Here is a 2800+ word draft blog post on AI powered business forecasting tools:
Business forecasting is essential for planning and strategic decision making. Predicting future sales, demand, and growth allows companies to allocate budgets, manage resources, set goals and mitigate risks proactively.
However, traditional forecasting methods relying solely on historical data have limitations. Expert human judgment introduced biases. Data patterns were missed. Forecasts were made in silos.
Now with AI, businesses can automate and enhance predictive analytics to new levels. Advanced machine learning algorithms uncover hidden insights without human subjectivity. As a result, forecasts become more accurate, timely and holistic.
Read on as we explore the transformative power of AI-powered tools for business forecasting.
The AI Difference in Forecasting
While business forecasting has been around for decades, AI propels it to new heights. Traditional statistical models rely on assumptions that pasttrends will continue. But machine learning techniques like neural networks uncover non-linear relationships and patterns that econometric models miss.
As Mikael Henriksson, Head of Industry Solutions at SAS explains:
"The difference with AI is that statistical models presume to know the shape of patterns in data, whereas machine learning models discern the patterns on their own.”
Beyond better accuracy, AI enables continuous learning from updated datasets. Models instantly improve with new information instead of human tweaking. Integrating predictions across the organization also yields complete views.
According to IDC, 75% of commercial enterprise apps will use AI by 2025. The business forecasting software market already tops $7 billion. Leading the charge are machine learning and neural network breakthroughs.
Major Business Forecasting Types and Use Cases
While there are many forecasting categories, two crucial ones that AI enhances are:
Demand Forecasting
Predicting what customers will want and how much is critical for balancing supply. Without AI, matching production volumes, warehouse space, inventory levels and personnel to future demand is challenging. But machine learning provides guidance by processing countless data variables that impact buyer behavior.
Growth Forecasting
Business performance relies on accurately projecting growth. Leaders need visibility into potential revenue trajectories to plan investments and set stretch goals. However, swirling market forces make teasing out expansion opportunities difficult. AI parses volumes of external and internal data to model possibilities.
Beyond demand and growth, AI transforms a variety of forecasts across functions:
- Sales: Lead volume, conversion rates, booking velocity
- Marketing: Campaign response, content performance, channel mix
- Finance: Cash flow, equity/debt decisions, commodity hedging
- HR: Hiring needs, retention risks, skill gap analysis
- Operations: Infrastructure capacity, supply shocks, price forecasting
The use cases are endless. AI doesn’t replace human strategic thinking but enables it by providing insights at scale and speed impossible before.
Top 8 AI-Powered Forecasting Software Tools
Dozens of technology vendors now infuse AI into predictive analytics. Let’s explore 8 popular business forecasting software options with highlights of standout features:
1. Alteryx
Alteryx facilitates end-to-end automated forecasting powered by its proprietary “Suggested Modeling” tool. After connecting to data sources, the software auto-generates models tailored to the dataset. Users simply pick the best performer based on backtesting without coding.
Additional highlights include:
- Drag-and-drop workflow design
- Advanced regression, time series and machine learning algorithms
- Predictive performance analytics scoring
- Python and R integration
- On-premise or cloud deployment
Ideal for data analytics teams without data science expertise seeking quick, accurate forecasts.
2. SAS Business Forecasting
As a forecasting pioneer for 40+ years, SAS leverages its industry leading AI engine SAS® Viya® for reliable predictions. Users praise the flexible self-service interface to handle robust data inputs spanning CRM, ERP, PoS and external sources.
Other key features include:
- Automated identification of best-fit forecasting models
- Demand planning and optimization
- Predictions with confidence intervals
- Collaboration tools to gather insights
- Custom forecasting apps building
Trusted by finance and operations leaders given proven large enterprise scalability.
3. SAP Integrated Business Planning
As an ERP giant, SAP offers integrated planning with its cloud-based Continuous Insights solution. Machine learning trained on billions of data points powers demand forecasts, pricing simulations, R&D scenarios and supply shifts.
High-value components also incorporate:
- Unified metrics across lines of business in a dashboard
- Natural language query for ease of use
- Predictive enablement of third-party apps via APIs
- User guidance on decision implications
Ideal for existing SAP customers to extend platforms with instant insights.
4. IBM Cognos Analytics
Part of the vast IBM hybrid cloud portfolio, Cognos leverages Watson AI to help users predict business changes. Beyond automated forecasting, it generates explanatory reports detailing each prediction’s rationale.
Other vital features consist of:
- Conversational natural language interface
- External data integration from sources like Experian
- Progressive web app mobilizes insights
- Auto-generator tool creates models rapidly
- Open platform to add new data types
A flexible blueprint for large organizations early in the AI transformation journey across varied analytics use cases.
5. Qlik Sense Business
Qlik applies its Associative Engine known for finding overlooked connections in data to predictions. It examines not just obvious patterns but discoveries never before imagined across disparate sources. Augmented analytics automates much of the manual work for users while spotlighting what needs human attention.
