Beyond ERP and CRM: How to Implement Intelligent Acceleration in Orders and Quotes

Kickstarting Automation is Imperative for Today’s Efficiency Levels in Enterprise Operations

While decades of ERP and CRM deployments have delivered structured automation gains for enterprises, the next phase is cognitive acceleration: empowering AI-driven platforms to execute actions, generate documentation, and adapt to situations. In this new paradigm, workflows like quoting, order intake, and client communication are no longer linear and reactive these processes are intelligent, streamlined, and built to relieve pressure from overstretched teams.

The evidence is clear. As global logistics, manufacturing, and tech enterprises compete in hyper-saturated environments, the ability to generate quotes, proposals, or internal workflows based on real time requests is becoming a decisive efficiency differentiator. Full automation is the new foundation of operational workflows.

The Hidden Inefficiencies in Quote and Order Workflows

For many organizations, sales and order processes remain constrained in manual tasks. Quote-to-cash (Q2C) flows are fragmented across email inboxes, siloed spreadsheets, and unintegrated systems. Reps waste hours transcribing customer requests, navigating legacy pricing tools, or reformatting data for operations. Meanwhile, customer expectations continue to rise rapid response times, personalized proposals, and cross-channel engagement are now the norm.

Studies confirm the drag this creates. According to Andiyappillai (2021), even logistics giants face consistent barriers when legacy systems prevent smooth coordination between departments. These bottlenecks result in high error rates and slow internal response cycles that exhaust staff and frustrate customers. McKinsey's report on logistics automation echoes the challenge, noting that while companies understand automation's value, implementation is stalled by fragmented decision-making and bottlenecks that stifle workflow continuity (Dekhne et al., 2019).

Manual quoting exposes companies to operational fatigue: pricing errors, lost time, and missed opportunities compound daily. In many firms, inboxes act as unstructured queues, overloading teams and draining time and energy from high-value work. Organizations know they need change but until now, solutions have been either too rigid or too complex.

Automation Opportunities

AI-driven solutions are bridging the gap between current manual processes, to automated workflows with minimal manual work for tasks such as document generation, document generation, and decision-making. Here’s how they’re doing it:

• Unified Request Prioritization Across Channels: Today’s requests come in via email, WhatsApp, customer portals, or phone. Without a centralized triage process, teams drown in fragmented communication. Intelligent platforms now assess and prioritize these requests in real-time, offering immediate clarity to employees and reducing stress. The result is a calmer, more controlled workday where no critical task falls through the cracks.
• Document Intelligence to Drive Internal Workflows: With the right tools, an incoming quote request isn’t just a communication; it’s a trigger. AI agents can parse the request, extract relevant data, and produce internal documents that activate workflows across departments. This eliminates bottlenecks, reduces email overload, and gives teams a feeling of flow and forward motion.
• Smart Internal Knowledge Activation: Employees often lose hours hunting for files, checking on project statuses, or confirming specs. Intelligent document systems now allow AI agents to not only answer questions based on received files but also connect those documents to the company’s internal memory. Whether it's a past quote, a delivery schedule, or a compliance protocol, answers are accessible reducing delays and restoring confidence across the team.
• Explainable AI for Trust and Clarity: Explainable AI allows users to understand why Machine Learning delivers specific results. Employees need to understand why one request was prioritized over another or how a document was generated. Leading automation platforms with built-in explainable decision logs reduce ambiguity and build human trust in the system.

Key factors to implement quotes and order automation

Implementing quotes and order automation isn’t a technology problem; today it means a strategic alignment issue. Successful deployments share key characteristics:

• End-to-end process integration: Automations that touch email or CRM alone won’t drive ROI. Impact comes when agents operate across departments: extracting documents, triggering workflows, syncing pricing tools, and surfacing internal knowledge.
• Customizable algorithms: A procurement quote in pharma has vastly different thresholds than a B2B tech order. The automation logic must be tailored with internal expertise and sector-specific nuance through algorithm training.
• Organizational scalability: As more requests are processed, the system should learn which workflows deliver value and which create friction. The goal isn’t just speed, its repeatable confidence, cross-team reliability, and sustained relief.
• Early adopters' culture: Automation thrives when it’s not seen as a threat, but as relief. Teams must be shown how it simplifies their lives and how it eliminates repetitive work, reduces context-switching, and allows them to focus on results.

How Backwell Tech address the workflow challenge?

At Backwell Tech, we developed the MailSense platform to close this automation gap and restore operational clarity. MailSense enables companies to automatically prioritize incoming requests across channels, generate structured documents that activate internal processes, and empower teams with fast access to the information they need.

Our platform delivers:

• Inbound request classification: Rank and organize customer order emails into manageable queues. Automated document generation: Instantly create quotes, contracts, and proposals without repetitive manual labor.
• Knowledge activation: Enable AI agents to pull answers from internal files and respond intelligently to customer requests.

With MailSense, companies are both adding automation and reducing mental overload, which in a way achieves to an smarter redistribution of work, and to unlocking team-wide relief. At its core, it is about giving people the tools they need to let them work with clarity, speed, and renewed focus.

Backwell Tech’s MailSense exists to make work easier. For teams who are tired of chasing customer emails, formatting documents, or scrambling to find files, the future is faster, lighter, clearer, and finally manageable.  

About Backwell Tech

Backwell Tech is a Berlin-based high-tech company specializing in predictive AI solutions. The platform offers companies scalable AI models for profit maximization by utilizing historical and real-time data and ensuring data integrity. Since its founding in 2019, Backwell Tech has combined cutting-edge research with practical innovation in explainable algorithms. The company focuses on ethical AI development and delivers reliable, interpretable forecasts that enable informed business decisions. More information at www.backwelltechcorp.com.

Backwell Tech Corp contact:

Maximilian Gismondi

hello@backwelltechcorp.com

References

• Andiyappillai, N. (2021). An Analysis of the Impact of Automation on Supply Chain Performance in Logistics Companies. IOP Conf. Ser.: Mater. Sci. Eng. https://doi.org/10.1088/1757-899X/1055/1/012055
• Dekhne, A., Hastings, G., Murnane, J., & Neuhaus, F. (2019). Automation in Logistics: Big Opportunity, Bigger Uncertainty. McKinsey & Company. https://www.mckinsey.com/industries/travel-logistics-and-infrastructure/our-insights/automation-in-logistics-big-opportunity-bigger-uncertainty
• Baramichai, M., & Prateepkaew, P. (2025). Leveraging Artificial Intelligence for Efficient Quotation Management. https://so03.tci-thaijo.org/index.php/journalcim/article/download/288450/189168
• Hota, A. (2024). The Modernization of the Quote-to-Cash Process in Data Centers through AI and Automation. https://www.researchgate.net/publication/386584450
• Karnani, B. (2025). The Evolution of CPQ Systems: AI Integration and Revenue Impact. https://www.researchgate.net/publication/389368660
• Zdravković, M., & Panetto, H. (2022). AI-Enabled Enterprise Information Systems for Manufacturing. https://hal.science/hal-03286677/file/Zravkovic%20et%20al.pdf