Bridging the Forecasting Gap: A New Solution to Enterprise Forecasting
Predicting business performance has always been hard. But now it's even harder. From global events like COVID-19 disruptions and rapidly shifting customer preferences to organizational challenges like siloed teams, disconnected processes, and inefficient spreadsheet-based workflows, modern forecasting is more complex than ever. It's clear—companies need new forecasting solutions that work for their business planning processes.
Yet, many companies still use old-fashioned forecasting methods. Different teams use separate data and make predictions based mostly on last year's events. The result? Inconsistent forecasts that miss real market changes and important new trends.
Even for companies making significant investments to improve their planning and forecasting capabilities, the journey isn't simple—it requires building a sound data foundation, adopting advanced analytics, and creating cross-functional collaborative processes.
Fortunately, AI-powered solutions are elevating the forecasting process. These solutions use AI and real-time market data to give companies a much clearer picture of what's driving their numbers. Whether it's social media, weather, or new customer habits, these tools track the market signals that matter.
This article looks at how new forecasting solutions are improving business planning. From breaking down silos to using real-time data, these tools make advanced forecasting available across an organization—no need for a data science degree.
The Forecasting Problem in Modern Organizations
Most companies face significant forecasting challenges that go beyond just accuracy. Different teams operate in silos, using their own methods and data sources. Strategy teams develop five-year plans, while finance focuses on quarterly numbers and supply chain plans for next month's inventory. Each function not only uses different timeframes but also different approaches to creating their forecasts.
This siloed approach creates major communication barriers. Marketing and sales teams struggle to keep their plans in sync, constantly adjusting trade strategies that require ongoing coordination. Global companies face an even bigger challenge: they must align dozens of local teams and blend their different forecasting approaches into a single, coherent planning picture.
Another issue is how companies handle data. Leading companies tap into 15-20 different data sources for demand planning, but most organizations can't effectively use their information. They miss critical market signals that could improve their forecasts, relying on disconnected methods that limit their ability to make smart, strategic decisions. This fragmented approach leaves companies guessing instead of understanding the true drivers of their business performance.
Many companies also ignore outside signals like market trends or unexpected events. Recent years have delivered wave after wave of disruption. The COVID-19 pandemic dramatically shifted consumer behavior, followed by post-COVID consumption volatility, widespread supply chain challenges, extraordinary inflation, and major geopolitical conflicts.
Now, with a new US administration, companies face likely changes in tariffs with pricing and trade implications, as well as significant policy shifts and continuing geopolitical uncertainty. These disruptions have proven that simple forecasts based on past growth patterns are no longer sufficient.
Effective forecasting requires a close connection to planning decisions. Teams need visibility into what's driving their numbers to identify risks and opportunities in their business. They should be able to explore different scenarios to understand how various decisions would impact business outcomes. But most companies lack the tools to evaluate these possibilities.
Understanding Modern Forecasting Needs
The industry has long recognized integrated business planning (IBP) as an essential approach to forecasting—the idea that all organizational forecasts should align and work together. IBP methodologies and supporting tools are constantly evolving. In parallel, advanced planning systems (APS) have emerged to tackle certain aspects of planning with more sophisticated technology and analytics. However, both of these approaches primarily excel at addressing granular supply chain issues—producing detailed volume forecasts and supply plans.
As a result, most planning solutions still lack the macro-level forecasts essential for finance and sales planning, such as market and account-level category forecasts and brand sales projections. These largely remain manual efforts and are often disconnected from the more granular supply-level forecasts.
New product forecasting presents another significant challenge—very few companies have consistent, statistically-sound analytical approaches for predicting new product performance. Instead, new product forecasts typically consist of marketing plugs that tend to be overly optimistic.
Modern forecasting requires more advanced analysis and modeling techniques. Forecasts must consider many interconnected factors like economic shifts, weather, climate change, inflation, customer spending, and changing customer preferences. These factors influence and amplify each other in complex ways that only advanced causal modeling can untangle.
But good modeling isn't enough. Companies need access to the right external data and information to feed these models. They need AI systems that can automatically generate and update forecasts as new data comes in and improve model accuracy as business results are realized. Most importantly, they need tools to make sense of all this complexity.
A New Approach to Commercial Planning and Forecasting
Traditional commercial forecasting tools have hit their limits. Spreadsheets—even sophisticated ones—are no longer enough. Commercial planning requires advanced tools that incorporate AI/ML analytics, provide critical planning features like forecast transparency and scenario planning, account for execution drivers and external signals, and enable appropriate human inputs within an AI collaboration framework.
However, advanced forecasting technology is only part of the solution. A major challenge lies in the planning process requirement for intense human cross-functional collaboration. Forecasts are only as good as their inputs and assumptions—and these inputs must come from throughout the organization: sales teams need to provide changing promotional plans across accounts, marketing must communicate shifts in advertising spend, and manufacturing needs to relay potential supply constraints.
The Cloverpop Decision Intelligence Platform delivers a comprehensive planning and forecasting solution that addresses both the technological and collaborative aspects of commercial forecasting. The platform's four key forecasting components work together to transform how organizations approach planning.
1) Leading Analytics
Cloverpop's driver-based models incorporate both internal and external category drivers to ensure accuracy and reliability. The platform pulls data from third-party sources and open-source information to give teams the full picture.
