From Autopilot to AI Co-Pilots

Eight AI experts share their advice on how to prepare for an AI-driven future.

When Orville Wright took to the skies in the world’s first airplane, his hands never left the controls. Yaw, pitch, roll, and speed — every movement relied on his delicate manual input. It was taxing work: Every iota of the young aviator’s brainpower was focused on keeping his flimsy wooden airplane safely in the air.

Fast-forward a hundred years, and while today’s pilots can manually control airplanes, they don’t most of the time. Automation systems, specifically autopilots, now handle more than 90% of flight time, guiding the airplane through the climb, cruise, and descent.

But that’s not to say pilots are without jobs — instead, they’re free to focus on the bigger picture. Rather than fixating on every little movement, modern pilots devote their time and energy to thinking about all of the outside circumstances that impact the yaw, pitch, roll, and speed of the plane. They do things like communicate with air traffic control, monitor the weather, and check fuel, position, and time.

Autopilot radically transformed the aviation industry, empowering pilots to fly more smoothly, reliably, and accurately. Now, artificial intelligence (AI) is poised to do the same for people across all roles, functions, and sectors.

The launch of ChatGPT in late 2022 catalyzed both individuals’ and businesses’ interest in AI. Since then, millions of people have poured countless hours into thinking about, experimenting with, and investing in it. And it’s unlikely that this trend will change in the coming years: A recent IDC forecast found that, by 2027, the global AI software market is expected to reach up to $251 billion — and, separately, the generative AI market is expected to grow to $55.7 billion.

With this burgeoning AI market comes the rare opportunity for businesses to transform the way they work — to save time with automation, simplify complex workflows, and drive better outcomes. And, given how many businesses are already investing in AI, now is the time for go-to-market (GTM) leaders to carefully consider how AI can work for them so that they and their teams can stay relevant and thrive in the future.

That’s why we called on eight of the brightest AI thought leaders we know — some of whom have been watching the AI industry for years — to share their hottest takes, best tips, and recommended roadmaps to help you get on the runway with AI. By reading this book, you’ll learn about the history and predicted future of AI, its risks and benefits, and how to start leveraging AI today so you can become the pilot of your industry tomorrow.

So, please make sure your seat backs and tray tables are in their full upright position — because the AI revolution is about to take off ✈️

Now Is Your Opportunity to Embrace Generative AI | KATIE KING, CEO, AI IN BUSINESS

Everyone is a generative AI expert and every business is a generative AI leader — according to LinkedIn, anyways. But when you peel back the social media hype, the reality is more nuanced.

When assessed across the 10 dimensions of AI in Business’s Score Card for Success (such as culture, innovation, and talent) most organizations fall into the lowest two categories of AI adoption. This means that, in reality, the majority of companies are still novices in AI or just starting to explore what AI can do for them. Rarely are they exploiting the full benefits of AI at scale.

So, why is there a disconnect between hype and reality?

Here, it helps to consider the Gartner Hype Cycle — a graph used to assess the maturity, adoption, and implementation of new technologies. It charts the five hype phases that new innovations often go through, starting from a fresh idea to inflated expectations, and ultimately into productive use.

When the Hype Cycle is applied to generative AI, it’s clear where we actually stand. Ever since the launch of ChatGPT, most organizations have been in full experimentation mode, testing and trialing new tools while people’s expectations have steadily been mounting. You’ve probably read the same headlines: Why 95% of Salespeople Will Be Replaced by AI Within 20 Years. Forget Freelancers — ChatGPT Can Write Your Content. AI Chatbots Run a Software Development Company.

The truth is, generative AI can’t do all of that stuff — at least not yet.

When folks discover the limitations of AI, some will naively abandon their AI transformation programs. But for those who push through (hint: you should be one of them), the payoff will be immense. Because, even now, AI can do a lot. It can wipe out tedious manual work, drive predictions from complex data, and make humans faster, smarter, and more efficient.

By sticking it out with AI, you’ll be the first to drive gains from what it has to offer. As a result, when we do reach that plateau of productivity in the Hype Cycle, you’ll already be leagues ahead of the competition.

