AI Applications for Sales and Marketing

November 6, 2026

Artificial intelligence (AI) is becoming part of everyday sales and marketing activities. For packaging companies, its usefulness can range from simple tasks such as researching a prospect or summarizing a meeting to more advanced applications built into CRM and other business systems.

The range of possible uses is broad, but companies don’t need sophisticated technology to start using AI. A salesperson working from a spreadsheet can use an AI tool for prospect research or to help draft a customer email. A company with a well-established CRM may be able to use AI to analyze sales activity, prioritize opportunities, or capture information from customer calls. The right applications will depend on a company’s current processes, what information it has available, and where employees spend their time.

Finding and Understanding Prospects

Sales teams can use AI to support prospect research and market profiling. For example, an AI tool can help develop an initial list of companies that fit a particular target market. A salesperson could use it to identify manufacturers in a particular industry, companies with certain characteristics, or businesses that may need particular types of packaging.

AI can also help organize and summarize information about a prospect before a sales call. Instead of reviewing numerous sources individually, a salesperson can use AI to develop a starting point for understanding the company, its markets, products, or recent developments. While this is useful as a research aid, it should not be treated as a verified prospect database. AI can miss companies, misunderstand what a company does, or provide outdated information. Check important information before using it in a customer conversation.

Supporting Market Education and Content

Marketing teams spend a lot of time translating technical and sales information into customer-facing material, which provides many opportunities to use AI. AI is useful for:

Researching for newsletters, articles, and other educational content.
Brainstorming topics and developing outlines.
Creating initial content drafts.
Editing for spelling, grammar, and readability.
Turning technical information into material for a less technical audience.
Developing FAQs and question-and-answer content.
Summarizing research and identifying trends.
Analyzing marketing data and reports.

Search engine optimization (SEO) is another area in which AI can assist. Marketing teams can use it for keyword research, content planning, content audits, meta descriptions, schema markup, and other routine SEO tasks. AI can also help evaluate whether content is structured in ways that make it easier for search engines and AI-driven search tools to understand.

However, there is a limit to how much of this work should be delegated. Corrugated packaging manufacturers have substantial technical knowledge that generic AI tools do not. Information about substrates, converting capabilities, lead times, equipment, testing, print processes, or packaging applications needs to come from the company and its people.

AI does a great job organizing and presenting that knowledge, but employees with subject-matter expertise still need to establish what is accurate and relevant. Care should be taken when using it to create content without extensive human review. AI-generated content is often generic and won’t differentiate the manufacturer in the market.

Managing Sales Opportunities

Companies that already use a CRM may have opportunities to add AI capabilities to their existing sales process. AI-enabled CRM functions can help identify accounts that need follow-up, prioritize opportunities, summarize customer interactions, prepare sales reports, and assist with email development. These applications can help when salespeople manage many accounts and opportunities.

Call-management tools can provide another source of information. Depending on the system, AI may be able to transcribe calls, summarize conversations, identify action items and topics, analyze participation in the conversation, or record relevant activity in a CRM. For a sales manager, this information can make it easier to see what is happening across a sales team without asking every salesperson to prepare another report. It can also reduce salespeople’s administrative work. A call summary, for example, can provide a starting point for entering information into a CRM and preparing the next customer communication.

Improving the Sales Process

Many sales teams find AI assistance helpful with activities before and after customer meetings. A salesperson can use it to prepare a meeting agenda based on previous conversations, summarize notes after a meeting, and turn those notes into follow-up actions.

It can also organize information from several meetings or identify recurring questions and concerns. Over time, analyzing this information may reveal patterns that are difficult to spot when information is scattered among emails, spreadsheets, and individual notes. For example, an analysis might show that opportunities are regularly delayed at the same point in the sales process or that certain types of prospects have longer sales cycles.

The salesperson or manager still needs to determine what those patterns mean and what action, if any, should follow. AI makes the information easier to examine, but sales and executive teams should provide business judgment and decisions, as applicable.

AI as an Everyday Assistant

Some useful applications do not
fit neatly into sales or marketing tasks but, nonetheless, are worth mentioning. Use of AI as an everyday assistant can save a considerable amount of time.

