Key Takeaways
- AI transformation is a holistic process of integrating AI into the operations and culture of an organization, bringing substantial competitive advantages.
- Agile ways of working are crucial for AI transformations as they enable the experimentation, prioritization, and continuous improvement that drive them.
- AI transformation roadmaps begin with strategic alignment and leadership investments before progressing to pilot programs designed to sort through legal and technical issues. Only then does full scaling begin.
- When implementing AI transformations, it’s vital to have clear goals and well-supported teams. At the same time, change management and compliance can’t be ignored.
AI is on the minds of enterprise marketers and leaders all over the world for good reason. It’s already showing itself to be perhaps the most transformative technology since the advent of digital marketing. But actually adopting AI is far easier said than done, evidenced by how much enterprises are struggling.
Cultural backlash, unfocused AI integration efforts, endless compliance issues, and more are preventing the full AI transformation most enterprises urgently need to remain competitive. That’s why it’s worth taking a step back to really understand what an AI transformation entails and the common pitfalls you need to avoid.
Understanding AI Transformation
AI transformation is the process of integrating AI tools deeply into every aspect of your business’ marketing. Rather than simply using AI tools here and there, it’s more of a total rethink of how your marketing operates with AI in mind.
But an AI transformation doesn’t stop there. Because AI technology is evolving so rapidly and new tools are being released every day, AI transformations also need to be built on a foundation of continuous evolution and improvement. After all, even the most advanced AI-integrated marketing function isn’t going to remain at the cutting edge for long if it’s not willing to change.
AI Transformation and Agility
One aspect of AI transformation that doesn’t get as much attention is its relationship with Agile ways of working. A big reason so many enterprises struggle with AI transformation is because they have neglected the Agile foundation that makes it possible.
How can we be so confident about the importance of Agile for AI transformation? We have the data to prove it.
Our latest State of Agile Marketing Report surveyed hundreds of marketers around the world and found that while 36% of fully Agile marketers had fully integrated AI into their processes, we couldn’t find a single non-Agile marketer who could say the same. Even the level of Agility had a major impact, with fully Agile marketers being more than 3x more likely to find AI success compared to somewhat Agile ones.
Behind this data is the reality that the experimentation, iteration, and dynamism required for a successful AI transformation are integral to how Agile teams already operate. Traditional teams struggle to manage the amount of change required: not simply adopting a new tool but integrating radically new technology into nearly everything marketing does.
Data from the report pointed to the importance of autonomy, experimentation, and focusing on fewer high-value activities. When looking at hundreds or even thousands of different AI tools and use cases, all of those are essential to identifying what’s worth doing and figuring out how best to implement it.
The Imperative for Marketing Leaders
AI-powered marketing is not just hype. It’s actually driving substantial ROI for marketing teams right now. That same report mentioned earlier found that extremely successful marketers were 8x more likely to have fully integrated AI into their work compared to somewhat successful ones. But besides overall success, how is AI really moving the needle for marketers?
Marketing strategies are a good example because AI is being used at nearly every stage of the strategy development and execution process. Large Language Models (LLMs) enable marketers to brainstorm and bounce around ideas extremely effectively and quickly. For example, you might ask an LLM to react to ideas from the perspectives of various types of customers. Alternatively, you might use tools like Hubspot AI to identify weak spots in your customer pipeline.
Once you have a strategic plan, AI tools enable you to analyze data far more efficiently. That might mean a literal dataset or something like sentiment analysis based on thousands of emails, comments, etc. Those data insights can then be filtered back into your strategic planning process.
That’s just the tip of the iceberg though. AI tools like chatbots can enhance customer engagement, enable personalization at scale, streamline the content production process, automate campaign management, and so much more. New use cases are being developed all the time, so what’s important is to understand just how comprehensively AI transformations can integrate these tools into your processes.
Building an AI Transformation Roadmap
Embarking on a full AI transformation is no small task. Such a large undertaking requires time, resources, and careful planning. That’s why you should always begin with a solid roadmap.
