McKinsey: Leveraging Data & AI to Reshape Procurement

As AI and analytics continue to transform how procurement works, many CPOs say they are not ready for the data-driven transformation, according to McKinsey
As AI and analytics continue to transform how procurement works, many CPOs say they are not ready for the data-driven transformation, according to McKinsey

A report from McKinsey has shown how procurement teams can harness the power of new data to reshape procurement into a strategic lever for organisations for value protection and creation.

Despite this, a survey of CPOs shows a feeling remains that there are still areas holding back their digital ambitions, as many procurement functions struggle to transform themselves into data- and technology-driven organisations.

The power of data

In today's uncertain business landscape where price volatility, geopolitical tensions and sustainability targets all converge, the procurement function stands at the forefront of navigating these complexities, acting as a strategic lever for value protection and creation.

Procurement operates at the confluence of huge quantities of data, flowing from within the organisation — for example, spend, demand patterns and specifications — and from without — suppliers, market insights databases and the wider web.

As the world becomes more digitised, connected companies must harness the data and create new tools to make faster, better sourcing decisions. By mastering this information, procurement teams can achieve strategic objectives that go far beyond traditional cost, quality and delivery metrics.

Better data can support activities and decisions across the sourcing life cycle, from the development of category strategies and the assessment of potential suppliers to the execution of negotiations and ongoing supplier performance management. Done well, this can increase the pipeline of value creation initiatives by up to 200%.

  • Optimising spend and demand: AI can automate category management, enhance demand forecasts, and improve sourcing decisions. Tools like generative AI can facilitate autonomous trade decisions and automated contract generation.
  • Managing external drivers of profitability: Combining internal and external data allows for better predictions and strategies around commodity prices and product margins, enhancing profitability and supply resilience.
  • Managing supplier performance: Digital dashboards and AI tools improve supplier performance tracking and negotiation, leading to more informed and effective procurement strategies.
  • Managing supply risks: Digital twins of supply chains offer near-live risk assessments and simulations, enabling faster and more efficient responses to disruptions.
  • Leading on sustainability: Procurement can use data to estimate carbon emissions and support sustainability goals, fostering better supplier relationships and achieving environmental targets.

Speaking on McKinsey Talks Operations podcast, Marie El Hoyek, Partner at McKinsey & Company said: “Generative AI might be relatively new, but we have years of experience in scaling digital transformations. One of the biggest challenges is the pilot trap. Building a pilot or innovating with the technology is great, but transforming an organisation is a whole different playing field.

Marie El Hoyek, Partner at McKinsey & Company

“Nicolai talked about the business-led mindset to prioritise applications that are useful with real business ROI. Beyond that, getting a real impact out of any digital change, and for generative AI in particular, will always be both a human and systems question. The way I’d summarise it is, without people, the best technology has no impact. We need to take our people on a real change journey to build the capabilities to use this technology, develop this technology, but also just to know what you can ask of this technology. And by the way, in terms of developing it, there are new skills that are needed here.”

Data challenges still remain

Despite the revolutionary power of this new technology, there is still concern among leaders in the procurement space. Senior leaders know they need to take advantage of more-effective use of data, as it is seen as vital. Many, however, are still struggling to transform themselves into data- and technology-driven organisations. In a recent McKinsey survey, CPOs highlighted three key problems that they believe are holding back their digital ambitions: issues with data quality and access, lack of clarity over the business case for new digital or AI applications and difficulty driving adoption of the new tools at scale.

  • Data quality and access challenges: CPOs expect data, analytics and gen AI to play a core role in every business decision by 2030, but respondents to the survey admit that their data infrastructure is not ready to support this ambition. Another 21% say their data infrastructure maturity is low, with less than 70% of spend data stored in one place. An additional 30% think they have average levels of data maturity and even those who have implemented systems to give them a single source of truth for all spent data admit that this data is not cleaned and categorised. These systems may also lack important information from outside the procurement function, such as quality or specification data, or external data from suppliers, customers and the wider market.
  • Difficulty articulating the business case: Procurement teams also find it difficult to secure funding for analytics and AI projects, often due to the lack of a compelling business case. This challenge is typical in organisations that follow a 'technology-back' approach, that is, selecting software and solutions without a clear link to business value creation opportunities.
  • Low levels of adoption: Organisations that overcome the first two challenges often run into the third. Even when they have built a business case and proved the effectiveness of a digital use case in tests, they find it difficult to embed its use in their core processes and teams’ ways of working across the organisation. This is a common challenge in data analytics transformations regardless of business function, leaving many organisations stuck in pilot purgatory. It is especially common in procurement, where teams are often focused on delivering quarterly results, or submerged by short-term obligations and do not take the time to understand and adopt new technical solutions.
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The recipe for success at scale

McKinsey outlined how organisations can achieve success when it comes to harnessing AI. The report states that companies that have achieved this tend to get a few vital things right. They focus on a small number of high-value digital and AI use cases.

They build and, crucially, own a robust data infrastructure to support those use cases. These companies also spend as much time thinking about people and processes as they do on technology: adapting their core business processes and operating model, upskilling and reskilling their people, and steering change across the organisation.

  • Focus on high-value use cases: Prioritise a few impactful AI solutions that address core business needs and user pain points
  • Develop robust data infrastructure: Create a dedicated procurement data model with a focus on high-priority data needs, ensuring collaboration with IT for best practices
  • User-centric design: Ensure new tools are intuitive, integrated seamlessly with existing systems, and supported by comprehensive training and communication
  • Invest in talent and skills: Increase the number of data-savvy procurement professionals through hiring and reskilling
  • Monitor progress and impact: Establish a transformation office to track implementation and value delivery, allowing for timely course corrections.
Transforming the procurement function is a journey which can take anywhere from six to 18 months, according to McKinsey.

From vision to transformation

Transforming the procurement function is a journey which can take anywhere from six to 18 months, according to McKinsey.

A successful transformation requires vision, ambition and sustained commitment from senior leadership. It also depends upon teamwork, engagement and excitement from across the organisation. Any CPO embarking on such a journey should begin as they mean to continue: by engaging with internal and external stakeholders. By understanding what is needed from a high-performing procurement function, and where the pain points are, companies can get their procurement priorities right.

Technology partners are also key. They include the organisation’s internal IT function and external suppliers of data platforms, AI technologies and analytical tools.

Armed with a clear picture of business needs and potential solutions, procurement can revise its technology roadmap. It should do this with twin objectives in mind: early implementation of AI and analytics solutions that create value while building the foundations of a data platform that will meet the organisation’s long-term needs.

Quick wins from the first use cases are critical in building momentum for the transformation. By showing end users and business leaders what data and analytics can deliver, they help foster excitement and drive engagement across the organisation.

As businesses begin to deploy those high-priority solutions, procurement leaders should keep another group of collaborators at the forefront of their minds: the procurement teams who translate data-driven insights into value for the business.

Focusing on the adoption of AI technologies from day one helps procurement build solutions that work better, scale faster, and create more value for the organisation.


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