The difference between true AI and automated data entry pretending to be AI
Between 2015 and 2019, the percentage of U.S. companies using artificial intelligence (AI) in some capacity increased by 270%. In most industries, AI is hiding just below the surface, driving enterprise software applications in everything from finance to HR and sales forecasting. Big business has taken note, too. Fortune Business Insights estimates that the market for AI tech will reach nearly $267 billion by 2027.
So why aren’t we seeing the effects of truly intelligent machines in our daily lives yet?
Getting a practical answer to that question requires a look at what is technically classified as AI and what technologies have the capabilities to function more like the human brain. AI shows up on so many features lists that people have started to almost tune it out, classing all AI together. In the sales industry, AI features seem to proliferate. But most of the sales tech that’s billed as AI-driven doesn’t function the way true AI does. It’s what might be better billed as “AI-lite,” a kind of proto-intelligence that is really good at simplifying repetitive tasks like data entry, but helpless at more advanced actions.
The most advanced AI technology available today leverages deep learning and neural networks that can learn and adapt in ever-changing environments to provide future-focused data and insights. Read on to learn about the differences between types of AI, examples of true AI being used by industry leaders, and why true AI in sales forecasting is set to leave other tools in the dust sooner than you may think.
AI-lite: Recognizing patterns
At its core, what we’re referring to as “AI-lite” makes use of a process called machine learning to “teach” an application to perform tasks — like speech or image recognition — based on analyzing patterns in data. This complex process involves human developers training an application to perform a specific task by feeding it structured, pre-processed data through algorithms. However, because an estimated 80-90% of an organization’s data is unstructured, this type of classical machine learning relies on quite a bit of manpower.
For example, developers might provide an application with hundreds of processed pieces of information regarding the sales of a series of products to help it “learn” about the sales lifecycle. Once the CRM is trained by developers, sales teams could enter information specific to their own sales to determine what product is selling best or how long it takes, on average, to close a deal.
All this results in tools that can deftly recognize patterns and even automate simple tasks, freeing up time for sales staff to actually sell. However, there are limitations to what technologies trained in machine learning can provide. For example, sales that fall outside the “taught” pattern will be missed — even if those sales provide a fuller glimpse into future market trends.
Furthermore, these tools often focus on one company’s historical data set, forcing businesses to focus on the past and present rather than look ahead or consider factors outside the business that might impact a sale — like buyer budgets, market fluctuations, or competitor actions. AI-lite CRM tools, limited by one company’s sales data, can’t make accurate forecasts.
True AI: Built to work like the human brain
More advanced AI-driven technologies function similar to the human brain, and can actually adapt to new data as it comes in to change course and provide better outcomes. The most modern AI algorithms allow applications to ingest and process large, unstructured datasets without the need for much human intervention — something the applications utilizing classical machine learning described above couldn’t come close to. Collective[i]’s technology, for instance, can identify the most relevant information and offer practical recommendations to sellers based on a broad network of data.
There are several algorithms and technologies that true AI relies on. Some of the most common include:
Neural network: A subset of machine learning that identifies underlying relationships in datasets through a top-down approach that mimics the human brain. This allows applications to recognize patterns, solve problems, and train data to learn and improve over time.
Deep learning: A subset of machine learning that uses algorithms to train applications to perform tasks by exposing neural networks to vast amounts of unprocessed data. Think machine learning done by a neural network on a much larger scale.
True AI results in more reliable information for decision making because of its ability to problem solve and learn as it ingests new information. In sales, this means that true AI-driven CRMs can offer real-time insights and predictions about next and best actions based on current buyer behavior and market trends.
Consider the kind of personalized buying experience that Amazon offers. For consumers, it’s a neat trick when Amazon makes spot-on recommendations for future purchases based on their buying history — one that accounts for 35% of their purchases. But the heavy lifting behind the scenes is no trick. It’s all due to neural networks.
Using data from customer preference and purchases, search histories, and patterns built around related items, Amazon used machine learning to build a system that can create personalized lists of products for customers. For example, a user may search for “adidas mens pants.” The neural network algorithm that Amazon developed to predict aliases might identify “running” as the most relevant category and provide additional recommendations to the user to entice more purchases. But it doesn’t stop there. Further up the supply chain, Amazon uses these predictions to ensure products are available at nearby Amazon warehouses for speedy delivery to the user. That, in turn, keeps consumers coming back for more.
Not all AI is created equal
CRMs training classical machine learning technology on only their historical sales activity can help automate data entry but cannot provide businesses with anywhere close to the level of insight on buyer behavior that a broad neural network can provide. It can save sellers time, and that’s certainly worth something, but it still keeps sales teams focused on the past when they’re trying to predict the future. Collective[i]’s deep learning tools offer sellers more than just fancy pattern recognition. Our intelligent set of algorithms synthesize broad data sets to produce insights that can help businesses make more informed decisions about sales, revenue activities, hiring, and inventory. As selling teams rely on the Collective[i] technology, it gets faster, more flexible, and more intelligent, leaving sellers and managers with more time to focus on activities that humans do best: communicating and building relationships.
That’s the idea behind Collective[i]’s Intelligent InsightsTM, which builds on our platform’s daily sales forecasts to provide sellers with a clear look at their next best steps to drive revenue. Our solution is what true AI looks like in practice.
See how leveraging AI can help your sellers win deals today. Explore Collective[i].