Everything Marketers Need to Understand About Enterprise AI

Key Takeaways

• For larger enterprises, the advantages enterprise AI offers are vital for remaining competitive in the long run.
• Enterprise AI is a holistic transformation of an organization, integrating AI into its operations to make it dramatically more efficient and effective.
• For marketing leaders, enterprise AI offers dramatic efficiency improvements that enable marketers and their leaders to focus more on high-impact work.
• Data shows that Agile teams are dramatically more effective at integrating AI into their work because they are more flexible, experimental, data-driven, and focused.
• Implementing enterprise AI begins with understanding strategic objectives before accessing technical, legal, and cultural readiness. Then any needed culture shifts can be worked on before running a pilot, scaling, and iterating.
• Agile ways of working offer proven ways to overcome many of the biggest enterprise AI implementation challenges.

At this point, the barrier to entry for basic AI tools is pretty low. Any individual marketer can start using a Large Language Model (LLM) like Claude or ChatGPT with virtually no training or prep. More and more marketing tools also have AI built in. The accessibility of all these forms of AI means the competitive advantage gained from any individual tool is always going to be limited.

Gaining a serious competitive advantage in today’s marketing landscape requires a far deeper integration of AI into your organization. In other words, enterprise AI is the real key to marketing efficiency, innovation, and competitiveness.

However, unlocking enterprise AI presents huge challenges. It’s no wonder our recent survey of hundreds of marketers around the world found just 17% had fully integrated AI into their processes. However, 93% at least wanted to do so, pointing to an enormous gap between what marketers want to achieve with AI and what they can.

Fortunately, that same survey holds key insights into what kinds of teams are able to successfully implement AI on a deeper level. So let’s dive into understanding enterprise AI and accessing its tremendous potential in your organization.

Understanding Enterprise AI

At its core, enterprise AI is a bit akin to something like an Agile transformation. It’s about deeply integrating a variety of AI tools into every aspect of how an organization functions. That means automating processes, but also leveraging data and algorithms to make complex decisions both alone and alongside humans.

In this way, enterprise AI transforms organizations on a level not seen since the widespread adoption of computers and their software. The ability to significantly improve the efficiency and effectiveness of everything from fraud detection to personalized user experiences is already having a major impact on competitiveness.

One area where enterprise AI is having a particularly large impact is in marketing. Here, standard AI implementation may simply be using an algorithm to analyze campaign data to find patterns or using an LLM to answer questions. Enterprise AI, by contrast, sees marketing departments combine data from all their various teams along with related functions like sales to make better decisions. Processes are automated across the entire marketing function and AI integration into those processes is inherent.

Shifting from more ad hoc uses of AI to full enterprise AI is a bit like the shift from marketers using word processing software to full CRMs. It’s an order-of-magnitude shift in terms of the ROI you can expect. It’s hardly surprising then that our annual survey found that full AI integration translated into dramatically better outcomes for marketers.

Enterprise AI Benefits for Marketing Leaders

Because enterprise AI is a holistic approach to integrating AI into nearly every aspect of marketing, its implementation has to begin with leaders. Without strong and active leadership support, attempts to implement enterprise AI are far more likely to fail. But what benefits should marketing leaders expect from their efforts?

Firstly, enterprise AI offers a way to significantly improve operations. By tracking key efficiency metrics, you can create performance data AI algorithms can analyze to identify areas for improvement. For example, the built-in AI tools within Asana can help flag the types of tasks that tend to create bottlenecks.

In other cases, AI can simply automate tasks entirely. This can mean taking tedious and time-consuming steps like data analysis and completing them in seconds instead of hours. In one example, analysts at JPMorgan Chase saved approximately 360,000 hours by using AI to interpret data instead of humans. Overall, one estimate by McKinsey noted that 60-70% of employee’s time today may eventually be automated by AI.

Even when processes aren’t fully automated, enterprise AI offers ways to augment the work marketers do. Even simple augmentations like using LLMs to help think through language for a campaign or help construct a persona can have significant impacts. That’s because these use cases don’t just make marketers more effective, they all create something even more valuable: time.

Agile marketing has long demonstrated that marketers can be dramatically more effective when they do the right work at the right time. This is shown by survey data demonstrating that teams with more autonomy who work on fewer high-value activities are far more likely to be successful. Enterprise AI unlocks more time and resources that can be focused on high-value-add areas.

Overall, for marketing leaders enterprise AI provides more tools, information, capabilities, and time to deliver more value where it will have the greatest impact.

Enterprise AI Implementation Strategies

The benefits of enterprise AI are clear, but actually unlocking them can be far more difficult. The kind of holistic transformation required can put immense strain on organizational cultures, resources, and leaders. So what steps can organizations take to minimize these costs?