Employees can use AI to help with:

Researching and gathering information.
Training and education.
Brainstorming and refining ideas.
Creating templates.
Cleaning and organizing data.
Identifying trends in data.
Summarizing large amounts of information.
Developing project plans.
Preparing communications.
Creating images and graphics.
Explaining technical subjects to nontechnical audiences.

For a corrugated packaging manufacturer, that last application can be particularly useful. Salespeople regularly explain technical packaging considerations to customers, while marketing employees may need to understand manufacturing processes well enough to write about them. AI can provide information to help someone get oriented to an unfamiliar subject or turn technical information into more accessible language. Of course, the employee still needs to determine whether the explanation is accurate.

Tracking Visibility Within AI

Marketing teams can use AI tools to run a basic visibility audit of their online presence. They can test the same set of customer-style questions across search engines and AI answer tools, then record whether the company appears, how it is described, which competitors are mentioned, and whether the answer cites the company’s website. The results will not be exact because AI answers can change by tool, prompt, location, and available source material. Still, repeated checks can show whether a company is clearly associated with its capabilities, industries served, materials, and common customer problems.

If the company is not appearing for topics it should, or if AI tools describe it inaccurately, the marketing team can improve the source material those tools are likely to read, an activity described as answer engine optimization (AEO) or generative engine optimization (GEO). That may include clearer service pages, stronger industry pages, FAQs written around customer questions, case studies, and better-structured content. For a packaging manufacturer, this gives marketing a way to see whether the company’s expertise is visible in both traditional search and AI environments.

Good Data Is Required

AI-generated analysis depends on the information available to the system. For example, if customer information is inconsistent, AI may produce analysis based on unreliable records, which could lead to poor decisions based on the results. If sales activity is not recorded, an AI system may have little useful information to analyze, providing inaccurate forecasts.

Because of this, companies may get more value from improving their existing processes before adopting more advanced AI applications. Cleaning up a spreadsheet or CRM, establishing a consistent process for recording customer information, or documenting a sales workflow can improve later AI results. This also creates a clearer path for companies at different stages of technology adoption. A sales and marketing team can start with individual tasks and gradually incorporate AI into existing systems when the benefit is clear.

Where Caution Is Needed

AI should be looked at as a productivity tool, and its output requires review. One of the biggest limitations is that AI can produce information that sounds convincing but is incorrect. This is particularly important in packaging, where an inaccurate statement about materials, specifications, testing, regulatory requirements, or manufacturing capabilities could create problems beyond a poorly written marketing sentence. A company risks losing credibility. Proper use of AI can help establish a company as a market leader; used poorly, it can also undermine that credibility. Human review remains necessary for technical information, customer-specific information, pricing, contractual matters, and other consequential decisions.

Companies should also consider what information they provide to AI tools. Companies should not enter customer information, proprietary pricing, unpublished product information, internal processes, or other confidential material into an AI service without understanding how that service handles the information and whether company policy permits its use.

There is also a risk of losing the personal element of sales and marketing. AI can produce an email in seconds, but a generic message sent to hundreds of prospects is unlikely to build a strong customer relationship. Similarly, prospect research can help a salesperson prepare a relevant message, but the salesperson still needs to decide what is worth saying and how to approach the customer.

The same caution should be applied to AI analysis of sales calls. Transcripts and sentiment analysis (whether AI determines the language is positive, negative, or neutral) can provide useful information, but they are interpretations rather than objective measurements of a salesperson or customer. Context, tone, and the nature of a customer relationship can be difficult for an automated system to capture reliably.

Where to Begin?

For companies that are just starting to explore AI, the first step can be using it for a single repetitive task. Meeting summaries or prospect research are good places to start. Marketing teams might begin with content research, editing, or data analysis. Sales managers might use AI to summarize reports or identify trends in existing sales data.

Once employees become comfortable using AI for individual tasks, the company can evaluate whether it makes sense to incorporate AI into existing systems and workflows. The results can be measured in fairly straightforward ways.

Is the tool saving employees time?
Is the output useful enough to reduce rework?
Are salespeople spending more time with customers?
Is information easier to find?
Are managers seeing patterns in the data that were previously difficult to identify?

These measures can provide a good basis for deciding where AI belongs in a company’s sales and marketing processes. AI will not eliminate the need for salespeople who understand their customers or marketers who understand their markets. In the corrugated packaging industry, relationships, responsiveness, technical knowledge, and manufacturing expertise remain central to how business gets done.