Artificial intelligence (AI) improves business forecasting by analyzing vast quantities of data, spotting patterns and using predictive analytics to make accurate projections.
Small business owners apply the technology to predict customer demand or future revenue – helping them make informed decisions.
In this article, you’ll discover what AI means for forecasting, its benefits and how to get started. You’ll also learn how to overcome common AI forecasting challenges.
What is AI forecasting?
Business owners use AI forecasting to predict future outcomes or events. Machine learning algorithms excel at analyzing vast amounts of historical data and other variables to identify patterns and trends that human analysts may miss.
Imagine a fashion retailer that wants to predict demand for its summer collection. With AI-based forecasting, it can assess:
Historical sales data
Consumer buying habits
Social media trends
Weather patterns
If the model predicts exceptionally high demand, the store can invest heavily in new stock, confident that sales will increase.
Various industries leverage AI forecasting. Healthcare companies use it to estimate patient demand and outcomes. Financial services firms can predict market outcomes and make high-frequency trades to outperform traditional strategies.
Note: AI forecasting is different from generative AI. Forecasting models use data to predict events or trends, while generative AI produces new content – like blog posts, emails or videos – based on patterns learned from existing data.
AI forecasting vs. traditional forecasting
Previously, businesses made predictions using traditional methods like statistical modeling. AI-driven forecasting improves traditional forecasting techniques in several ways.
Here’s how the two forecasting methods compare:
Traditional forecasting | AI forecasting |
Can’t identify intricate data patterns. | Uncovers hidden patterns like seasonal trends and cyclical behaviors that are difficult for human analysts to detect. |
Struggles to adapt to new data. | Constantly updates and retrains when new data becomes available. |
Can only handle a couple of variables. | Handles multiple variables simultaneously, such as economic factors, market trends and customer behavior insights. |
Unable to forecast in real time. | Analyzes data in real time. |
Is limited to companies or individuals with specific knowledge and skills. | Is accessible to anyone through a one-off fee or a monthly subscription. |
Traditional forecasting happens slowly – a handful of technical experts generate insights from small datasets. AI forecasting occurs at scale, delivering more accurate, real-time insights to businesses of any size.
5 benefits of AI forecasting
AI-powered forecasting surpasses traditional methods, generating more reliable, nuanced and complex forecasts faster.
Here are five ways that AI improves forecasting for your business:
More accurate predictions – delivers more precise predictions by analyzing more data and uncovering patterns human analysts may overlook.
Better and faster decision-making – real-time AI data processing allows business owners to capitalize on emerging trends.
Lower costs – reduces unnecessary expenses by helping small and medium businesses predict demand accurately to optimize inventory levels.
Higher scalability – automates much of the prediction process, significantly improving efficiency. SMBs can focus on high-value tasks while the tool creates forecasts in the background.
Happier customers – increases customer satisfaction by ensuring sufficient product availability based on demand prediction. AI forecasting also anticipates future trends, helping businesses meet changing customer needs.
Ultimately, AI forecasting allows small businesses to compete with larger companies by creating fast and accurate forecasts without hiring a dedicated team.
How does AI forecasting work?
AI forecasting uses machine learning models that process vast amounts of data. Companies use various model types to forecast their data.
Here are some of the most popular AI forecasting techniques in data science:
Time series forecasting | A model that predicts future events or values using historical data collected at regular intervals. |
Regression-based models | A model that analyzes relationships between variables like website clicks, page views and conversions to predict future outcomes. |
AutoRegressive Integrated Moving Average (ARIMA) | A statistical data analysis model that computes future predictions by analyzing the past (autoregressive), making data stationary (integrated) and using previous predictions to improve future results (moving average). |
Long short-term memory (LSTM) | A type of recurrent deep learning neural network that retains information from previous sequences to predict future outcomes. |
The forecasting process stays the same regardless of the AI algorithms you use. It looks something like this:
Data collection. Gather relevant information from CRMs, marketing software, customer data platforms (CDPs) and third-party data sources.