Begin By Defining Strategic Objectives

As with any major organizational transformation, it’s absolutely vital to begin with strategic objectives in mind. So many decisions need to be made during the implementation of enterprise AI that lacking a clear north star can lead to chaos. One team can strive to optimize for time savings while another focuses on simply shortening the implementation stage. 

By contrast, when each and every player works towards the same goals, you get an implementation that’s more focused and effective. But how do you choose the right goals for such a major undertaking?

It starts with a simple question: what strategic goals will have the greatest impact on your organization? You probably already have some kind of quarterly or annual cadence for determining strategic priorities. It’s worth taking a day or two to gather leaders together and conduct something like a big room planning session to determine what you’d like enterprise AI to accomplish.

Assess Your Technical, Legal, and Cultural Readiness

A component of this early planning process that can’t be neglected is assessing your organization’s readiness to undertake an enterprise AI transformation. That includes technical considerations around data access and quality, cloud computing resources, etc., legal concerns around compliance, as well as cultural readiness.

On the technical side, it’s important to remain tool-agnostic at this stage. Determining right from the beginning precisely which AI tools your teams will use robs them of the crucial autonomy they will need to experiment, iterate, and figure out what tools are actually going to be most effective. So when looking at your technical needs, do so in a more general sense and not for a specific tool or use case.

Compliance or legal teams also need to be involved from the very beginning. Ideally, they will even attend those initial planning meetings to set strategic objectives. The reasoning is that the compliance issues around AI cannot be an afterthought. If teams are going to operate in an Agile way, testing and iterating AI tools and making dramatic changes to how they operate, they need to feel confident they aren’t creating legal problems.

Involving compliance experts early on makes it far easier to create this confidence. Their input throughout the entire enterprise AI implementation process can help balance their legal considerations with those of the teams themselves. This helps avoid going too far in either direction, ending up with substantial legal risks through reckless AI use or being too conservative and missing out on opportunities.

Culture also cannot be neglected. Trying to force enterprise AI on teams that are fundamentally conservative, siloed, adverse to change, etc. is likely to end in costly failure. This kind of AI implementation requires a tremendous amount of experimentation and creative problem-solving. The first step here is understanding your starting point. This can come through some combination surveys, interviews with marketers and their leaders, and analysis of current organizational structures and processes.

Build the Teams and Culture You Need

Once you have an understanding of your current internal culture, you need to make any necessary changes to lay the foundation for long-term enterprise AI success. This will usually require some degree of training and coaching. 

Balancing these properly is vital for success. Simply sending teams off to a training session for a few hours or even days is highly unlikely to make a strong and lasting impact on their culture. Instead, following the 70-20-10 principle, you should balance around 10% of a team’s time in structured learning with 20% working with coaches and mentors, and the remaining 70% actually applying these learnings.

This approach is far more successful at translating training and such into actual culture shifts. The goal here is to develop an Agile mindset, a shift not just in the practices and processes a team uses to operate but in how they fundamentally look at their work. That may sound like an extreme shift but so is the shift to enterprise AI. If you’re going to sustain such a fundamental change in how your organization operates, it’s going to need to be driven by a culture that readily embraces change.

But besides mindset and culture, team structure also plays a crucial role. For the kind of rapid iteration and experimentation that enterprise AI requires, cross-functional teams are ideal. This is when a single team contains all the people and skills required to accomplish its work. Not needing to constantly bring in outside individuals and teams to accomplish takes makes them far more nimble and efficient.

So it may be worth reorganizing teams to be cross-functional ahead of an enterprise AI push as well.

Start with a Pilot

Once you’ve laid all that groundwork, it’s time to dive straight in and begin transforming your organization with enterprise AI, right? Well, no. The reality is that no amount of planning is going to predict all the obstacles you will encounter. That’s why it pays to begin with a pilot.

Tasking one or two teams with implementing something akin to enterprise AI but on the team level enables you to begin with high-impact and low-risk use cases on a smaller scale to better understand your challenges. You can learn mistakes that could be catastrophic at the organizational level when they are far less serious and costly. 

Your teams can really embrace AI, knowing that it’s okay for them to make mistakes because their goal at the pilot stage is to do just that. Each learning they produce can then become an invaluable insight for ensuring the next enterprise AI implementation phase is successful.

Scale

Combining an appropriate degree of planning and foundation-laying with such a pilot will enable you to actually scale enterprise AI throughout your organization with confidence. You will have a culture ready to embrace AI, alongside a strong understanding of the legal, technical, and operational issues you can expect to encounter.

That is why this is the moment to really scale enterprise AI to encompass your entire organization. No doubt you will still encounter challenges (more on overcoming them in the next section) but further preparation after the pilot is likely to lead to fast diminishing returns.