That said, you don’t want to fall into the trap of wasting time building a super-detailed plan that’s going to become irrelevant within days or weeks. So plan, but remain flexible and adaptive as you go as well.
1. Build Strategic Alignment
Whenever you’re planning a major shift in how your business operates, like an AI transformation, strategic alignment is essential. The last thing you want to do is invest the time, energy, and resources needed only to realize later that you and your stakeholders have totally different visions for what the end result should be.
So your AI transformation should begin with gathering stakeholders together and discussing what you’d like your transformation to achieve. Once you’ve come to a consensus, ensure you’ve really built alignment, not just tacit acceptance, around those goals. Only after you know what you’re trying to achieve should you progress to the next steps.
2. Invest in Strong Leadership
We’ve seen for years that leadership can make or break any major transformation. That applies to the agility that drives successful AI transformations as well. Leaders need to understand the role they will play in an AI transformation and that this role will likely differ from what they’re used to.
First is a shift in leadership style. We mentioned how important autonomy was for successful AI implementation and that autonomy can only really come from leaders. They need to shift from a more traditional command and control style of leadership to servant leadership. Here, their role is to look for ways to support teams rather than controlling them.
For AI transformation, the autonomy that it creates is vital. It’s what enables teams to freely test, experiment, and dig down to really understand how AI can best be used in various situations. That’s never going to happen if leaders simply tell teams what tools to use and how.
Lastly, leaders need to be ready to serve as the connection points between the strategic vision of an AI transformation and the realities on the ground. They need to help everyone understand that AI transformations will take time and are never truly “finished.”
3. Begin with a Pilot Program
As tempting as it can be to dive straight into your AI transformation, that creates some serious risks. Anything that complex is going to bring up problems you never saw coming. That’s why it makes sense to begin with a smaller-scale pilot program. Essentially, you’re starting with an MVP.
The benefits of this approach are considerable. A pilot enables you to learn hard lessons while minimizing risk. For example, you might find that you experience more cultural backlash against your AI transformation, necessitating more investment in training before scaling the effort. Or you may learn about some specific compliance issues you need to resolve.
The point is that you can’t know what you’ll learn from a pilot. What you can know is that learning those lessons via a small pilot will be far less expensive and disruptive than learning them at the scale of an enterprise organization.
4. Sort out Technical and Legal Questions
While this step does come after the pilot, ideally the two should happen in parallel. One of the main goals of your AI pilot should be to sort through technical and legal issues that may arise.
Your legal or compliance teams can use that pilot to gradually familiarize themselves with the legal or regulatory constraints your AI transformation presents. But it’s also a chance for them to better understand what the marketers themselves need from the technology. This mutual understanding can help avoid a more adversarial relationship between marketers and compliance teams.
Then there’s the question of technical requirements. Your pilot should be looking at how well various tools integrate with one another and with existing tools like your CRM. You may need to invest in data infrastructure, greater cloud computing, specific APIs, etc. This is your chance to really understand what investments will be needed for a scaled AI transformation.
5. Scale with a Focus on High-Impact Use Cases
Once you’ve identified your strategic priorities, built up the necessary leadership capabilities, run a pilot, and identified the technical and legal obstacles you’ll need to overcome it’s time to scale. However, that doesn’t mean you should begin rolling out your AI transformation everywhere all at once, as that can easily overwhelm internal resources like an AI or Agile center of excellence that may be supporting your AI transformation.
Instead, it’s best to focus on a smaller number of high-impact areas first. Much in the same way focusing on fewer high-impact activities drives successful Agile teams, that principle applies just as much to entire organizations. By the time you’re this far along, you should have a better idea of where those high-impact areas will be as well. So scale there first and use the resulting learnings to help scale AI to other areas.