Today’s AI Is the Least Capable We’ll Have (and It’s Crazy Good) | PAUL ROETZER, Founder & CEO, MARKETING AI INSTITUTE

ChatGPT can pass medical, law, and business school exams. It can understand and explain a funny joke, invent brand-new languages, and write tear-jerking Shakespearean sonnets.

The ChatGPT we know today is flexible, powerful, and impressive. But give it a year, and it’ll likely feel as antiquated as a horse-drawn cart. If that seems unrealistic, just think back to GPT-1. In the span of a few years, OpenAI’s GPT went from responding in repetitive and sometimes incoherent paragraphs to writing full-on poetry. Similarly, you can literally see the same rapid evolution in Midjourney’s AI.

Midjourney’s evolution from February 2022 to May 2023.

These two examples point to the reality that AI is evolving at a breakneck pace. This is because tools like OpenAI’s GPT and Midjourney were built with AI at their core, which means they can learn from their data, adapt to it, and improve all on their own. As a result, we’re entering an exponential growth phase with AI.

The AI products we see today are simply the foundation of what is to come. And with improvements happening fast, it’s important that your team is improving alongside AI — otherwise, you’ll be stuck playing catch-up later on.

That’s why you need to start planning and implementing your AI roadmap today. To do this, ask yourself: What problem is our team facing now that might be solved with AI? Where in our process can we use AI to drive more efficiency? And where will AI be able to generate the most value? Once you have identified which use cases and problem statements you want to address, create an AI roadmap that charts out how you will bring AI into your organization. Aim for a timeline of about three to six months.

By creating this roadmap, you will have a concrete guide to help you shift your company into gear with AI. Keep in mind that you don’t have to overhaul everything about your business — even a little bit of AI can go a really long way in creating value.

The most important part of AI adoption is to simply get started. Since the businesses that move now will be the ones to reap the biggest benefits, you don’t want to wait for your industry to get smarter around you. So, get your roadmap in place and start driving progress with AI today.

Generative AI Is a UX Revolution | JAM KHAN, Former SVP of Product Marketing, 6SENSE

Think about the tasks that dominate your days as a revenue professional — toiling over a messy first draft, picking out target accounts from an endless list, or piecing together personalized outreach from scratch.

Now, think about what your day would look like if you could free up 60-70% of the time you spend on that work. Your schedule opens up. Your energy refreshes. Instead of drowning in monotonous work, you are free to focus on strategic initiatives like designing creative content or having conversations with prospects.

This is exactly what everyone was hyped about when generative AI first hit the mainstream. But what was truly revolutionary about the commercialization of it was that it turned the way we approach AI on its head — a UX revolution.

Whether it’s Google’s search algorithm or Netflix’s recommendation system, AI has long felt like a magic trick that happens behind the scenes: Though we could never really tell what was happening, we trusted that we would get the outcome we wanted. However, when it comes to generative AI like ChatGPT, that’s far from the case. Instead, generative AI puts you in the driver’s seat where you can influence, manage, and even control it.

At its core, what this UX revolution means is that you can’t treat generative AI like the AI that came before it. So, when it comes to how you engage with generative AI, the tools that you engage with, and how you use those capabilities on your team, you need to emphasize usefulness rather than just correctness. For that, you want to assess any generative AI you use on these three guiding principles:

  1. Reliability: Consider what principles are guiding the AI — be it factuality, relevance, brand tone, etc. — and whether the AI can check itself on these principles. This will help you decide whether you find that specific tool reliable or not.
  2. Explainability: Instead of treating generative AI like a black box, make sure that the tool you use can explain its decisions to you. By focusing on tools that can do this, you will be able to prevent yourself from getting carried away by the AI and making a misinformed decision as a result.
  3. Steerability: To be a true UX revolution, the user should always be in charge. So, choose AI tools that will allow you to direct the generative AI to behave in a way that fits your goals. The more control that you have over the AI, the better.

