AI has been around for years in back-office functions like IT and finance, but with the boom created by ChatGPT and generative AI, it is everywhere now. Increasingly, front-office teams, particularly innovation and R&D groups looking to understand more about their customers, are using AI tools to dive deeper in their work.
Hans Mueller-Schrader, Engagement Leader at Cherry Bekaert, sat down with Hisham Nabi, Managing Director, and Rashaad Balbale, Senior Strategy Engagement Leader, in the latest Digital Journey’s Podcast to discuss where AI has been, where it is going and which exciting applications you should think about for your own business.
Within the Cherry Bekaert’s Digital Advisory practice, Hisham and Rashaad focus on helping clients with growth, innovation and automation, particularly with AI. Above all, they help clients make sense of AI and explore its capabilities to form market insights that enable companies to stay ahead of the competition.
In case you missed it, here are the highlights from the thought-provoking conversation.
Growth and Categories of Artificial Intelligence
To discuss the future of AI, it is important to first understand what Artificial Intelligence is and where it currently stands. AI is defined as the ability of machines of computer-controlled robots to perform tasks that require intelligence. The goal of AI is to create intelligent machines that can replicate human behavior.
AI can be categorized in to three groups based on their capabilities:
- Narrow AI is capable of intelligently performing specific tasks and is currently in a restricted stage.
- General AI is also known as Artificial General Intelligence and refers to machines that can mimic human intelligence.
- Super AI is self-aware AI with cognitive abilities superior to humans. It is a level at which machines with cognitive abilities can perform any task that a human can.
What AI Looked Like 5, 10 and 15 Years Ago
Fifteen years ago, AI, big data and data analysis were very limited. There were few applications compared to today, so business process automation relied heavily on rule-based systems. Machine algorithms were present but very limited.
Data analytics technology evolved a decade ago, and we began to see speech recognition technology emerge. Siri and Google Assistant were introduced to the public, marking the first commercial AI push. AI also became employed in regulated industries like finance, healthcare and defense during this time. We are currently seeing attention focused on security of AI with President Biden’s recent Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence.
Fast forward to 2018, when AI had started to rise. Chatbots and virtual assistance became the prevalent first significant use cases for AI. Booking or canceling reservations via chatbots and similar machine-learning techniques began to gain traction during this time.
Since then, AI has taken off dramatically. A study by MIT researched over three dozen organizations to find out their perceptions of AI, where they adopted AI and their analysis of it as a business tool. The study revealed organizations increased their use of AI tools more than six-fold in less than a decade. In 2016, just four percent of the companies surveyed by MIT used AI. Today, one in four of them do.
They also observed the companies that were not considering AI. In 2016 that was 26%, and today that’s zero. To stay ahead of the curve, companies need to consider innovative ways to incorporate AI and other evolving technologies into their business practices.
At Cherry Bekaert, we’ve worked with clients to transform and automate manual processes using AI tools. In one case study, a health company had a worker manually pull together multiple files to generate invoices. By implementing AI tools, we automated that task so human workers could focus more on high-value work functions. The bot replicated what the human was doing, but performed the jobs in the background without the concern of human error. Automating processes and implementing bots are the first steps into AI that have unlocked countless doors for companies looking to innovate.
The Evolution of AI
The evolution of artificial intelligence (AI) has been a testament to the predictions of Alan Turing, the founder of computer science. In 1947, Turing anticipated a shift in language and informed opinion, envisioning a future where discussions about machines learning would be commonplace. The continued evolution of AI has been marked by a progression from disputable notions to widely accepted concepts, an incredible shift.
As a term, “Artificial Intelligence” itself is traced to the 1950s with neural networks being conceptualized and designed. Since then, the field of artificial intelligence has undergone significant advancement and transformation. The advent of machine learning paved the way for more sophisticated concepts like deep learning, and AI models continue to evolve every year.
Introducing AI Into New Business Areas
AI requires a lot of data. Over the past decade, accelerated growth in the cloud has created an entirely new world that allows for much more interconnectivity. AI has allowed us to bridge traditionally isolated data sources like social media, building permits, patents, academic research and even TikTok videos, to name a few.
Text-to-voice on your cell phone, personalized recommendations on Amazon Prime, or interactions with Siri and Alexa are all enabled by AI. However, new advancements bridge more data than ever, uncovering new consumer insights and rapidly translating them into new product ideas. Learn about new AI Salesforce features in Cherry Bekaert’s Dreamforce 2023 Takeaways. This advancement in AI for business unlocks market insights to identify higher probability sales leads or accelerate product discovery by quickly designing and testing new product formulations.
In a case study for a financial services company looking to identify areas of opportunity to increase probability sales leads, a new approach was taken to look at traditionally disparate sources of data, from transactions to building permit applications. This data allowed the company to preemptively identify when commercial customers might need a new lending facility before they started inquiring. The bank could then have preemptive sales conversations and increase the likelihood of landing transactions.
The Future of Artificial Intelligence
Automation, customer service sales and pricing promotions are some of the top applications that come to mind when thinking about AI, but others have come to the table. The top applications involve uncovering truly novel consumer and market insights, and building predictive trend detection capabilities. The goal is to determine the next big thing consumers will flock to.
The typical R&D discovery process involves time-consuming focus groups and in-person research, which have always been relatively analog. This has taken even best-in-class companies months, if not years, to understand when the market will be ready for mass adoption. It often results in notable companies leading or stagnating in the market.
A great example is the booming sparkling water market. LaCroix, arguably the first mainstream sparkling water brand, was first launched in the 1980s and it took nearly 30 years before the market was ready to purchase their products. In that time, companies like Pepsi were able to time the market better and enter with a more successful product at the right time.
The winning companies are the ones who figure out exactly when to go to the market and when not to. The true power of AI is taking insights and trend identification and layering on prediction.
AI Implementation: How To Get Started
While companies are excited about the capabilities of AI, there’s a lot of noise in the marketplace, which can be daunting. Here is a step-by-step guide for how to begin your AI journey for business:
- Define the AI goals and use cases. These can be business problems or opportunities specific to your company and market, or products where AI could significantly impact you.
- Assess and audit your data. Since AI relies on a lot of quality data to train the models and make accurate predictions, having that data in place is critical.
- Start testing. Begin with small-scale projects or proof-of-concept initiatives to test the feasibility and impact before investing significant time and resources to push something out at scale. Try something small to get a sense of the right solution and put yourself in a position to adapt and learn quickly.
- Work with the right partners. There are many excellent solutions in the market, and it can be overwhelming to select one. Finding the right partner or partners can bring advanced tools, frameworks and expertise to help you start much more rapidly and efficiently.
At Cherry Bekaert, we focus on ensuring that you have a strong foundation and strategy in place so that tools like AI can succeed. We would love the opportunity to help you tap into the power of using AI.