Moreover:
- All-in-one self-contained environment avoids IT backlog
- Geoanalytics feature handles location-based data
- Apps customize role-based information needs
- Scales on any cloud platform for performance
Solopreneurs to enterprise users praise Qlik for democratizing data and speeding insights.
6. UiPath Predictive Analytics
New to the forecasting arena, RPA darling UiPath leverages its process automation expertise for accurate projections. It ingests loads of structured and unstructured data to serve predictions in a simple browser dashboard. The tool also compares predictions against actuals to continuously improve.
Vital attributes also consist of:
- Direct connection with UiPath process mining
- Low-code platform enables citizen development
- Automated modeling without data science experience
- Interoperability with numerous data sources
- On-premise installation or multi-cloud
UiPath customers benefit from integrated process analytics and ML acceleration.
7. C3.ai Suite
Founded by enterprise software veteran Tom Siebel, C3.ai infuses AI across the value chain. The suite provides prebuilt solutions for intelligent forecasting tailored to industries like manufacturing, oil and gas. Users praise transparent AI with understandable model logic.
Other notable features include:
- C3 AI CRM enhances sales predictions
- C3 AI Inventory Optimization boosts demand visibility
- Self-service to rapidly scale insights
- 200+ prebuilt machine learning algorithms
- Models exportable to AWS, Azure and Google Cloud
Industry leaders like Shell, AstraZeneca, and Koch Heavy adopt the C3 platform for reliable, scalable insights.
8. Palantir Foundry
Known for securing sensitive government data, Palantir focuses on operational forecasting. It creates high-fidelity models from extremely granular enterprise data. All information stays siloed to respective departments for security.
Additional security-conscious capabilities:
- Forecasting analytics isolated from model building
- Confidential collaborating across teams
- Fine-tuned access controls by user credentials
- Model explainability with audit trails
- Cloud-agnostic deployment in days
Heavily regulated industries like aerospace/defense find Palantir’s governance rigors appealing.
The list goes on with Oracle, Teradata, MicroStrategy, TIBCO and upstarts all embracing AI’s potency for forecasting. Assessing needs around data infrastructure, user enablement and deployment preferences aids matching the best solution.
Getting Started With AI-Powered Business Forecasting
Hopefully the examples above illustrate AI’s immense potential to enhance predictive analytics. While judging the slew of tools offering AI can feel overwhelming, a few best practices ensure forecasting success:
Clean, Connected Data
“Garbage in, garbage out” remains true — AI is only as smart as the data allowed. Ensure a sound data foundation with:
- Checking for completeness gaps, anomalies, inconsistencies
- Blending in external market data like economists‘ outlooks
- Structuring data relationships with ETL pipelines
- Keeping data current with continuous flows vs batch
- Sufficient history for algorithms to learn patterns
Holistic Focus
Instead of forecasting for isolated business units, adopt an integrated approach:
- Unify data from across departments vs silos: sales, finance, marketing, HR and operations
- Develop a centralized data portal with consistent governance
- Link model outputs cross-functionally to catch interactions
- Enable self-service access to insights to spur enterprise-wise planning
Human + Machine Combination
While machines crunch immense data volumes quickly, don’t remove human judgment completely:
- Review key statistical forecast drivers for relevance
- Sense check predictions against business intuition and domain expertise
- Let analysts handle anomalies unexplainable by algorithms
- Keep staff focused on higher value analysis augmented by AI
Continuous Improvement Culture
Extracting insights from data is a journey, not a destination:
- Measure forecast versus actual to assess model accuracy
- Refresh algorithms frequently with new data
- Tune conceptual modeling as market dynamics shift
- Learn best practices from the latest predictive innovations
- Make refinements responsively part of everyday workflow
While forecasting helps guide business strategy, successfully competing also requires understanding customers at a human level. Learn how leading companies infuse emotional intelligence across the organization with our featured guide here.
The Future of AI for Predictions
Leading research firm Gartner cites AI as a top strategic trend for business, noting that “by 2025, the number of employees with AI-enhanced skills will grow by five times.” Business forecasting offers a prime opportunity to develop technology proficiency and impact.
As neural networks grow more complex leveraging quantum computing on the horizon, their ability to process countless data variables for patterns will explode. While many current AI models focus on historical time-series projections, causal/explainable AI will tackle hypothetical what-if scenarios.
Cloud analytics lowers infrastructure barriers so companies of any size can exploit intelligent insights. Expect end-to-end forecasting platforms as a subscription service. Data quality/security, model governance and bias detection will also gain focus to ensure trusting AI decisions.
In the words of data science thought leader Bernard Marr, "The ultimate combination of human and machine intelligence will make commercial forecasting, planning and optimization more successful than we have ever seen." Harnessing AI today ushers in new sight for business growth potentials not seen before.
