Dataiku – how to make the most of enterprise AI in recession

Gregory Herbert, SVP EMEA at Dataiku, shares how companies can get more out of their tech platforms in the face of economic volatility

Some companies see AI and other enterprise technologies as tools to help them do what they are already doing at less cost or faster. Others yet see these technologies as something strategic that is going to help them do something no one else in the industry is doing. 

In an economic downturn, when technology teams are trimmed, and spend on technology is closely considered, enterprise applications (including AI) must prove themselves and become productivity multipliers to quickly demonstrate value. 

In the face of economic volatility, will companies slow or stop investing in technology? What are the dangers of budget cutting on investment in technology initiatives? And how can companies get more out of their enterprise technology platforms?

Here, we talk to Gregory Herbert, Senior Vice President & General Manager, EMEA at Everyday AI provider Dataiku, to find out more.

In the face of economic volatility, will companies slow or stop investing in technology? 

With economic stress currently a primary concern for most organisations, alongside the subsequent shifts in consumer patterns, there is likely to be a slowing of investments in technology. Enterprises are understandably looking to cut costs wherever possible and, if quantifiable value from AI is not seen, dropping investment seems inevitable.

However, deprioritising AI may be short-sighted. Instead, leveraging AI projects could have the power to increase efficiency and generate real cost gain. Regardless, it is naive to ignore the associated expenses of such a task, and so to generate short-term impacts, lowering overall analytics and AI project costs should be the initial objective, as this will determine whether long-term benefits are delivered effectively.

What are the dangers of enterprise budget cutting on existing investment in technology initiatives?

Enterprise budget cutting is aimed to ensure immediate business survival. While it may seem intuitive to slow down existing investment in technology initiatives, the value and scale they can provide may outweigh short-term costs if navigated correctly.

Bespoke, infrastructure built in-house may for example handle early use cases, but is unlikely to scale well to handle more advanced use cases, and may also be time consuming to administrate. Buy or build investment decisions must come down to whether the upfront costs will outweigh savings down the line.

AI vendors must focus on engagements that solve real, pressing, everyday AI issues, and help companies optimise costs and increase efficiency, in turn permanently lowering expenses. Streamlining the entire business model is the biggest priority. Teams need to be equipped with the ability to respond to market changes rapidly ahead of them becoming problems.

The most valuable method of achieving this is through leveraging technology. If budget cuts obstruct investment, this could be damaging for durable cost-saving measures and business optimisation.

How can companies get more out of their enterprise technology platforms in the face of economic volatility? 

When businesses are looking to where they should first apply AI, the areas that seem to present a massive opportunity may actually already be highly optimised. In that case, AI can become overly expensive, offering no further gain. There is then a risk of implementing an initiative that does not generate enough value. To avoid this, companies facing such decisions must consider more creative applications of AI.

AI can deliver significant acceleration on cost reduction initiatives through speeding up analyses and detecting inefficiencies; streamlining data processing in business processes; and enhancing processes with AI-powered approaches.

In terms of elevating operational efficiency, formulating a recommendation stems from a level of insight. This is majorly reliant on analytics. Having agile access to data to gain an understanding of past trends, using modern techniques to best identify critical factors, or leveraging process mining are essential first steps to reviewing and assessing the current state before taking the appropriate action.

As the need to monitor and report on company activities across multiple dimensions has grown, so has the development of complex processes heavily relying on data ingestion and processing. The gap must often be bridged between systems and the level of human intervention required. This remaining need for manual work provides a key opportunity to rationalise and reduce overhead. Empowering teams with enhanced analytics can revamp these processes and unlock potential that is blocked by tedious processes.

Process efficiency does not need to be limited to existing data-heavy processes. Applying end-to-end data engineering to advanced data science techniques can provide further opportunities for AI-powered approaches.

There are a number of business use cases that can be selected for AI optimisation. It is a matter of thinking creatively and measuring costs relative to the organisational context and expected output to reinforce the likelihood of positive outcomes. 

What should companies look to avoid doing with enterprise technology in an economic downturn?

Limited budgets and pressures to invest in technologies with unforeseen implementation costs can generate greater inefficiencies and waste economic resources. 

Larger teams aren’t necessarily reflective of better productivity or success of an AI initiative. Case studies of even larger companies utilising smaller teams of data scientists as capable and dynamic forces, particularly during good times, only adds to the justification of doing so during a recession.

Managing AI in such a way that an organisation can increase its headcount without hiring more people can be done with the correct management and tools. AI should help data scientists to solve hard problems at a fraction of the cost, in turn helping other employees to take on the less difficult or leading-edge tasks. 

Moving data around is an expensive process. Continuing to do analytics and building new models, even while not being able to replace lost or reduced headcount, can significantly lower expenses. Data can be moved to the most cost-effective platform in the meantime for productionisation.

How can enterprise technology make companies more resilient and help them to weather the storm?

Resilience is key during times of economic volatility, and enterprise technology can play a major role in accelerating this future. Reviewing measures and already adopted initiatives as a manner of assessing for any vulnerabilities and efficiency of operations moving forward is highly important for prevention of crises.

Investment decisions must be made wisely. Mindless embracing of technology is unlikely to be fruitful. Rather, businesses should deliberate and ensure that any projects are valuable assets in building resilience and improving business practices. This is not to say that investments should come with immense hesitation, but unwise investments can lead to more problems and greater expenditure. Considering more creative applications of existing initiatives and leveraging AI to further enhance can increase the resiliency of businesses.

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