How Artificial Intelligence Can Improve Forecasting and more

Introduction

Companies increasingly rely on historical data to predict the future. However, relying solely on historical data leaves many opportunities for improvement unfulfilled. Artificial intelligence (AI) can help you identify these opportunities and improve your forecasting capabilities. In this article, we'll look at why using AI is so powerful in RevOps, what specific types of AI are being used today and how they're applied in RevOps.

Artificial intelligence is gaining acceptance in the RevOps space.

Artificial intelligence is gaining acceptance in the RevOps space. AI has already been used to improve forecasting and decision making, and it is also being used to cleanse data.

AI is being used to automate insights and forecasts

AI can help companies make better decisions by automating insights and forecasts. For example, it can be used to generate accurate predictions of future demand based on historical data, so that a company can allocate resources appropriately. It can also help companies predict product variations or new products that might match their customers’ needs better than existing ones do—and then identify those customers who are likely to buy them when they become available for sale online or in stores later this year!

Why is it important?

You might be wondering, “Why is it important for me to use AI in my RevOps process?” Well, there are many reasons. First and foremost, AI can help you predict future sales and revenue. It can also help you predict costs, customer behavior, risks, opportunities and trends in the marketplace (and just about anything else.)

AI is an excellent tool for decision making because it uses historical data to make predictions that are often more accurate than humans could make on their own. Finally—and perhaps most importantly—AI allows us to visualize our data in new ways that we weren’t able to before.

How to use AI and where to get started

AI is a tool that can be used to automate insights and forecasts. It is not, however, a panacea. But it can be used to automate some processes.

AI will never replace human judgement—but why not use all the tools available to you? Here’s how you get started:

1. Identify the key data sources that will be used for forecasting.

In order to build accurate predictive models, you'll need to identify all the relevant data sources that can help in forecasting. This includes all historical data, as well as information on new products and projects. You'll also need to integrate this data so it's available for use in building your model.

There are several steps involved in getting started with AI/machine learning algorithms:

• Cleanse the data: Identify and correct errors in your source files before integrating them into one place where they can be used for building predictive models (e.g., Excel spreadsheet).
• Integrate the sources of data: Put all relevant sources together, ensuring that each source is consistent with another and has similar formats (e.g., column headers are all spelled correctly).

2. Integrate the data from these sources using an AI tool or platform.

Once you've collected the data, it's time to integrate it into your existing analytics tools. This is where AI comes in.

AI platforms such as IBM Watson Analytics, Microsoft Azure Machine Learning Studio, or Amazon Web Services' SageMaker can help you cleanse and use all of this information more effectively. They can identify errors such as duplicates or inconsistencies in the data and help organize it so that you don't have to do so manually. They're also great at providing predictions based on what they've learned from analyzing previous data sets—a step that would be extremely time-consuming if done by hand!

3. Use AI to cleanse the data, identifying and correcting errors such as duplicates or inconsistencies.

One of the most important steps in using AI is to cleanse the data, identifying and correcting errors such as duplicates or inconsistencies. Inaccurate or incomplete data can prevent you from drawing meaningful insights from your RevOps results, so it’s critical that you have a rigorous process for cleansing your data before applying it to an AI model.

Common issues with RevOps data include:

• Duplicate records: When multiple records for the same vehicle are entered into one platform, this causes problems when trying to compare performance across models and makes it difficult to identify trends over time.
• Inconsistent units: It’s important that all types of information stored within an organization—such as mileage values—are stored consistently across teams so they can be compared effectively against each other. This also helps ensure that metrics are comparable between different regions or markets where vehicles may vary slightly in terms of efficiency factors like fuel economy regulations or driving habits in certain areas vs others (e..g., rural vs urban driving conditions).

4. Build predictive models using AI, machine learning algorithms, natural language processing, etc.

AI can be used to build predictive models and improve forecasting, data cleansing, error detection, and insights generation.

The first step in using AI for automating insights and forecasts is to identify which functions are amenable to automation. These are often processes that rely on historical data or repetitive tasks that require little analytical skill from the person performing them. Examples include:

• Forecasting demand for products or services based on past sales volume trends
• Generating new product ideas based on existing product specifications (or vice versa)
• Calculating employee headcount based on expected workloads

Next, you should decide which machine learning algorithm(s) will be most appropriate for each task. The best algorithms will depend upon the size and complexity of your problem space as well as any constraints imposed by limited computing power available to run complex calculations at scale.

5. Use insights and forecasts to drive decisions and actions across RevOps functions with a connected platform linking all functions together.

In order to form a complete picture of the operations management process and any bottlenecks or issues that may exist, you will need to integrate data from a variety of sources. This includes your ERP system, but it also includes other systems such as CRM, SCM and HR. You can use an AI tool or platform to cleanse this data, identifying and correcting errors such as duplicates or inconsistencies in data fields. You can then build predictive models using AI algorithms that analyze historical trends and make recommendations for how best to improve performance going forward.

Conclusion

In the end, it’s all about driving sustainable growth and increasing profitability. AI can help you do that in a way that no other technology has before. It will connect your teams, provide insights at scale and give you access to data from any source—all without human intervention.