Iterate and Improve

Even once you have successfully scaled enterprise AI throughout your organization, the process is absolutely not complete. In fact, it never is. Even if you somehow managed to deliver the absolute optimal implementation of AI in your organization, that setup isn’t going to remain optimal for long. With new AI tools and use cases being released daily, the only way to remain competitive and effective in the long run is through constant iteration and improvement.

Here that Agile mindset and culture comes into play. Agile teams are built around continuous improvement. Instead of waiting for ad hoc circumstances to prompt reviews of processes or ideas to improve them, Agile teams regularly pause to evaluate their work and brainstorm testable ideas for improvement.  

This is important because simply telling teams to evolve this way is unlikely to be successful. When teams view continuous improvement as an integral element of their day-to-day operations, they’re far better suited to adapt and evolve alongside AI.

Using Agility to Overcome Enterprise AI Implementation Challenges

While there will always be unknown and unexpected challenges that arise in any enterprise AI implementation, there are a few common ones you can prepare for. Fortunately, Agile ways of working can help.

Cultures That Readily Embrace Change

As mentioned, one of the biggest challenges most organizations run into when trying to implement enterprise AI is cultural. Most traditional teams and organizations simply are not ready for the pace and intensity of change and evolution required. This is evidenced by the data in our latest State of Agile Marketing Report which found that fully Agile teams were far more likely to fully integrate AI into their work.

In fact, the survey was unable to find a single non-Agile marketer who had fully integrated AI. The enterprise AI implementation laid out here should make it clear why. Agile teams are uniquely positioned to embrace precisely the rapid change, experimentation, and iteration that enterprise AI requires.

Agile Marketing Is Built on Continuous Improvement

Think about what counted as a cutting-edge use of AI two or even five years ago. Chances are it was something that already seems wildly out of date. This illustrates why the only way for enterprise AI to continue delivering value in the long run is through continuous iteration and improvement.

As mentioned above, Agile teams are built around continuous improvement. It’s not simply something they can do, it’s integral to how they function both in terms of their operational processes and their culture. When continuous improvement doesn’t need to be forced, that enables leaders to focus their attention elsewhere. They can focus more time and energy on supporting their teams instead of telling them they need to review and improve their processes.

Agile Is Data-Driven

There’s a mind-boggling amount of hype around things like enterprise AI. But if you’re really going to unlock its benefits, you need to see beyond that hype and use data to demonstrate clear ROI. 

It’s easy to simply assume that a particular AI tool or use case is providing a benefit, but without precise data to quantify that benefit, you’re at a major disadvantage. This makes it difficult to compare tools and use cases, understand how they’re affecting your organization, or even determine whether it’s worth paying for a particular tool.

But experimental design isn’t something most teams are used to. Poorly structured experiments can be extremely costly, as single mistakes can lead to weeks of delays in obtaining quality data you can use for critical decisions. When you start with Agile teams that are used to running such data-driven experiments, it’s far easier to avoid such problems when implementing enterprise AI.

Enterprise AI FAQs

What is enterprise AI?

Enterprise AI is a holistic integration of AI technologies into nearly every element of a business or organization. Beyond simply using AI tools for single use cases, enterprise AI uses it at scale to help the entire organization operate more efficiently.

What is the difference between enterprise AI and generative AI?

While generative AI involves using AI tools to create things like images or text, enterprise AI is a holistic integration of AI into nearly every aspect of an organization's operations. So an enterprise AI application will likely use generative AI, it will also use many other types of AI to enhance the organization’s ability to operate.

What is the best enterprise AI?

Generally, enterprise AI operates on large-scale cloud platforms provided by companies like Google, IBM, and Oracle. The best option for a particular organization will always vary in part because these enterprise AI offerings are not off-the-shelf products. The sales process will involve some negotiation, so it’s worth speaking to many providers to find the best option.

How much does enterprise AI cost?

Because enterprise AI solutions are not one-size-fits-all off-the-shelf kinds of solutions, their pricing tends to be custom. That said, depending on the scale of the enterprise such AI solutions and the IT infrastructure they run on can easily run into the 6 figures annually.

Next Steps for Embracing Enterprise AI

It should be clear by now just how complex the process of preparing for and executing enterprise AI is. While elements like technical requirements can be fairly straightforward, the culture shift required is far more complex. However, the scale of the coaching and training required to get an entire organization worth of teams ready can be daunting.

That’s why we created AgileSherpas Edge. It’s a new way to gain access to thousands of hours of training and expert coaching in a way that’s affordable, flexible, and scalable. If you’re ready to embrace Agile ways of working and unlock the full potential of enterprise AI, check it out and see how you can take the first steps today.