Building the case for AI-driven decision making  

21st June 2021

Author:
Maria Paris

Building the case for AI-driven decision making  

The pace, volume and value of business decision-making are accelerating. Data, analytics and artificial intelligence are increasingly powerful and important capabilities to automate tasks and support those responsible for making decisions.

Machine learning algorithms can be used in bespoke decision support tools to analyse large volumes of data better than the human mind and can drive improved insights that will lead to enhanced decision making, operational efficiency and financial outcomes. 

The business case for investing in decision-support tools is a simple one. By focussing on high-value business needs Arca Blanca delivers measurable improvements in decision quality and cycle time.

A data-driven crystal ball

Arca Blanca worked with a retail service provider to predict sustainable rent levels for tenants across 300 different sub-sectors and geographies, allowing users to quickly understand the effect of economic scenarios on their industry. These predictions have been proven to have a mean absolute percentage error of <10% which shows how close data-led predictions have been to the actual results.

Saving time is money

The brain’s computational constraints mean that a well-performing tool will always be capable of doing more in a shorter time scale. This will improve existing business processes and allow users to do fewer repetitive tasks and focus on work that requires more judgement.

Arca Blanca worked with a logistics company to gauge demand for warehouses based on size needs. The bespoke tool we designed reduced the time analysts took to iterate a site mix from weeks to just minutes by replacing manually adjusted excel spreadsheets with the tool that could recalculate results upon changing parameters and combine this with the insights from large data sets.

Multi-million-pound improvements

Faster decision making will lead to improved financial outcomes. Creating price predictions for a housebuilder based on local transactional data gave the company an unprecedented amount of local knowledge on demographics, past and future lease events, while customer segmentation data allowed for more targeted marketing. It resulted in multi-million-pound profit improvements. Another decision support tool for a logistics company reduced costs by £8m while generating the same income, increasing yields by 8%.

Better equipped for the unknown 

For those concerned about giving up too much control, decision support tools do not eliminate intuition entirely but support faster, more accurate decision making while also delivering improved results. By addressing human bias concerns and our inability to process data the way machines can, these tools allow for the elimination of intuition’s flaws, while employing its strongest facets.

A user can then influence final decision-making by overlaying scenarios and modifying macroeconomic, sociodemographic, consumer and business indicators. This can better help business leaders plan for the unknown and adapt quickly to shifting economic conditions and see how resilient their planning is. In the context of COVID-19, our decision support tools have enabled our clients to see how retail and commercial space rents would change based on different UK recovery scenarios.

There is a competitive edge to be captured by employing decision support tools to improve the accuracy of data and speed of decision making. Through these tools, data-driven businesses can get more value from both internal and external data, highlighting previously unknown improvements.

About The Author

Maria Paris is a senior consultant at Arca Blanca. She is a chartered accountant specialising in business model and customer experience design and has a strong track record in delivering decision support tools in the property sector.

About Arca Blanca

Arca Blanca is an integrated consulting and data company, resolving complex growth and profitability challenges. We help businesses of all types thrive in the face of unprecedented change and upheaval driven by new technologies.

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