With generative AI, there are benefits to be had for the entire revenue team. Whether that’s crafting emails for high-intent prospects, instantly providing insights into a target account, or recommending solutions that will drive up sales effectiveness, generative AI tools have the potential to save time and drive efficiency.

But you will have nothing to gain unless you approach generative AI in the right way. This means always focusing on ensuring the reliability, explainability, and steerability of any generative AI tool you use — because that is how you will truly be able to put your team in the driver’s seat.

AI Can Boost Efficiency in All Parts of Your Business | DR. AYESHA KHANNA, Co-Founder & CEO, ADDO

While we throw around the idea that we’re currently living in an “AI revolution,” it’s actually more accurate to say that we’re experiencing three concurrent AI revolutions.

If you’ve so much as dabbled with any sort of GPT technology, then you’ve already experienced the first revolution: AI that makes humans more productive. Whether it’s providing prompt suggestions to break your writer’s block or translating chat conversations in real time, these AI agents help us humans work faster, smarter, and more efficiently.

The second revolution is a little more hidden: AI that makes machines more productive. For example, manufacturers use digital twins — digital reconstructions of real-world equipment like jet engines — to monitor the health of their equipment. By modeling wear and tear using AI, manufacturers can pinpoint when equipment does (and doesn’t) need to be fixed, saving millions of dollars on unnecessary routine maintenance.

But it’s the third revolution that people tend to overlook the most — and that is where AI makes AI more productive.

For the most part, AI tools operate in silos. An imaging system will buzz away in one corner, a chatbot in another, and a copy generator in a third — but these tools can’t communicate with each other. While you can manually piece together Frankenstein-like AI systems, the results are often inefficient, unproductive, and unreliable.

However, recently, it’s become easier to build these coordinated AI systems. By using natural language processing (NLP) to get AI tools to talk to each other, you can now chain AI tasks together so that a human can give a command to one AI assistant and then communicate it to the other AI tools behind the scenes.

What might this look like in practice? Say you’re a conversion specialist on a marketing team. You tell your AI agent, “Angela matches our ideal customer profile (ICP) perfectly. Wherever she goes, I want you to personalize our offerings to her.” From this command, the AI agent will coordinate with your generative AI tool to craft highly personalized copy, the social platform’s AI to optimize ad targeting, and your on-site AI chatbot to personalize the ad landing page.

While this is only a vision of what’s to come, this third AI revolution is not far off in the future. Soon, AI will be able to communicate with, collaborate with, and train each other, which means that humans will be able to execute strategies more easily and efficiently. When combined with AI for humans and AI for machines, these three AI revolutions will empower you to drive better results in every corner of your business.

Take a Flywheel Approach to AI Adoption | MATT TIPPETS, SVP of Product, Drift

The B2B world has already experienced dozens of disruptions: Cloud technology reimagined software delivery. Social media transformed how buyers interact with vendors. Mobile unchained buyers and sellers from their desks. And the internet…well, the internet changed everything.

With each of those disruptions, the most successful adopters followed the same continuous cycle: identify, experiment, implement, repeat. This adoption flywheel is what has empowered businesses to try out new technologies, find the right use cases, and scale on what works for them. And it’s the same cycle that we need to follow now with AI.

Currently, companies are scattered across the AI maturity spectrum: Some are still learning about what the technology can do while others are steadily expanding their use cases. No matter where you currently are in your implementation of AI though, the four steps of the flywheel can act as a guide to ensure you have the right use cases in place.

Think about it as approaching AI adoption like a scientist. The first three steps of the flywheel encourage you to craft hypotheses, rigorously test them, and then make decisions based on those results. Here’s a breakdown of each of these steps in the context of AI:

  1. Identify opportunities. Think of this as your exploration phase. Here, you want to look at all your business processes, products, and services to figure out where AI would fit into your business and where it would have the most impact.
  2. Run experiments. Don’t assume that the opportunities you’ve identified are the right or best ones. Instead, design and execute experiments that will test for specific outcomes to prove or disprove your hypothesis.
  3. Implement and scale. Once you have the results of those experiments, pick out the use cases that have had the most impact on your business. From there, simply implement and scale.

