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Financial Friction: Examining Data and AI Friction Part Three

It’s so often said and so often true – money makes the world go ‘round. And while there are certainly other factors at play when it comes to organizational data and artificial intelligence (AI) strategies, financial considerations play a huge role in determining whether initiatives succeed or fail. In this last of a three-part series examining the data from our global survey report on data and AI friction, we’ll take a closer look at where financial friction stems from, how it’s affecting organizations, and more. 

 

Prevalence and Causes of Financial Friction

Let’s start with a succinct definition: Financial friction presents itself when budgets are tight, resources are spread thin, and projects need to provide a return on investment. Financial friction can be most apparent when trying to invest in a new initiative or trying to scale efforts with expensive, legacy investments. 

As in this series’ previous articles, it’s worth mentioning how prevalent data and AI friction is today: Within the past two years, 33% of the survey’s 2,037 respondents said more than half of their data science projects never made it to production. On the AI side, 42% of respondents said their organization has experienced an AI project failure within the past two years. Additionally, 26% said more than half of their AI projects have failed within the past two years. On a more subjective note, 63% of respondents agreed with the sentiment their organization makes working with AI tools more complicated than needed. Undoubtedly, friction is a fact of life.  

Now, let’s examine how financial friction specifically is contributing to overall friction within organizational data and AI initiatives. To start, 25% of respondents cited financial constraints — specifically those that prevent them from accessing new technology infrastructure — as a point of friction that negatively affects AI initiatives within their organization. This data point is both financial and technological in scope, as a lack of funding for new technology is both a financial strain and causes technological friction down the line. 

In addition, 28% of respondents said their leadership is too focused on data and AI strategies’ upfront costs to understand how investing in AI and machine learning would benefit their organization. On this same note, 33% of respondents said that the “high cost of implementation” — whether real or perceived — is one of their organization’s shortfalls when relying on AI tools to complete projects. Though short-term costs may seem significant to executive decision-makers, the long-term missed opportunities – including chances to minimize operating costs, hasten time to market, increase efficiency, and more – are almost surely more costly. This is especially true if competitors within the same sector or industry are already investing in organizational data and AI strategies, thus gaining a head start that can have exponential benefits to early adopters.  

On a related note, respondents struggle scaling their financial investments in data and AI strategies and that because of financial friction, 40% of respondents said they’ve wasted money as a result of an AI project-related failure within the past year. As mentioned in this series’ earlier articles, this friction also has other effects in different areas within organizations. For example, wasted money also usually means wasted time, damaged morale, and can create hesitancy toward adopting these strategies. Lastly, below, you can see what factors respondents identified as top challenges that prevent them from leveraging their financial investment in AI strategies.


 

Conclusion

Throughout this miniseries, we’ve covered the three main types of friction that hamper organizational data and AI strategies today: organizational friction, technological friction, and financial friction. Throughout our exploration of these areas of friction, one thing remains clear – friction is the main reason data and AI projects fail. Moreover, it affects organizations of all industries and sizes around the globe. 

There’s no single silver bullet that will solve friction throughout these three areas. Like any major organizational strategy, data and AI initiatives require buy-in and coordination between a myriad of teams and individuals, alongside robust investments in the cutting-edge technology and tools that enable everything. 

That said, Altair® RapidMiner®, Altair’s data analytics and AI platform, offers the most comprehensive frictionless data and AI experience on the market today. In all, Altair RapidMiner eliminates friction:

  • Between users and data — Our platform works with any data and helps build trust in the insights that data provides. We empower users to extract and prep data easily from any source, working with reports and PDFs that are core to the business. We build trust with a wide array of features that explain complex data models and serve up the insights to the right stakeholders in real time. 
  • Between data and domain experts — Altair RapidMiner scales AI initiatives without requiring a big team of data scientists or expensive services engagements. We help organizations upskill their workforce so novices and experts alike can leverage the tools needed to provide data-driven insights. Teams can collaborate on projects easily while still working the way they want to between our Auto ML, visual workflows, and coding options. 
  • From idea to production — Our platform and methodology are designed to get models deployed so they can deliver business value right away. We work with organizations on an AI roadmap, identifying the highest priority use cases based on feasibility and value, and then help tackle those first. Altair RapidMiner is truly end-to-end, from data ingestion and modeling to operationalization and visualization. No matter where data is coming from or where insights need to go, everything is easily distributable and consumable at scale.  
  • When infrastructure, tools, or vendors change — Altair RapidMiner supports diverse infrastructure landscapes — from mainframes to cloud — and alleviates the pressure of modernizing expensive legacy environments. With Altair SLC™, teams can create, maintain, and run SAS language programs, models, and workflows directly in a multi-language environment without needing to license third-party software. We offer flexible licensing and usage of all Altair’s data analytics and AI products via Altair Units, Altair's gold standard software licensing system. Altair Units gives users the flexibility to run software anywhere, the freedom to choose what software tools they need when they need them, and unparalleled value that maximizes use and minimizes cost. 

Click here to read the next part in this series, "Organizational Data and AI Strategy Adoption: Examining Data and AI Friction Part Four." In addition, check out Altair’s additional data and AI resources, including: