Marketing is only helpful when it’s meeting a need. It sounds simple, but those needs can be really tough to parse. Like any consumer, my needs evolve every day, if not every minute. I won’t stand for poorly targeted ads or messages that are irrelevant to me.

I work in marketing technology, and this industry has been talking about data-driven personalization for years. We’ve made a lot of progress, but we’re only just beginning to realize the potential of machine learning to match goods and services with a particular person in a specific situation.

Machines are changing how marketing is done. I’m not just talking about workflow automation or customer service bots. I’m talking about software that can help brands understand, meet, and even predict the subtlest of consumer needs.

It’s a new phase that I think of as Marketing 3.0. The 1.0 version, marketing in its early 20th century form, involved selling products to people who had demonstrated a need. The 1950s saw the rise of Marketing 2.0: ad men who shaped consumer desires to sell products. Machine learning allows marketers to move beyond this model and return to the original purpose of marketing, while adding speed and scale.

Marketing 1.0: Meeting needs as expressed
Marketing 2.0: Creating needs, then meeting them
Marketing 3.0: Machines analyzing needs, then meeting them

Marketing 3.0 uses machine learning to match product and consumer faster, more precisely, and in the right context; and to identify people who have an implied rather than overtly demonstrated need. Machines learn from a large pool of real-world examples, so they can predict future intent by observing past behavior. Marketers don’t have to comprehend the precise patterns that emerge from massive amounts of data or map out the rules that determine people’s behaviors.

In other words, machine learning shifts the role of the marketer from trying to manipulate customers’ needs to meeting the needs they actually have at a given moment.

Think about a BMW dealership looking to sell more of a particular model. They can use machine learning to identify indicators for people who bought a 5 Series in the past year: They researched similar cars like the Audi A6 and Mercedes E Class, they asked about mileage per gallon, and they had similar demographic traits.

Say I’m looking to buy a car and have a friend who recently bought a 5 Series. I’ve read about one of its new features: a 3D view of the car that I can see from my phone. When I search for “BMW 5 Series” on my iPhone, I’ll see a list of dealerships within a 10-mile radius of my regular commute. I call the dealership to ask about their inventory, and they know I’m ready to buy. I’m automatically matched with the sales rep who sold the same car to my friend, knows the specs I’m interested in, and can talk to me about 3D view.

I see massive opportunity to use predictive capabilities to link online and offline interactions — mobile ads, email campaigns, phone conversations, and in-person experiences. It’s becoming a reality as Google, Facebook, Apple, and Amazon continue investing in voice assistants and natural language processing technologies. Amazon is reportedly updating Alexa to be more emotionally intelligent. It’s not a huge leap to transition from making voice commands in my living room to calling a business and making a purchase directly through my Echo. A conversation is the most natural form of interaction, and the most conducive to forming relationships.

I think voice will be central to how marketers balance machine learning capabilities with the need to create human experiences. Even if machines can surface information and recommendations at exactly the right time, people still want human conversations, especially when it comes to buying complex or expensive products. I’m fine with Alexa ordering me a pizza, but not a car.

As I see it, the role of machines is to draw correlations between consumers’ behaviors and their ultimate intent. The role of the marketer is to figure out what can be automated (e.g., triggering an email after a purchase is made) and what can be augmented (e.g., predicting what products will most intrigue a customer) by using software. The next wave, Marketing 4.0, will take this a step further by meeting consumers’ expressed and unexpressed needs.

We’re moving toward a more predictive world in which machine learning powers the majority of interactions between consumers and brands. I don’t see this being at odds with human connection or authentic experiences. Marketing will be ambient and truly data-driven. It will catch up with consumer expectations and with the potential of technology to change how marketing is done.

Colin Kelley is CTO and cofounder of Invoca, the call intelligence company. In addition to 25 years’ experience in communications technology and call intelligence, Colin founded and co-wrote Gnuplot, an open source data visualization tool distributed with the Gnu project that remains widely in use today at universities and research organizations worldwide. Follow him on Twitter: @colindkelley.