But remember, that’s not where the flywheel ends.

With AI developing at a breakneck speed, it’s only a matter of time before the AI tools you use now are superseded by new ones, your perfect use case is rendered imperfect, and your outstanding results are overshadowed by those of your competitors. Never assume that what worked yesterday will work tomorrow. That’s why the last and most important step of the flywheel is to continue identifying, experimenting, and implementing AI because it’s the repetition of going through this cycle that will truly make you able to succeed.

The 3 Ways to Break Out of AI Experimentation Land | MEGHAN KEANEY ANDERSON, Head of Marketing, JASPER

Seven out of every 10 change management initiatives fail. This is because organizations are naturally risk-averse and biased towards the status quo — and the bigger an organization is, the worse it gets. As a result, even the simplest of ideas can stall out.

When it comes to introducing an entirely new technology like AI — a tool that people are experimenting with at the individual level — that hesitance reaches an all-time high. That’s why, to convince your company to implement AI, you need to drive strategic and cultural change. Here are three steps to do exactly that:

1. Define Company-Wide Standards for AI Use

While individuals have the power to decide when and how they use a new AI tool, a company can’t make those decisions on the fly. That’s why you need to clearly outline standards that define how your company approaches AI. This means answering questions like: What security and privacy regulations do you want your AI tools to comply with? What is an acceptable (and unacceptable) AI use case? What measures will you put in place to ensure your output from the AI is up to quality?

Once you’ve agreed on these standards, make sure that you put everything down on paper, so it can serve as a source of truth for your company. In addition, you might want to set up an internal AI council to ensure that each team’s use of AI fits those standards, while also continuing to drive literacy on new AI. With these measures in place, you can set up guardrails that ensure your organization is leveraging AI responsibly.

2. Redesign Your Team’s Workflows

Although AI has already proven to save time for individuals everywhere, there’s no guarantee that it leads to better outcomes. After all, if the time you saved is reallocated to answering Slacks or scrolling LinkedIn, you won’t have made much of an impact on your bottom line.

That’s why, as you transition to working with AI, you should redesign your team’s workflows. For instance, if your team plans to use generative AI to create and repackage content, then you want to reallocate your team’s efforts towards coming up with original ideas that the AI can accelerate on. By building a workflow that integrates AI, you can ensure your human team and AI are working together to drive greater results.

3. Prioritize Quality Above All Else

Tools like generative AI have proven to boost a GTM team’s speed and output. But with many generative AI tools relying on content in the public domain, these results often miss the mark on accuracy, relevance, and value.

So, as you explore and expand your company’s AI use cases, prioritize the quality of your output. This might mean choosing a tool that is more grounded in your business context, using a content ingestion feature to educate the tool on your brand, or building processes that keep humans in the loop to ensure accuracy.

While experimentation land is a lot of fun, the ultimate goal with AI is to drive better results. The key to moving from individual to team acceleration isn’t so much technical knowledge as it is strategic change, and by following these three steps, you can set that change in motion, and in no time, be paving your way to successful AI transformation.

Predictive AI Is the Key to Unlocking Personalized Experiences | DON SIMPSON, CEO, LIFT AI

Your Netflix home page looks nothing like your friend’s — and that’s only partly due to your superior taste.

For years, Netflix has used machine learning to identify the shows and movies you like, uncover patterns in your preferences, and then deliver perfect recommendation after perfect recommendation. As a result, B2C companies like Netflix have set a new bar for personalized experiences — even among B2B buyers. Today, 66% of B2B buyers demand seamless and personalized experiences on the same level as those in the B2C space.

The problem is that personalizing for B2B buyers tends to be a taller order than for B2C buyers. This is because most B2B buyers are anonymous, with many often being reluctant to share their information. Their buying behavior is also more complex — what with the involvement of large buying committees and long approval processes. This makes it hard to understand what any given buyer is looking for, which in turn, makes personalization impossible.

And that was the case for a long time — until predictive AI hit the scene.

