In recent years, the role of procurement within organizations has grown in importance. It has evolved to be a strategic "value driver," delivering not just savings but also product innovation, improved cash flow, reduced risk and even revenue growth.
Such a momentous shift has meant the workload of the avearge chief procurement officer (CPO) has risen dramatically, and they are being increasingly relied on to do more within the business. Despite this increased workload, few organizations have increased procurement resources and teams are still lumbered with menial, low-value tasks which consume significant time. As a result, CPOs have actively sought to automate tasks and free existing capacity for more strategic activities.
Making procurement smarter
Artificial intelligence (AI) is a key technology in delivering this automation and reducing the time constraints on CPOs, whilst also providing insights to inform the procurement strategy. Recent research by Forrester found that 55% of business leaders now plan to adopt AI in procurement over the next 24 months. This planned investment represents a huge shift for procurement teams and a change in the way CPOs and procurement teams work.
The appeal of AI is huge as it offers tremendous potential to automate complex and labour-intensive tasks. AI can also spot sequences and trends in data and deliver useful insights. For example, AI applications can identify savings opportunities or fraudulent activity. It can also be used to centralize purchasing contracts by unifying the buying habits of different departments that use the same suppliers.
Having good quality data is imperative to building artificial intelligence bots which can assist with tasks. (Image: Pixabay)
AI has the capacity to enable a more efficient and cost-effective procurement process and identify areas for improvement. Better yet, the cost and time efficiencies gained through AI can be amplified across procurement and associated teams, without applying additional resources. For example, once an error like a duplicated invoice payment has been identified, AI can identify all other instances of this or similar errors, enabling scaled improvement of invoice payment processes across the organization. Real-time intelligence capabilities allow these decisions to be almost instantaneous and lead to a more streamlined procurement function.
There may be trouble ahead
The benefits of AI in procurement are clear, so it is little wonder that the appetite for it is growing. Yet there remain far too many disappointing AI projects due to short-sighted approaches to the technology. In their haste to implement AI, many organizations are failing to lay the foundations that will allow them to realize the promised value. Issues such as poor data quality regularly hinder progress if organizations don't take simultaneous steps to address gaps. Without strong and clear data, it will be impossible for AI to make accurate and informed decisions, and the potential value from any AI investment will be limited.
Enterprise-wide data quality needs to improve, and the Forrester research reveals 59% of business leaders reported that poor data quality undermines their ability to get full value from AI. More worryingly, 10% said their data issues were insurmountable.
Data quality issues should not be a barrier to implementing AI, yet the quality of insights is still ultimately dependent on the volume and quality of data mined. It is important that companies address any underlying data issues to gain useful insights and position themselves to achieve the full potential of AI. To remedy this problem, organizations must implement AI in conjunction with cleaning up their data, rather than using poor data quality as an excuse for inaction.
Clean data, clear insights
To begin with, organizations need to digitize procurement in order to stop producing poor data and make procurement smarter. Digitization and integration with other systems ensures that data capture is improved, but it doesn't fix existing issues with the master data.
To do this, smart procurement technology platforms can serve as a master data management solution. This technology can fix issues in an organization's current master data and integrate with all back-end systems to correct errors, duplications and other data quality problems. Furthermore, smart procurement can automatically clean data and make it available for real-time reporting, which can allow organizations to optimize spend.
An holistic approach to fixing existing issues and digitizing data capture can help CPOs and procurement teams to operate in a much smarter way.
But to get to this stage, CPOs need to ensure executive buy-in. Research shows 44% of procurement professionals reported they do not have the support of the C-level executives. To get this backing, CPOs need to demonstrate their procurement objectives match wider business objectives and explain how AI and better data can help deliver value for the wider business. For example, helping to enhance customer experience to meet rising customer expectations.
Google's data allows the company to build products such as Google Home, and determines which ads to show when you search for results. (Image: Kevin Bhagat)
Don't let excuses hinder adoption
AI is finally coming of age and can add transformational value for procurement leaders and their teams, freeing capacity and providing better insights.
However, to realize any of this potential, organizations must ensure they implement the correct data strategy from the start. The accuracy and depth of AI insights is dependent on the quality of data, but a lack of quality isn't something that should be used as an excuse for inaction. Improving data is something to tackle in parallel with AI adoption.
By implementing smart procurement technology which can clean data whilst improving data capture for future entries, CPOs and their teams can realize the true potential of AI to unlock a wealth of opportunities and fast track insights and results.
Alex Saric, smart procurement expert, Ivalua