Data processing. Clean and format data to prepare it for analysis. Remove duplicates, complete missing values and ensure data is error-free.
Model selection and training. Choose an AI forecasting model that aligns with your data and business or sales goals. Train the model using your prepared data.
Forecasting. Apply the model to new data to generate predictions about future events or outcomes.
Evaluation and adjustment – Measure the effectiveness of your model using relevant KPIs. Check for biases and incorrect predictions.
Companies can follow these steps to turn raw data into accurate, actionable forecasts for improved planning and performance.
4 ways SMBs can use AI forecasting to improve their business
Pipedrive’s The State of Sales and Marketing Report 2023/24 found that the uptake of AI technologies is higher among smaller companies.
42% of respondents in businesses with up to 10 employees reported using AI, compared to 23% of companies with 100+ staff.
Here are four ways small businesses can adopt AI forecasting technology:
1. Forecast future sales and find high-value leads
AI can analyze your company’s sales pipeline and historical sales data to predict how much revenue your salespeople will generate by year-end.
AI forecasting models can also analyze live sales data to predict which deals will most likely convert. With these insights, sales reps can prioritize their efforts and close more deals.
For instance, Pipedrive’s AI Sales Assistant evaluates deal, sales prospect and email data to pinpoint high-potential leads and suggest next steps.
The AI assistant might remind a sales rep to follow up with a high-value, high-priority lead – or recommend moving on from a deal that’s unlikely to convert.
Note: If the assistant suggests contacting high-value prospects, use Pipedrive’s AI email writer to craft a compelling, personalized message from a simple prompt. If you need a refresher on previous conversations, the tool’s AI email summarization feature digests long email threads to give you a concise recap.
AI forecasting also gives sales managers accurate, real-time revenue predictions. It allows them to adjust their sales strategy confidently to hit targets or make budgetary decisions.
2. Predict customer demand
Demand forecasting models analyze sales, customer behavior, seasonality and other data sources to predict product and service demand fluctuations.
They help business owners and managers optimize inventory levels and streamline production planning.
Imagine a tech e-commerce store preparing for Black Friday. Instead of relying on last year’s sales data alone, AI forecasting considers additional factors like economic shifts and competitor behavior.
The forecast predicts a dip in sales, helping the retailer maximize revenue without overstocking – likely inevitable without AI-powered predictions.
The seller can then use CRM data to support marketing efforts and grow sales opportunities.
With Pipedrive, businesses can turn forecasts into action. The CRM tracks real-time customer interactions for a complete view of the customer journey.
In Campaigns by Pipedrive, they can launch targeted email marketing campaigns using customer segmentation and custom filters. The feature also monitors performance metrics like open and click rates to help optimize engagement and conversions.
Together, AI forecasting and Pipedrive’s CRM tools allow businesses to anticipate and meet customer demand proactively.
3. Improve supply chain management
Manufacturers can use AI inventory management to forecast supply and demand levels, ensuring they have enough raw materials to supply customer orders.
For example, a solar energy installation company can use an AI model that predicts how many materials they need for production by analyzing factors like:
Buying trends
Customer demand
Economic indicators
Seasonality
It can double-check the forecast’s accuracy using Pipedrive’s product revenue forecast reporting function – which predicts expected revenue from different product lines.
To create the report, go to Pipedrive’s Insights feature and click “+ Add item > Report > Revenue forecast > Product revenue forecast”.
By forecasting how many products will sell and when orders will come in, the manufacturer can align their production and inventory strategies to avoid stockouts.
4. Analyze price changes
AI forecasting helps business owners predict the impact price rises will have on demand before deciding.
Imagine a SaaS company wanting to understand the price rise’s impact on subscription levels. It uses an AI forecasting tool to analyze sales data, customer purchasing habits and other market trends.