As companies like Netflix have proven, behavior doesn’t lie. By finding patterns in your buyers’ browsing behavior, you can gauge each individual buyer’s intent, as well as their needs and interests. And it just so happens that AI is the ultimate pattern detector. A predictive AI tool can watch how a buyer interacts with your website — what pages they visit, what content they download, or what services they check out — to then predict their intent with impressive accuracy. And that’s without knowing anything else.

With a predictive AI engine, you can just look at buyer behavior and personalize from there. For example, if someone’s browsing behavior suggests that they’re a high-intent marketing persona who is looking for a new marketing automation platform, you can roll out relevant case studies, send a custom message via a chatbot, or tweak the home page messaging to fit their interests. Suddenly, they’re not looking at a generic website, but rather a highly personalized landing page.

In this way, predictive AI allows you to capture demand from anonymous buyers on your website, instantly segment website visitors based on their level of intent, and create targeted experiences for dream buyers. All the while, each new visitor that lands on your website helps to improve the AI’s predictive model, boost its accuracy, and give you better results.

Ultimately, with predictive AI working behind the scenes, you can create a website experience that closes the gap in your conversion opportunities, brings together sales and marketing by ensuring you’re handing over all the best leads, and deliver a smoother, more enjoyable buyer experience — one that will truly live up to your buyers’ expectations.

Future AI Will Reinvent Every Team, Function, and Industry | DANILO MCGARRY, Top 20 in the World for AI

Elite sportspeople win or lose by razor-thin margins. When Usain Bolt won his final 100m Olympic gold medal, he won by eight milliseconds — just 0.8% faster than second place.

B2B businesses are no different — they have competitors, and they’re constantly racing to see who can come out on top. In congested industries, even the tiniest improvement in your product or GTM strategy can quickly snowball into market domination.

But the fact is that AI promises to improve your business by way more than just a slim margin.

It’s no exaggeration to say that, in today’s world, AI is the largest economic opportunity. According to McKinsey, generative AI alone is estimated to have a global economic impact between $2.6 trillion and $4.4 trillion annually. Plus, this impact is expected to touch every function of a business. The same report estimates that generative AI could potentially drive a productivity lift of 4% of global functional spending in sales, 10% in marketing, and 38% in customer operations.

While these numbers on their own are impressive, it’s even more shocking to think about the impact that AI in general will have on the global economy. This is especially true when you consider what sorts of AI might emerge in the near future. For instance, in the wake of generative AI, we’ve seen the development of narrow and super AI, which are super-specific AI systems designed to fit narrow applications in industries like medicine, logistics, and education.

With all of the developments we see in AI today, coupled with its estimated impact on the global economy, it’s clear to see that AI can change — and is already changing — how B2B businesses think, operate, and sell. What this means is that the companies who implement AI first will be the ones who, in the future, will be able to compete on price, speed, and accuracy.

However right now, many companies are still sitting back, studying their competitors, and seeing how their AI implementation will play out. But the reality is that you need to be the one your competitors are watching. Like Usain Bolt who won gold by being 1% faster, you too need to act now to get ahead of the competition.

So, if you’re a GTM leader, invest in education and training about AI for your team. Encourage them to be curious about AI tools and systems. And give them the skills they need to put AI to effective use. Because that is how you will be able to win the AI race.

Take Off with Your AI Co-pilot

Autopilot started as a way to solve the taxing and routine work required to fly an airplane. Over the years, through countless iterations and innovations, autopilot became what it is today — a must-have for every airplane flying in high-traffic airspace.

That’s precisely where AI is headed soon.

While right now AI still feels like a novelty, it’s soon going to be everywhere. From software and sales to finance and food service, AI will change our work and reimagine our jobs.

But while the opportunity is immense, it’s what you do now — how you think about, implement, and work alongside AI — that will determine whether your business stagnates or thrives.

So, seize the moment. Embrace changes in your skillsets, roles, and workflows. And, above all, be ready to transform your business — with AI sitting next to you in the cockpit.

Ready to Hit the Runway and Take Off Towards an AI Future?

Now that you know what the future holds, it’s time to start your journey with AI. Download our AI one-pager to help you get started on the right foot with AI.