Falling for AI in a Data-driven Business Environment

Falling for AI in a Data-driven Business Environment

Like all great love stories, the dawn of AI began with passion, intrigue, and the search for the right partner.

But in 1952, technologist Arthur Samuel couldn’t find a partner — for checkers, that is. So he built the world’s first intelligent self-learning algorithm on IBM’s first stored thinking computer, the 701. The Samuel Checkers-Playing Program later defeated the fourth-ranked checkers player in the nation. Four years later, mathematician John McCarthy coined the term “artificial intelligence” during a summer research project at Dartmouth, and a new class of machine learning was born.

Artificial intelligence quickly matured from a backroom computer project to a mainstream productivity tool. Here are a few notable dates along the way:

  • 1961: Graduate student James Adams builds the Stanford Cart, one of the world’s first examples of a self-driving vehicle.
  • 1966: German professor Joseph Weizenbaum creates ELIZA, the world’s first chatbot, as an AI psychotherapist using natural language processing (NLP) to communicate with human patients.
  • 1979: Executives from SRI International, Carnegie-Mellon University, the Jet Propulsion Laboratory, and other organizations jointly form the American Association for Artificial Intelligence (AAAI).

The global artificial intelligence market promises to reach a $305.9 billion valuation by the end of 2024. That valuation could grow nearly 650% by 2030. But despite benefits in productivity, cost reduction, employee safety, and more, many companies are still hesitant to give AI the love it might deserve. It’s time we explored why.

Exploring the Roots of AI Hesitancy

Only 35% of all companies worldwide use some form of artificial intelligence. 50% intend to learn more about it. The rest have yet to express plans to incorporate machine learning across their organization for several reasons. The fear of the unknown, data privacy concerns, or practical hurdles like ongoing investment can keep companies from embracing AI and its benefits.

AI is a complicated field.

More than other forms of technology, artificial intelligence is a complicated tool. It combines elements like neural networks, NLP, and trainable algorithms that seek to make intelligent decisions. Even to AI-savvy teams, these hurdles can feel intimidating.  However, we see that many of the same access technologies that teams have used for years maintain great applicability to AI. Enter the trusted API.  In plain terms, Application Programming Interfaces (APIs) make something complicated — such as artificial intelligence — more straightforward to implement.  

AI can represent a legitimate security concern.

One of the most significant challenges to AI implementation is internal security. Particularly for industries like finance and healthcare that handle sensitive information, entrusting that information to computer systems can feel like more of a risk than an opportunity. Businesses must still satisfy traditional data privacy and security laws when integrating new AI solutions. However, AI can hugely benefit highly regulated companies.  For example, Terazo recently leveraged ChatGPT4 to write thousands of compliant SMS Texts delivered by Twilio.

Not only could the solution generate thousands of compliant messages. but it saved time. The compliance was built into the AI solution, which enabled Twilio and Terazo’s client to trust the communications. While security concerns are worthy of consideration, AI’s use in compliant environments also has massive benefits.  

AI requires ongoing investment.

AI algorithms grow and change with use, requiring companies to continue investing in AI. It is not a great set-and-forget tool. For best results, teams who use artificial intelligence tools will require ongoing training to stay ahead of the rapidly evolving field. However, an AI partner like Terazo, with extensive experience in the technologies that touch and impact AI, can be a huge help. Our teams can minimize downtime and project disruption during implementation and keep your internal teams abreast of new developments in the field.

Finding Your AI Love Story

There’s no tool like artificial intelligence when it comes to collecting, analyzing, and interpreting your data. And when that data is customer information, you can derive powerful insights about the way your customers think, purchase, and express brand loyalty.

Here’s the catch — like love itself, commitment to AI is a two-way street.

If you’re going to leverage AI for the data insights it can unlock, it’s important first to prepare your data sets.  Even if you don’t know exactly how you plan to leverage AI tomorrow, you can prepare your data for integration today.  A strong data engineering partner can help you evaluate, organize, and prepare your raw data to power AI.  We work with clients to shorten the distance between data collection, analysis, and utilization with fully realized AI strategies.  Like a matchmaker, we can unlock the promise of AI for your company and set you on the path to a lifelong commitment.    

Our Valentine’s gift to you is a complimentary 30-minute data audit with members of our data engineering team. It’s the fastest way to identify your company’s data strengths and weaknesses and learn how you can implement time-saving AI strategies to supercharge your revenue pipeline.

Torie Flood

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