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Market Predictions Are Precarious: Throw Yourself an AI Lifeline

Scaling AI Joy Looney

If you haven’t checked out Dataiku’s History of Data Science experience yet, start with “AI Winter: The Highs and Lows of Artificial Intelligence,” where we look at the hype cycles of AI with a particular focus on the low seasons — AI winter, periods of time when optimism and interest in AI dips (sometimes due to lack of vision or imagination and sometimes due to overarching factors). It’s worth noting that the onset of these cyclical changes is predictable to a certain degree, and considering the instability of the market today, this conversation is more relevant than ever. This brings us to the question of whether or not organizations should make an effort to circumnavigate these low points and continue to reach for innovation despite trepidations. Let’s discuss.  

→ Check Out the Full History of Data Science Article

The Financial Factor Lurking Upon Your Future Plans 

If you’ve been reading news article after article predicting economic downturn, at this point, it might seem as though the Jaws theme song should be the soundtrack for all future planning.

Take a look at this quote from Sequoia Capital, “We do not believe that this is going to be another steep correction followed by an equally swift V-shaped recovery like we saw at the outset of the pandemic. We expect the market downturn to impact consumer behavior, labor markets, supply chains, and more.” 

You can almost see the jagged fin breaking the water’s surface as you reach that last period. But, humorous shark movie metaphors aside, the concerns induced from market forecasts are very real and have the potential to influence organizations’ leaders to call for reduced AI project funding, guiding their initiatives into the low point of the cycle we mentioned above — right into an AI winter. 

winter road

So, Winter Is Coming?

It’s complicated. The truth is that even though predictions might lend themselves to a “yes,” what actually transpires depends entirely on the decisions that organizations will make in light of the speculation surrounding market forecasts. 

These conversations might serve as a wake up call of sorts, but this is not the time to tumble into fear-invoked decisions. The possibility of a market downturn that will potentially impact everything will urge organizations to take a long, cautious look at where to lean out resource allocations. The kicker here is that deciding to slim down in the wrong places could leave many organizations missing out on the very thing that would could carry them through volatile times — *hint hint* AI. It’s in companies’ best interest to reach the realization that now, rather than later, is the moment to jump on board with an AI strategy of resiliency as opposed to sinking into what they believe is a safe-zone but is actually a territory of hazardous stagnation. 

Choose Rearrangement Over Reflex 

Many organizations might reflexively turn away from AI, but there are many reasons why your organization should prudently navigate out of this trajectory in favor of an actionable path that balances the demand to address cost realities with the need to remain flexible for scaling as opportunities arise. 

If you’re still viewing AI as the cherry on top, it’s time to rearrange your approach to AI. Let AI be the lifeline that guides your organization through potentially volatile times. You’ll emerge on the other side ahead of organizations who abandon their AI efforts — the reason being that AI is the key ingredient for sustainable, resilient, and scalable day-to-day business practices in today’s increasingly digital world. 

But remember, doubling-down on AI doesn’t mean you have to ignore financial responsibilities and goals! Here are some of the key ways for you to boost your AI efforts while remaining realistically mindful of cost

Upskill Non-Technical Teams 

Save money by making the most of what, or rather, who you have. Systemization of data and analytics projects is key for simplifying and growing high-return business use cases, but this requires all teams, not just data science teams, to incorporate AI into daily decision making. Siloed teams do not lead to efficient practices, and in addition to placing an emphasis on collaboration, more people need to master the ability to weave the capabilities of AI into everyday processes in pragmatic ways.

→ Here’s How to Upskill Your Teams

Inspire Experts to Do What Experts Do Best 

In times of uncertainty, it might feel safe and comfortable to put your best minds on projects with low-risk and immediate reward, but these projects won’t significantly move the needle in the grand scheme of things and forcing talent with highly advanced skills into these projects can actually harm you in the long-run by driving away these people. 

So, instead, opt for keeping the momentum going. Motivate and equip expert talent with the tools they need in order to tinker in moonshot projects. Remember the saying, “You reap what you sow”? These projects with high-reaching goals are the ones with particularly unique value and the potential to change the course of company history. Big projects backed by expert skill can bring big payoffs in the future, so be careful to not desert the areas where ROI could be the highest. 

→ Read the Steps to Inspiring Your Experts

Wave Goodbye to Busy Work and Wasted Time 

Systemization and automation of AI projects is key for saving precious time. Writing manual code for repetitive tasks is simply not scalable, and particularly not so when focusing on value-added work is of the utmost importance. Leveraging AI allows you to reuse code and in turn do more with less, saving time and saving money for tangible business impact. Some other core components of systemization include: increased data access and defined governance frameworks, tool integration that meets the needs of users, and responsible project lifecycles that ensure model reliability. 

→ Learn More About Why Systemization Matters

Identify Key Use Cases to Optimize & Accelerate With Everyday AI 

Strike a balance between your risks and reward. Through predictive analytics, identify areas for improvement and optimize your current projects with the express purpose of cost reduction. In addition to optimizing the projects that you’ve already heavily invested in, AI will help you accelerate your future projects through providing insights that simplify complex problems to inform future decision making. Just one example of this is the use case of churn prediction. Leveraging AI in a strategic way makes sure that resource allocation is practical everyday, in every aspect of your business operations. 

→ Discover How to Use AI to Combat Costs

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