By Jon Reilly, Co-Founder and COO at Akkio, working to level the playing field for AI in business.
Even if you’re not familiar with artificial intelligence (AI), you probably know that it’s a hot topic. You might have even seen some of your favorite brands testing out new ways to use AI in their products and services.
In fact, there is so much interest in AI right now that most major companies have dedicated entire teams to explore how they can incorporate the technology into their business models. At its core, AI aims to make decisions based on data.
Research from CB Insights shows that AI acquisitions saw a more than six times uptick from 2013 to 2018, and further analysis shows that AI startup funding has surged in Q1 of this year, surpassing $20 billion. The boom is clear — AI is officially mainstream. So what does that mean for your business?
The Traditional Approach to AI
In order for any company or organization to realize the potential of AI, they’d traditionally need access to sophisticated toolsets, such as neural network frameworks like TensorFlow or Keras — complex, if free, libraries used for training machine learning models.
These toolsets require significant technical expertise on top of a solid software development foundation, which can make them seem inaccessible even for highly skilled developers who want to experiment with them on their own time.
While these toolsets may be interesting for academic purposes, they don’t provide practical value in most organizations today — unless you work at large tech firms like Facebook or Google where thousands of engineers are using these same frameworks across multiple projects throughout the day.
This need for AI-related skill sets creates an unnecessary barrier for non-technical teams when trying to understand the capabilities of different technologies and integrate them into their workflows, potentially leaving valuable insights undiscovered.
A New Approach to AI
Every business should have access to sophisticated machine learning technology; however, available tooling can often feel overwhelming since it requires a background in computer science or deep dive into complex documentation just to begin experimenting.
With no-code AI, the goal is to enable businesses to turn data into actionable insights through predictive analytics within minutes rather than weeks or months. Platforms can be designed from day one with end-to-end scalability in mind — from rapid deployment times all the way through plug-and-play integration.
Best of all: No-code AI keeps things intuitive enough even for non-technical talent (like product managers and salespeople) to add powerful machine learning capabilities directly into your marketing pipelines, CRM systems, customer service applications and more — instantly creating competitive advantages.
The Implications of Democratized AI
The shift to AI-first products and services has been hugely beneficial for consumers. Since its mainstreaming, AI has impacted many aspects of our lives, from recommending songs on Spotify to creating personalized entertainment experiences on Netflix to giving us the information we need on Google.
Now imagine if you could apply this same level of insight into your own business. With no-code AI, you can. My company has seen firsthand the tangible benefits that can be reaped when teams have access to advanced machine learning capabilities.
For example, customer support teams can predict which users will churn, allowing them to proactively anticipate issues and provide superior service. Sales teams can predict which leads will convert to optimize their sales funnels, further improving return on investment on every engagement. Finance teams can build complex models based on historical transactions and detect fraud, enabling them to take informed action before it becomes an issue.
Moreover, HR teams can use no-code AI to spot potential red flags and predict employee attrition, further improving performance management. And product managers can finally turn their ideas into reality by forecasting consumer demands, enabling them to know what will work and what won’t — all while saving valuable time and money in the process.
It’s no wonder that companies like Google, Facebook, Microsoft and Amazon have all made AI a key component of their innovation strategies.
I believe that the best AI projects are the ones where people aren’t afraid of messing things up or wasting valuable time reinventing processes. By democratizing access to machine learning capabilities across any team — no matter their skill level — more businesses will realize the power of AI.