RevOps, or Revenue Operations, is the framework companies use to align marketing, sales, and customer success teams around the customer lifecycle. It streamlines operations, improves knowledge sharing, and paves the way for data-driven growth.
Studies show that companies deploying RevOps grow revenue up to 3 times faster than their counterpart, and increase sales productivity by up to 20%. This leaves many marketing directors confused when their RevOps strategies don’t deliver the results they expected.
Unfortunately, just having a RevOps function in your business doesn’t guarantee success. Many organisations fail to implement RevOps effectively, meaning they miss out on the opportunities the right strategy can bring. One way some innovators are tackling this problem is with the use of AI and automation. The question is, where can AI really improve your RevOps strategy?
Can AI Improve RevOps Outcomes? The Opportunities
Leveraging AI and automation in RevOps makes sense. After all, one of the ways RevOps functions accelerate growth is by improving how companies use and access data. Unfortunately, data, when derived from multiple sources, can be difficult to assess and rationalise.
Artificial Intelligence solves this problem, helping businesses identify previously hidden opportunities and improve their forecasting capabilities. The right tools can also empower businesses to accomplish more in less time, boosting productivity and reducing bottlenecks. That’s one of the reasons 90% of companies have already made some form of investment in artificial intelligence.
Let’s take a closer look at just five typical RevOps issues AI can help you overcome.
1. Lack of Common Metrics
Aligning your employees around data starts with ensuring everyone is focusing on the same metrics, KPIs, and insights. If everyone in your team has a different focus, then it’s difficult to make significant changes to your workflows or cultivate measurable results.
Business leaders need to ask themselves which metric, when changed provides significant value to the business. If marketing teams double the number of prospects sent to sales, but the sales team doesn’t close any deals, then focusing on “lead generation” metrics doesn’t make sense.
Companies need to take a data-driven approach to pinpoint the metrics that have a significant impact on their growth. AI can help with this, by using advanced analytics to identify the key drivers of value throughout the business. It can help companies see how different metrics align with specific business outcomes, such as increased sales or improved retention.
With AI tools, companies can bridge the gap between the actions different team members take, and the outcomes that propel companies forward, leading teams to focus on the right metrics.
2. Manual and Fragmented Processes
Manual, fragmented processes are poison to effective RevOps strategies. Unfortunately, many companies are reluctant to embrace tools that can help them automate tasks and align teams. One study even found the medical industry could save around $25 billion just by automating processes.
Fortunately, there are numerous ways AI can help with this issue. Intelligent performance analytics and process monitoring systems can determine where fragmented and manual processes are causing delays, errors, and inefficiencies in your business.
They can pinpoint whether hand-offs from marketing to sales and customer success, authorisations, and financial due diligence processes, when done manually, are wasting critical time.
Then, AI tools can leverage natural language processing and machine learning to provide insights and suggest improvements. They can automate tasks that don’t need manual input, streamline workflows, and enhance overall productivity throughout your teams.
They can also help to ensure people, processes, and technologies stay aligned throughout the workplace. According to Forrester, this can improve revenue growth by 36%, and profitability by up to 28% in any business.
3. Poor Data Quality and Integration
As mentioned above, data is a core component of a successful RevOps strategy. It’s how you ensure you’re making the right decisions to drive business growth and track the outcomes of your efforts.
Unfortunately, managing large amounts of data, particularly in an ever-evolving digital workplace, isn’t easy. Often, databases can end up filled with incomplete, inaccurate, inconsistent, or even outdated insights. Sometimes, data can even end up trapped in silos, preventing cross-functional analysis and access.
Implementing RevOps strategies might help to align your data, but it won’t fix its errors. Fortunately, artificial intelligence can bridge the gaps. AI tools can use data cleansing, validation, and enrichment techniques to ensure the completeness and accuracy of data. For instance, solutions like Tableau use AI and machine learning to analyse and clean data sets.
AI-driven transformation tools can then enable data sharing and interoperability across systems and platforms, creating a comprehensive “single point of truth” for your organisation.
4. Low Adoption and Utilisation of Technology
Many RevOps strategies involve the use of comprehensive tools and resources from the digital landscape. These tools offer access to resources for revenue intelligence and forecasting, pipeline management, attribution and more. Each tool has the potential to significantly improve the alignment of your teams, and the value of your RevOps framework.
However, these resources can only deliver value if your employees are actually using them. While some of your staff members might be open to embracing new technologies, others will naturally avoid change if it means learning how to use new complicated systems.
AI can help with this too. AI assistants, built into tools like HubSpot Sales Hub, or Salesforce Sales Cloud, can streamline processes for employees, and make adapting to new technology easier. They can offer step-by-step training, coaching, and support to staff members in real-time. Plus, some solutions can even complete tasks for employees with nothing but a simple prompt.
AI tools can also offer companies insights into the adoption rates of new technologies, how frequently assets are used, and collect feedback from team members. This all paves the way for a more intelligent, data-driven approach to change management.
5. Lack of Innovation and Experimentation
It’s not just employees that can be resistant to change and evolution in the workplace. Many business leaders can suffer from the same issues. This is particularly true in today’s world, where companies are dealing with increased pressure from economic uncertainty.
It’s often easier to take an “if it’s broke don’t fix it” approach to growing your business than taking on the risks associated with innovation. This is particularly true when experimenting with new tools and processes is complex, time-consuming, or expensive.
The result is a change-resistant company culture, where organisations fail to try new ideas, test new hypotheses, or even learn from their failures. In an environment like this, even the best RevOps strategy can’t thrive. AI can address this problem by opening the door to more agile experimentation.
With AI-powered tools and platforms, companies can quickly prototype, test, and scale new solutions with simulation, scenario analysis and more. This facilitates consistent experimentation in the business landscape, reduces risk, and minimises stagnation.
Drawing RevOps Success from Failure
RevOps success is dependent not just on implementing the right framework, but ensuring you have the technologies, processes, and strategies in place to transform your business. Used correctly, RevOps is your chance to turbocharge customer satisfaction and increase your revenue stream.
However, it requires a careful, strategic approach, as well as access to the right tools. AI technologies could be the key to helping companies unlock the benefits of their RevOps strategy, by overcoming common issues and roadblocks.
If your RevOps efforts aren’t delivering results, it could be the perfect time to start exploring the potential of AI.