Take skincare products: the platform tracks TikTok activity, Instagram searches, and influencer content about specific products or ingredients. These real-time signals keep forecasts current and relevant, not stuck in historical patterns.
2) AI + Human Collaboration
Cloverpop uses AI throughout the entire forecasting process while keeping human expertise at the core. The D-Sight AI engine can automate data collection, process multiple data sources, and generate initial forecasts, but it critically involves human experts at key decision points.
Teams can input their strategic context, challenge model assumptions, and simulate different scenarios. For instance, users can tweak pricing, adjust marketing strategies, or explore new product concepts, while the AI can provide real-time insights and recommendations informed by both historical data and current market signals.
This approach shifts forecasting from a manual, time-consuming process to a dynamic, collaborative tool that combines the computational power of AI with the nuanced understanding of experienced business leaders.
3) Critical Planning Features
To improve planning, companies need clear ways to understand their forecasts, and this platform delivers three key tools.
First, the system breaks down growth predictions, or “decomposes” them, into easy-to-understand components. Instead of just saying a business will grow 5%, it explains exactly why—maybe 2% comes from changing prices and 3% from selling more products. Each part of the forecast gets a clear explanation.
Second, the platform lets teams play "what-if" games with their predictions. Managers can ask questions like "What would happen if we raised our prices?" or "How might our competitors react?" This helps teams see different possible futures before making big decisions.
Third, the platform tracks how accurate its predictions are over time. This means teams can see how well their forecasts match real results, helping them get better at predicting future performance.
4) Integrated Collaboration Layer
What truly sets Cloverpop apart is its integrated collaboration platform. Instead of teams working in silos, everyone accesses a single source of truth. The platform enables seamless in-platform collection of inputs from across the organization, recording changes and automatically connecting these inputs to forecast models.
This collaboration layer is critical during forecast review and cross-functional alignment meetings. The platform captures inputs from all parties, creates transparency, and maintains detailed records of scenario changes, assumptions, and human overrides. Once an organization reaches consensus on the final forecast, all information that went into the plan remains recorded and transparent—creating accountability and providing valuable context for future planning cycles.
This approach is especially valuable for large organizations managing multiple brands and markets that need to reconcile forecasts from teams spread across different locations and functions.
What's more, the platform works for everyone, from data scientists to business users who require user-friendly interfaces. By accommodating both, Cloverpop makes sophisticated forecasting accessible regardless of technical background.
Measuring Cloverpop Forecasting ROI
Cloverpop delivers meaningful improvements that significantly enhance business performance. Companies implementing this advanced commercial forecasting solution experience remarkable efficiency gains—achieving 100%+ increases in forecasting efficiency by streamlining collaboration and automating time-consuming tasks.
The platform delivers 20%-40% higher forecast accuracy through its sophisticated AI models and incorporation of external signals. Perhaps most importantly for today's rapidly changing business environment, organizations using Cloverpop are 5x more likely to make faster decisions, enabling them to respond more effectively to volatile market shifts and emerging opportunities.
These aren't abstract promises. They represent concrete performance improvements that can fundamentally change how organizations approach planning and decision-making.
Implementation Challenges
Adding a new AI forecasting system isn't as simple as pushing a button. Companies often run into problems when they try to change how teams work together. Employee training is crucial because many workers feel nervous about new tools that change the way they've always done their jobs.
There is also anxiety around AI in particular. Employees worry that AI might take their jobs, which can create doubt and resistance across the company. To overcome these fears, companies need to clearly explain how AI actually helps workers do their jobs better, not replace them.
But clear communication isn't enough. Getting support from top management is make-or-break for these new technologies. Without clear support from executives, expensive new systems can become nothing more than costly sideshows. Leaders must actively champion the new approach and show how AI can help teams unlock higher productivity.
Building trust in AI takes time and openness. Teams must understand not just the results but also how those results are created. This means creating transparent AI systems that can explain their recommendations, showing the step-by-step reasoning behind each forecast. When people can see how the AI thinks, they're more likely to trust and use it.
The most successful companies tackle these challenges carefully and thoughtfully. They understand that this new technology is really about people, not just computers and software.
The Future of Forecasting Solutions
Generative AI and agentic support will soon make forecasting even more accessible and actionable. In the near future, intelligent systems will analyze thousands of forecast lines to identify key drivers and detect negative trends before they surface.
These early warning tools will flag potential risks early and provide clear, plain-language explanations, enabling entire teams—not just data scientists—to understand and act on forecasts with confidence.
Cloverpop is actively developing its product forecasting and collaboration features to align with this vision. By focusing on both accuracy and usability, we aim to equip teams with the collaborative tools and insights they need to address risks, uncover opportunities, and make smarter decisions in an ever-changing market.
Forecasting solutions: Wrapping up
Better forecasting is essential for effective business planning. Companies can't rely on outdated processes or simple historical projections. The answer is combining advanced modeling, real-time data, and true collaboration to build forecasts that actually work.
But here's the reality: even the most sophisticated models won't help if they're not part of a complete solution. Companies need platforms that bring teams together, pull in pertinent data, and make complex analysis clear for everyone. When these pieces come together, organizations can finally get what they've always wanted—forecasts that align across teams and drive better decisions.
Cloverpop delivers on all fronts. It offers advanced modeling, clear explanations, and true collaboration in an interface that teams want to use. Ready to see better forecasting in action?