The AI tool simulates different pricing scenarios to predict how customers will respond. It finds that a 10% rise leads to 5% higher customer churn but an increase in revenue. However, upping prices by 20% results in 30% more churn and a significant revenue drop.
The SaaS brand uses the first scenario to optimize its pricing strategy, knowing that further increases will likely reduce operating income.
The company could later test the accuracy of predictions using AI. Pipedrive’s AI report generation tool lets customers create reports without technical knowledge or training using a simple prompt.
For example, an executive can ask, “How has the conversion rate changed over the last three months?” or “What’s our customer churn rate?” to understand whether 10% of customers left after the price increase.
The tool – part of the Insights feature – will use your input to generate a report. You can chat with the AI bot to request additional information or tell it to start over if the report isn’t helpful.
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How to start AI forecasting
It’s relatively straightforward to add AI forecasting into your small business workflow.
Follow these simple steps to start using AI forecasting successfully:
Get your data ready
AI tools need lots of high-quality, accurate data for analysis. The better your data, the more accurate your predictions.
Use the following strategies to assess your data readiness and get your data AI-ready:
Data accuracy and quality | Check data accurately reflects real-world scenarios. Find and fix errors, inconsistencies and discrepancies that could impact the reliability of AI models. |
Data completeness | Review datasets for missing values or gaps, like incomplete fields in your CRM, that can lead to flawed analyses and predictions. |
Data timeliness | Ensure data is up to date and relevant to current conditions to improve forecast outputs. |
Data consistency | Validate data is uniform across different sources and systems so models can process it efficiently. |
Measure data quality using dedicated tools like Decide AI. The software integrates with Pipedrive to spot duplicate entries, missing fields and errors.
Now is also a good time to establish data governance practices, highlighting how you store and protect your business information. Ensure you have policies defining data ownership and strong access controls preventing unauthorized use.
Define a use case
Choose a specific objective you want to achieve with AI forecasting. A clear use case will help you align with business goals and pick an appropriate tool.
Picture an IT sales organization struggling to predict pipeline value accurately. It can implement sales forecasting software to evaluate historical data, existing prospects and open deals to forecast future revenue.
Then, it sets a SMART sales goal to keep efforts on track. SMART goals are specific, measurable, attainable, relevant and time-bound.
For example, the IT sales executive looking to predict pipeline value might set the following SMART goal:
This implementation will give the executive confidence to decide budget, recruitment and sales targets.
Choose a suitable tool
There are plenty of AI forecasting solutions for small business owners. Some serve specific purposes, like optimizing warehouse inventory, while others are more general.
Find the best tool for your team by assessing the following qualities:
Usability | An easy-to-use tool will let your team use it quickly, accelerating time to value. |
Integrations | Tools that integrate with your tech stack facilitate real-time data transfers and improve data quality. |
Training | Extensive training materials, onboarding programs and resource centers will make the tool faster to adopt and operate. |
Pricing | Have a set budget to avoid overspending. Consider the long-term ROI of tools. |
Evaluate software usability and intuitiveness through sales demos and free trials. Check each tool’s website to find integration, training and pricing information.
If you already use Pipedrive, the platform’s AI-powered Marketplace will help you find forecasting tools that integrate with your sales CRM.
There are two methods you can use:
AI-powered Marketplace search. The search engine recognizes natural language queries, so you can quickly find tools without knowing the correct terminology. Simply type “What apps can I use to forecast revenue?” into the search bar.
SmartApps recommendations. The feature automatically suggests relevant software based on your company size, industry and apps that similar companies use.
In either scenario, you’ll find valuable tools that integrate with your CRM in a fraction of the time.
Integrate AI models with other data sources
Integrating AI software with the rest of your technology stack ensures data moves smoothly and automatically between each tool. Machine learning models will also have access to enough data to run accurate predictions.
