A.I. mania — it’s a thing. Global spending on artificial intelligence is projected to double from in the next few years, fetching more than $110 billion by 2024. If you’re...
A.I. mania — it’s a thing.
Global spending on artificial intelligence is projected to double from in the next few years, fetching more than $110 billion by 2024. If you’re an established enterprise, there’s a six in 10 chance your business has already implemented AI in some form. And if you’re a startup raising funds, just having an AI claim helps you rake in as much as 50% more.
Clearly, there’s tremendous value and buzz around the topic. But at the same time, there’s lots of confusion on what AI really is from one use case to the next. “AI” has become a trendy catch-all term for lots of loosely relevant digital technologies.
In the evolving field of B2B revenue technology, for example, many Account Engagement Platforms (AEPs) define themselves as AI-“powered,” “backed,” or “supported.”
But look under the hood and you’ll see that account-based RevTech solutions are not created equally. Some simply bolt on AI elements here or there, whereas others are all in with AI.
Knowing the difference changes the paradigm for customer-minded revenue teams. When an AEP’s AI is smarter and stronger, a revenue team doesn’t just gain “That would have taken me forever” shortcuts in its efforts to enhance buyer experiences. It receives “I would have never thought of that” advice.
Put another way: If we overlook impactful deep-data science because we assume it’s another buzzy-but-shallow doodad — or because we’re afraid it’ll take our jobs — we turn our backs on creating transformational buyer experiences at scale.
So it’s time to set the record straight. This article takes a look at:
First things first — let’s distinguish differing definitions of “Artificial Intelligence.”
Most of the AEPs touting AI today feature computational math, statistics, and automation. These code-based shortcuts improve upon the limitations of our slower, more error-prone human brains. They quickly reach answers to questions that are somewhat time-consuming but nonetheless involve relatively few variables. Some examples of Computational AI include:
In other situations, AEPs use graduated AI to vet, score, and prioritize data as a more foundational input.
These more powerful platforms read significantly more rows of data to, for example, better segment buyers. The findings they collect are based on a synchronized examination of hundreds of data points. However, at the end of the day, human coders are still required to evaluate the findings and take actions based on them.
Some of these next-level examples might include:
Then there’s the new future of AEP solutions, which is having AI at the core of the platform. Here, the AI isn’t just capable of data crunching — it self-improves with machine learning when encountering more patterns of information.
Even better, it contributes entirely new insights and advice in the quest for better buying experiences and revenue gains. Rather than simply being relegated as inputs (Computer: sort out all of this info so I can make customer decisions), it makes actual recommendations and decisions (Computer: tell me the two most helpful actions to take to win over the accounts now in the Decision stage).
AI-centered Account Engagement is hallmarked by:
For example, 6sense’s platform codifies audience targets into predictable buying stages and calls out insights relevant to specific accounts. It’s then ready to recommend — if not autonomously execute — account-friendly tactics based on layers of predictive filtering.
These features radically enhance a revenue team’s effectiveness, efficiency, and credibility.
As you explore AEP different solutions, how can you spot one AI type from another?
A telltale sign of an AEP without AI at its core is the presence of “if this, then that” rules. In a rule-based Artificial Intelligence model, human coders establish a set of conditions and corresponding reactions. When a buyer fits a profile and exhibits certain behaviors, they trigger a specified response.
The problem is that these rules, while often elaborate, aren’t easily modified. Buyer dynamics and behaviors change all the time. When this happens in a rules scheme, revenue teams have no other recourse but to constantly rewrite their orchestrations.
Imagine you’re a B2B software company with a solution helping HR and compliance professionals manage health outbreaks. In late 2019, you might have elected to offer a demo of your latest offering to:
However, as COVID-19 emerged and unfolded, these criteria would have constantly changed. Suddenly, you need to:
It becomes a constant chore to devise better rules and mobilize accordingly.
Conversely, solutions with deep AI throw these old rules out the window. They simply ask revenue teams to set goals. (How can I be highly selective and effective in offering our software demo to enterprise businesses likely to be valuable early adopters?)
Then they get to work determining how they’re best accomplished, adapting along the way and uncovering insights that humans likely miss due to our limitations and biases.
Of course, any time a discussion about business AI gets very far, worries surface that the machines are coming for our jobs.
We needn’t panic. There are still plenty of crucial decisions to make — and invaluable human-driven creativity required — to generate success, including:
In truth, AI actually enables revenue team members to be more effective — specifically to communicate to buyers at a level of precision and detail they could never get to before.
Modern machines can steer sellers and buyers in the routes most likely to result in deals. But within each of these lanes there exist untapped opportunities to drill into buyer data and create levels of personalization previously deemed inscrutable.
With deeper data, marketing can generate buy-in for creative concepts that may be otherwise met with skepticism. Sales can justify new territories. Leadership can put energy into identifying and exploring entirely new markets.
No matter the job function, data can eliminate debates and prevent delays.
It’s easy to develop software that’s a bit better than we’re accustomed to, slap AI to the name, and call it a day.
However, this behavior just makes all AI systems, including world-class RevTech platforms, confusing and inaccessible to buyers.
The beauty of high-IQ AI is its simple ability to facilitate faster and better decisions — not calculations. By leveraging this resource for all it’s worth, revenue teams gain fresh, deeply informed perspectives that can revolutionize buyer experiences and pack their pipelines.