The starting point: Why inside sales is under pressure in 2026

In 2026, inside sales faces pressure from three main directions. First, market pressure is increasing due to changing expectations in B2B purchasing. Buyers are bringing their private B2C experiences into their professional roles. They expect immediate availability, transparent delivery statuses, and fast shipping. This creates a baseline expectation for rapid response times, proactive updates during delays, flawless execution, and a quote for every single inquiry.

Second, demographic shifts are making the staffing situation worse. As baby boomers retire, valuable knowledge is lost, while recruiting new talent and skilled workers remains difficult. Repetitive tasks like manual data entry act as an additional hurdle when trying to attract people to these roles. In rural areas, this is combined with a general shortage of available workers.

Third, internal roadblocks slow down performance. Outdated IT infrastructures, rigid ERP interfaces, and historically grown processes clash with rising order volumes and a stagnant headcount. The pressure therefore comes not just from the market, but from the company's own success, which teams are trying to manage with old structures.

‍

Reality in daily business: High expectations, slow responses and error costs

There is often a wide gap between expectations and reality. While customers expect an answer within an hour, the actual response time is often "in two business days." An order placed on Friday can easily lead to a response the following week. This makes status inquiries much more likely, causing further interruptions for the inside sales team.

Manual data entry also increases the risk of errors. Typos or incorrect units of measurement can lead to wrong deliveries, returns, and frustration. At the same time, many organizations simply do not have enough time to reply to every inquiry with a quote. We see an average quoting rate of around 30% in the market, even though more revenue would be possible if teams could process more requests.

Looking at how tasks are distributed reveals a structural problem. Only a small fraction of the work adds real value. Another part consists of "firefighting" tasks like handling inquiries and internal coordination. The largest share goes to administration, especially transferring existing information into internal systems.

‍

The goal: From cost center to profit center

The objective is to shift the role of the inside sales department. Instead of acting as reactive order processors, the team should become active problem solvers. In cost center mode, the focus is on processing, measured by the number of orders and quotes entered. Scaling this model mainly requires adding more headcount.

In the profit center model, the focus shifts to proactively identifying customer potential, providing advice, and solving problems. Success is measured more by customer satisfaction as well as cross-selling and upselling opportunities. Flawless execution and digitized processes reduce manual work and prevent the need to scale linearly through hiring.

‍

Skills that are becoming more important in inside sales

As the role changes, so do the required skills. Consultative selling becomes central to analyzing actual needs rather than just taking orders. Empathy and problem-solving skills become more important, especially for edge cases and demanding situations.

Systematic process thinking becomes a key capability for identifying error patterns across systems and improving workflows holistically. Data literacy includes understanding reports and dashboards and deriving optimizations for sales activities. In addition, proficiency with AI tools becomes essential, including prompting and knowing exactly when human validation still makes sense in the process.

‍

AI-First: What a modern inside sales team would look like built from scratch

A modern inside sales department, built from scratch without legacy structures, would make order processing as digital as possible. Orders would primarily come in via web shops or online forms in a standardized and structured format. Standard products and uniform processes without special rules would reduce routine work and enable complete automation of digital transactions.

In this ideal scenario, customers receive order and shipping confirmations digitally. They can check status information in a customer portal and ask questions via digital platforms. Routine status updates and simple product inquiries are answered by AI agents. This is complemented by self-service offerings like product comparisons, video tutorials, spec sheets, and certificates to cover information needs without manual interaction. Human interactions are then used to deliver targeted added value, for example through specialized expertise and fast, competent problem solving. The foundation for this is a modern IT and data infrastructure with well-maintained data and interfaces.

In this context, AI-First means that AI is the primary driver of processes, and workflows are designed around digitization and automation. Processes are engineered to be digital, replicable, and as clear as possible to allow end-to-end automation of entire workflows and job roles. Humans handle exceptions and strategic tasks, orchestrate the AI agents, and stay in the final loop. The benefit for customers is receiving confirmations within seconds, meeting their increased expectations for speed.