6. Continuously Improve
One of the biggest mistakes organizations make when undertaking Agile, digital, or AI transformations is getting to a good point and thinking the process is complete. A true transformation is never done. The entire point is not to make some changes so your organization can achieve some kind of perfect state. Instead, it’s to create an organization that can use AI to continuously adapt and improve.
This is another reason why Agile ways of working are so important. They help create cultures that embrace experimentation and continuous improvement. So instead of having to find ways to force teams to run experiments and find ways to improve, that simply comes naturally. But in any case, this fact should be clearly communicated and emphasized throughout the AI transformation process. True that transformation will move through stages, but there will be no “completion” and return to the old status quo.
Overcoming AI Transformation Challenges
Even if you closely follow the steps laid out above, there are still some major challenges you’ll need to overcome to have a successful AI transformation. Here are a few key ones to look out for based on our experience with AI implementation and leading Agile transformations.
Implementing an AI Transformation Without Clear Goals
There’s a reason strategic alignment is the first step listed above, embarking on an AI transformation without clear goals is like trying to build an airplane in mid-air. There are thousands of different AI tools and nearly infinite use cases. The only way any enterprise can effectively navigate all those choices is with goals that are widely understood by everyone involved.
Neglecting Compliance
One of the unfortunate realities of any AI transformation is that it presents a host of complex compliance issues. From how internal data is used to customer data regulations like GDPR, there are a lot of rules enterprises need to follow.
That’s why it’s so important to bring in legal or compliance teams early and integrate them into your AI transformation process. This gives them a greater perspective on what the organization is trying to achieve as well as helps ensure their advice is followed early instead of coming in late to blow up existing processes.
Not Supporting Your Teams Sufficiently
Even the best-run AI transformations are disruptive and difficult for teams. They’re being asked to fundamentally rethink how they operate, integrating complex and new technologies while navigating difficult compliance questions. It’s a lot to handle.
In response, leaders need to really keep the needs of their teams top of mind. That might mean something like Agile training or AI-specific coaching. Often it just means being mindful of stress and work levels, shielding teams from unnecessary distractions so they can focus on the work that matters without suffering burnout.
Neglecting Change Management
Another way to frame the role of senior leaders in an AI transformation is around change management. It involves aligning around a purpose, empowering people, and creating feedback loops for continuous improvement. But besides those steps, it’s about shifting your mental approach to AI transformation to get beyond superficial changes. Successful transformations are transformative on a deeper level.
Leaders need to be aware that their role is not to get into the trenches to force change to happen. Their role is to set a direction, empower their teams, and work at a high level to manage the change that needs to happen. Many leaders struggle with this servant leadership style, so it may be worth investing in training there before beginning your AI transformation.
AI Transformation FAQs
What is AI transformation?
AI transformation is a process of holistically reshaping an organization around AI, integrating the technology into processes and mindsets. This transformation embraces change and adaptation, marking a cultural shift rather than simply a process one.
What is meant by transformative AI?
Transformative AI refers to the capabilities of AI to fundamentally transform societies. AI transformations within enterprises are one example, with AI tools being integrated fully into every aspect of the organization’s operations.
What Is Digital Transformation in AI?
This refers to the process of using AI tools to undertake a wider digital transformation of your organization: shifting how it functions with an emphasis on rapid evolution, deep technological integration, and the use of digital technologies.
Taking Your First Step Toward AI Transformation
What should be clear by now is that to be successful, AI transformation needs to begin with the right mindset. From senior leaders needing to embrace servant leadership down to team members needing to embrace experimentation, culture is a make-or-break element.
If you’re ready to begin investing in an AI-ready culture or want to learn more about AI transformations, it’s important to speak with people who have extensive experience with digital transformations. After all, each transformation is unique and built around the unique circumstances of the organization, so experience applying the principles laid out here is invaluable.
Oh, and before you leave, why don't you take a second to get our latest State of Agile Marketing Report and explore why Agile marketers are 3x more likely to be successful in integrating AI into their processes?
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