Assessing Sales AI Maturity: Are You Ready?
One of the most commonly used sales buzzwords of the past few years has been artificial intelligence (AI). Everyone is talking about how AI can open up a whole new world of opportunities in sales, helping companies sell products with greater efficiency, close more deals, and grow revenue.
The vast majority of companies exist at level one. These companies are aware of AI and its benefits, but haven’t implemented it in any meaningful way. There’s no AI strategy for these businesses. All AI discussions are predominantly theoretical.
There’s nothing wrong with being in this stage. Everyone starts at level one. The issue is staying at level one and never moving further up the chart. That’s where you’re missing opportunities.
Companies in level two of the maturity model have moved beyond discussing AI in hypothetical ways and have started to test it out in their business.
At level two, companies are taking their first tentative steps into this strange new world. There is some low level implementation of AI technology into the business at this level, but it’s all still superficial.
If you’re at this stage, you’re on the right path, but there are still many benefits you’re missing.
By the time companies reach level three of the model, they’ve matured from dabbling in AI to actually using it.
Companies at stage three are using machine learning tools regularly in their day to day operations. They have machine learning and AI teams. They have processes in place to capture and analyze data to drive results.
Companies at level three have a distinct advantage over their competitors still working through levels one and two. They’re taking advantage of technology in a way their competition isn’t.
Depending on your industry, you could stop here and reap the benefits of your efforts. However, taking the next steps can take your from the minor leagues to the pros.
By the time companies reach level four, they’re using AI and machine learning in unique ways designed to disrupt the traditional business models of their industries.
By this stage, the company has made a full commitment to AI and machine learning. They not only have teams in place to spearhead their AI efforts, they also have infrastructure to support their initiatives.
When your company reaches level four, you have a significant advantage over the vast majority of companies out there in the wild.
By the time companies reach level five of the AI maturity model, they’ve transformed their business.
For these companies, AI and machine learning are no longer abstract concepts or novel tools. By the time a company reaches level five, AI is an integral part of their operations.
Think of companies like Netflix and YouTube, who leverage the power of AI to create complex algorithms designed to create unique viewing experiences. Consider Facebook, who uses these algorithms to increase engagement by putting the right content in your feed. Shop at Amazon? Then you’re familiar with how they use AI to suggest products you didn’t know you wanted.
Companies at level five aren’t just disrupting the market – they’re reinventing it.
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How Do You Use the AI Maturity Model?
Now that we know what the model is and how it breaks down, you might be wondering who you use this model and the information.
The obvious answer here is that you use the model to understand how you’re utilizing AI and how you could use it more effectively.
That being said, there’s more to it than that.
Companies shouldn’t rush to adopt AI just because it’s trendy. While almost every company can benefit from AI adoption, figuring out where you belong on the scale is important. Not everyone needs to reach level five.
The model notes how companies use AI at each level. Find the level where you can gain the most benefit and start building toward that goal.
What Can the AI Maturity Model Tell You?
At this point, you may be wondering what the real benefit of the AI maturity model is. Sure, it’s great to understand where you are on the scale and think about how you might move to the next level, but what’s the point?
Here are just a few of the things AI can help you with:
Discover anomalies and other issues
AI is great at analyzing huge chunks of data. Sometimes, it will find problems or anomalies you didn’t know existed. Is there a point in your sales pipeline where customers are dropping out? Is a piece of content not reaching the right audience? What factors determine when customer’s make a second purchases?
AI helps with all of this.
AI can suggest products or upsells and cross sell to customers. But unlike a human, it can do it based on analysis of past behaviors. This increases the likelihood of making sales.
These are things AI and machine learning can do for you. The AI maturity model shows you where you’d need to be in your AI program if you want to achieve these results.
To get some of these results, you could be at level three. For others, you’d be best served by going for level five. The model simply breaks down what you can expect from AI implementation at each stage.
Is Your Company Ready to Take AI to the Next Level?
One of the most common questions we get is “am I ready to take my AI initiatives to the next level? What do I need to know and do?”
The simple answer is unless you’re at level four or five, you’re ready to move forward.
If you want to see where you’re at, we have our own test to help gauge where you are and plan for what comes next.
3 Key Practices to Boost Your AI Maturity
By this point, we hope you’ve taken our AI maturity test to figure out where your company is currently. We also hope that you now have some aspirations for how to take your AI initiatives to the next level.
As we mentioned earlier, the vast majority of companies are down on level one on the Gartner scale. But even if you’re at level three or four, there’s still room to grow and boost your AI maturity.