‍

Reality in established companies: Fragmented systems and special rules

In practice, the starting point is usually shaped by history. Historically grown, fragmented system landscapes, varying degrees of digitization, and fluctuating data quality make standardization difficult. Special rules for certain customers increase the workload. Routines require a lot of personnel because processes were historically built around available employees. On top of this are legacy issues from past ERP migrations and a lack of time for master data maintenance, since revenue priorities dominate. At the same time, poor data hygiene blocks opportunities for automation.

The goal is therefore to integrate AI systems into existing structures instead of starting completely from scratch. Existing systems and processes remain the system of record, but they are made future-proof by adding a digital intelligence layer.‍

‍

The AI-first maturity model: From manual entry to full automation

The AI-First Maturity Model helps companies classify their own development and existing system landscape. It helps to assess the current level of automation and to break down the path to full automation into clear, actionable steps.

Level 0: Manual

At this entry level, data entry is 100% manual. There is no automation whatsoever. This means employees spend a large part of their time manually typing data from emails or PDFs into the ERP system.

Level 1: Assisted

The first step toward relief is assisted work. Software supports employees during data entry. A typical practical example for this step is the use of OCR (Optical Character Recognition) software. This makes the content on incoming documents machine-readable, which provides an initial, noticeable reduction in daily workload.

Level 2: Semi-automated

The next stage involves semi-automated workflows. At this point, the system takes over predefined subtasks. However, a continuous control mechanism is still in place. A human still checks every single order without exception before it is finally booked or processed further.

Level 3: Intelligent

At Level 3, the system moves past rigid automation and becomes intelligent. Companies can now run standard orders fully automatically through the system. Humans are only brought in to review exceptions or discrepancies. This already brings very significant time savings, but the automation is primarily limited to pure standard cases.

Level 4: AI-First

The highest level describes the true AI-First approach. The AI no longer acts purely as an assistant, but as the primary process driver. At this stage, full automation of over 95% of the entire processes is achieved. The remaining 5% are not skipped for technical reasons, but are deliberately not automated. These are highly complex edge cases or extremely important key accounts. Here, humans intentionally take over communication because they create real, decisive added value through empathy and expertise in these situations.

‍

Methodology: Use AI where it makes sense

AI is most effective where processes are not straightforward and contain many variables. For clear 1:1 if-then workflows, classic automation can be enough. AI shows its real value when different inputs need to be brought together, when orders vary, or when data from multiple systems needs to be enriched.

This requires an understanding of technology that assesses both opportunities and limits, including the question of what level of uncertainty is acceptable and where stronger human-in-the-loop mechanisms are required. Make-or-buy decisions also play a role, since developing foundational AI models is not the core focus of many companies. A pragmatic approach follows the 80/20 rule. First, automate the processes that cause the most manual effort, the highest frustration, and the biggest time drains. This frees up time quickly and allows you to build on it to improve further processes.

‍

Architectural principle: “sandwich strategy” between input channels and ERP

In inside sales, inquiries and orders arrive via email, phone, and web shop, while the ERP system requires structured input as the central leading system. The Sandwich Strategy describes an intelligence layer in between. An AI agent handles the translation from unstructured input to structured data. In addition, an integration layer can enable data exchange with the ERP, for example via existing middleware or direct connections. One advantage of this approach is a faster implementation compared to multi-year IT projects.

Operationally, work shifts away from data entry, searching, and copy-pasting toward validating AI suggestions, relationship management, empathy, cross-selling, advising existing customers, and learning how to use AI tools.

‍

In practice: Three use cases for AI agents in internal sales

‍

1) Digital order recorder for PDF and email orders

A typical starting situation is manually retyping PDF orders, which often takes three to five minutes per order, and correspondingly longer for larger orders. Order quality fluctuates, which also affects processing time.

An AI agent can read emails and PDFs, understand the context, and match customer-specific item numbers to internal article numbers. By accessing ERP data, the agent can add master data, such as deriving delivery addresses from customer numbers, and create the order in the ERP system. The result is a process duration of less than ten seconds. In cases of uncertainty, the AI asks for feedback, while employees only need to review.

‍

2) Status queries and simple product inquiries (“Where is my order? “)

Status requests lead to frequent team interruptions. An AI agent can recognize the intent, search for tracking information in the system, and automatically reply with a link or status, such as whether a shipment is in dispatch, scheduled, or out of stock. This creates quiet time for more complex, revenue-generating topics. Status queries are thereby processed where they can be answered efficiently and digitally.