Start with your existing tech setup. For example, integrating an AI forecasting system like Dear Lucy with Pipedrive’s sales technology will give your AI forecasting model access to:
Sales prospects and other contacts
Current and historical deals
Interactions between sales reps and prospects
It will help you predict revenue by salesperson, business area, country and product in real time.
Add external data sources like social media activity, economic analysis and weather forecasts to improve forecast accuracy further.
Integrations are particularly effective for businesses with limited historical data. They let business owners make forecasts as soon as they launch their startups.
Measure and refine
Make your forecasts as accurate as possible using ongoing performance measurement and refinement.
Start by assessing your forecast accuracy using the following strategies:
Compare your predictions to real-world data. For example, see how closely your forecasted quarterly revenue matches the actual amount sales reps brought in over three months.
Track relevant sales KPIs and performance metrics like average order value and email marketing conversion rate.
Ask your employees for feedback. Use surveys to determine whether predicted trends materialized and collect suggestions for improvement.
The above strategies help you learn how much faith to place in your predictions. They also promote transparency and uncover specific ways to improve your AI forecasting results.
AI forecasting challenges and how to overcome them
AI forecasting is powerful, although not perfect. Here’s how you can overcome some of the most common stumbling blocks:
Data quality
AI forecasting tools rely on lots of high-quality data to make predictions. Flawed, incomplete or inaccurate information leads to poor insights and predictions.
A great first step is removing data duplicates, correcting errors and reformatting entries before running forecasts.
Additionally, you can improve the quality and accuracy of all future data sources using the following three policies:
Build clear data governance frameworks. Define roles, responsibilities and processes for managing data quality across your organization.
Run regular data audits. Conduct periodic reviews to assess data accuracy, completeness and reliability. Alternatively, automate the process with a tool like Decide AI.
Implement user training. Educate employees about the importance of data quality and how their actions impact it. For example, encourage sales reps to complete every customer contact field with all the relevant details.
Model bias and staleness
Any forecast model, even AI ones, has the potential for bias. Without supervision, AI tools may learn from and repeat inaccurate forecasts.
The model’s training data can cause these issues. Ensure your model can access high-quality, relevant data from your tech stack and third-party sources. Using integrations to provide new data regularly will stop your model from becoming irrelevant.
The underlying model itself can be a problem. The solution is to choose a provider that regularly updates its models, patches software and releases new features.
Product announcement pages show how committed companies are to updating their software.
For example, Pipedrive – whose AI CRM recommends sales actions and generates sales reports to aid forecasting – runs a dedicated blog featuring regular software feature updates:
Every new AI functionality gets a dedicated blog post detailing how it works and how customers can use it.
Consider trialing alternative AI forecasting tools if your provider doesn’t release frequent updates.
Data security
AI forecasting tools can be targets for cybercriminals owing to the large volumes of sensitive data they hold. Data breaches can result in stolen data, reputational damage and financial loss.
Protect your data by choosing forecasting tools with the following features:
Feature | Description |
Data encryption | Data encryption during transmission and while in storage prevents anyone from reading sensitive information. |
Controls restrict access so only authorized personnel can access specific datasets, preventing misuse. | |
Compliance with data protection rules | Adherence to industry-specific regulations such as GDPR compliance and CCPA boosts protection and avoids legal penalties. |
Regular updates | Software patches protect forecasting tools from cyber threats and other risks. |
Ask about cybersecurity features during product demos. Alternatively, review the security features page on the tool’s website to understand how it can protect your data.
Final thoughts
Incorporating AI into forecasting efforts helps small businesses predict future trends with unprecedented accuracy. As a result, they can optimize operations to increase profitability and make faster, better data-backed decisions.
AI forecasting tools work better as part of a collaborative technology stack. Pipedrive’s AI-powered CRM integrates with other AI tools to centralize customer data and deliver deeper insights. Start your 14-day free trial to see how Pipedrive can help you predict demand and forecast revenue more intelligently.
