disbelief after a demo with our team.

If you’re active in Shared Services and keen to explore ways to future-proof your business and benefit from 100% data acquisition to support the long term promise of AI, get in touch to discuss further.

0 replies

Leave a Reply

Want to join the discussion?
Feel free to contribute!

Leave a Reply