‍

3) Quick preparation of offers (“Quote Sprinter”)

Another problem is low quote coverage when the inside sales team is at capacity. For inquiries about standard items, an AI agent can analyze the request, consider purchase history, create a shopping cart with customer-specific prices, and prepare a quote for approval as a cart link, PDF, or email. Faster quote generation increases the likelihood of closing a deal. Market observations show that only around 30% of inquiries turn into a quote, even though the inquiries already exist, meaning there is potential to increase revenue.

‍

Control and quality: AI traffic light system with human-in-the-loop

A traffic light system is also used to control automation. In the green zone, confidence is high, so standard orders or recurring orders from existing customers can pass straight through. Yellow stands for a certain degree of uncertainty, where the AI assists and the human gives final approval, such as with new customers. Red marks exceptions and business-critical cases that are escalated directly to experts, such as complaints. This model supports the goal of not replacing people, but using them in value-adding ways where they provide the greatest benefit.

‍

Your practical guide for tomorrow: From start to rollout in 4 steps

The theory around AI agents and maturity models sounds logical, but the most frequent question from practice is how exactly to start this transformation without jeopardizing daily business. A successful transition does not need a big bang or a multi-year IT mega-project. It needs a structured, pragmatic 4-step plan:

1. Analysis (Needs analysis and strategic planning): Objectively identify the process in your company with the highest manual effort. Where do your employees lose the most time every day just typing or searching?
2. Data (Data collection and preparation): Check your master data quality. AI and automation need a solid foundation. Clean up item numbers and customer master data to ensure smooth operations.
3. Pilot (Testing and validation): Start with an AI agent for a clearly defined use case in a small sub-area. Test the system in a safe environment before changing over the entire back office.
4. Roll-Out (Implementation and scaling): If the pilot works successfully, you can scale. Ideally, carry the solution step by step into other processes and departments using the positive momentum and advocacy from the pilot team.

Important for success: AI must always be understood as a tool, not a replacement. Involve your team from day one, ask specifically about the biggest frustration factors, and invest in upskilling to train employees in using the new software.

‍

The right tool: AI tools for an immediate start

You do not have to reinvent the wheel for these first steps. For almost every bottleneck in sales and the back office, there are specialized AI solutions available today that fit seamlessly into the sandwich architecture. Here is a brief overview of tools that can help you right now:

• Venta: A specialized lead generation solution for business users.
• Acto: Provides targeted, AI-supported assistance for field sales.
• TLDV & Noota: Both tools help business users automatically record and transcribe meetings.
• Tacto: An AI platform designed specifically for strategic purchasing.
• n8n: A powerful tool for workflow automation (aimed primarily at technical users).
• Workist: AI agents for inside sales. Ideal for business users who want to fully automate order entry from unstructured PDFs and emails directly into the ERP.

‍

Results and business case logic: Time saved as the basis for increased revenue

A concrete calculation example describes the effect of automation. If 100 orders are automated per day, it creates a time saving of about five hours per day. This time can be invested in actively following up on open quotes, reactivating C-level customers, and providing better support for key accounts. The modernization pays for itself through the additional revenue made possible by the freed-up capacity. The focus is on relief rather than redundancy, with the goal of "less back office and more sales" in the inside sales department.

Additionally, happier employees and the ability to enter new markets are mentioned, since AI can cover new languages.

‍

Assessment: Where AI agents are a particularly good fit

Companies with a broad range of standard products with fixed item numbers and product descriptions are described as particularly suitable. A product catalog with item master data supports automation, especially when customers order without item numbers and only provide descriptions. For services that are formulated "on request" specifically for the customer, the level of automation is correspondingly lower.

‍

Digital and human interaction: A mix of both worlds

Digital self-service offerings and quick, automated answers are seen as an important trend, especially with younger buyers who want to handle processes digitally just like in their private lives. At the same time, human contact remains relevant when help is needed or when processes are set up so that orders do not happen exclusively via digital channels. A combined approach of digital processing and targeted human support therefore forms a consistent target picture for inside sales in 2026.