Here are three best practices to help you move to the next level.
1. Assign Budget and Accountability
Developing and implementing an AI program for your company is not something that can be done overnight or in a week.
To fully implement AI and machine learning into your business takes time, effort, and planning. It will also take funding and vision, two steps that trip up many companies.
Businesses at the lower levels of the AI maturity chart often lack the vision and foresight to see the big picture with AI. They tie the project to financial concerns like return on investment. If the money doesn’t materialize quickly, many will stop funding the programs before they become successful.
In fact, companies that assign their AI projects to a corporate function are 3.2 times more likely to be at level four on the maturity scale. Let’s examine why this is.
Better strategic alignment with corporate vision
When companies look at AI in the broader scope of their corporate functions, they’re likely to have input from a wide range of company representatives.
By including people from disciplines outside the AI team, it’s easier to ensure that the AI project’s goals have ties to the company’s specific goals and vision beyond just revenue. These outside representatives can also provide practical insight into what the program’s focus needs to be in terms of application.
We’ll talk more about the value of having a diverse team of more than just data scientists later in this article.
Better financial focus
Rather than just having your data scientists selling you on compelling ideas that may or may not benefit your business, adding outside representatives can better provide practical feedback.
With this sort of insight, it’s easier to determine what objectives to fund. Rather than fund everything, you can focus on the AI projects with the best potential return on investment or benefit to the company. This will make it easier (and cost less money) to fund the AI program for the long term.
So, how do we measure the financial returns of the AI project? Here are some key ways:
- Narrower customer segmentation
- More successful process occurrences using automation with acceptable levels of accuracy
- Increased customer counts/customer interaction successes.
Companies who measure financial and risk-related results of their AI program tend to me more mature on the scale. They understand the end goal of implementing AI better than their less savvy competitors.
2. Create Trust Between AI Specialists and Executive Team
In many businesses, employees are relegated to “silos”. Marketing and sales work together, but each is their own department, and so on.
However, it’s in a company’s best interest to actually promote what we call “fusion teams” when it comes to AI initiatives.
These teams should be made up of your AI specialists and your business leaders. The value here is one we touched upon in the previous point: it creates a synergy that leads to better overall results.
By having the executive branch and the AI team working closely together, the AI team gains a better understanding of the practical needs of the business. They can also better see the challenges the company is facing. This allows them to focus on how their work can have an immediate impact on the health of the company.
On the other hand, the executive branch can learn what’s possible from their AI specialists. Your AI team is an authority on this topic, and they have a level of insight into things that the average employee can’t conceptualize.
By combining these two powerful forces, you can have the best of both worlds: a visionary AI program that aligns with your company’s real-world goals.
If you’re not sure how to make this happen, why not schedule a meeting with us? Take our maturity assessment above and our experts will be happy to discuss your results and show you how to move forward with your AI program.
3. Choose the Right Staff for Your AI Program
Our third step builds on the previous two.
At this stage, it’s important to build a team from a number of different disciplines. The question becomes who to include?
The best AI teams are filled with people from a wide range of disciplines beyond analytics and data science. While companies that have dedicated AI teams are more likely to find success and increase in maturity, just stocking those teams with data scientists isn’t the pathway to success.
As a general guideline, you should strive for an 80-20 split between data scientists and analytics team members and members from other disciplines.
It’s not enough to just take any employees from outside the data team and plug them into the mix. Here are some of the key areas outside of data that can benefit the team:
- User experience
- Business development
Beyond that, your team can also benefit from members from outside your organization if you can get them. Some people to consider include:
- Subject matter experts
- Outside AI experts
- Participants from competitive portals
The goal of adding all these diverse voices to your team is to create a more visionary group. Data scientists are great at seeing things from the data science perspective, but like with the executive team, adding more voices to the chorus is a better approach.
By adding team members from a wide range of different disciplines, you will get a better overall view of how to implement your AI program.
If you’re like most companies, you’re aware of AI and machine learning and understand they could help your sales efforts.
It’s great that you realize these things, but just realizing the value of these technologies isn’t enough. If you’re not actively implementing these programs into your business, you’re losing ground to your competitors.
The first step on the road to success is figuring out your overall AI maturity and then determining what your AI goals are. We’re here to help with that – through this article and through hands-on contact.
Once you figure out where you’re at, and where you want to go, life becomes much easier. Building a successful AI program will take time, money, and vision – but it can be done.
Following the best practices outlined in this article is a great way to get started. Building the right team and setting the right goals is a major stumbling block for many companies, but we can help you avoid the pitfalls and find success!
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