From “type something into the box” to designing repeatable, revenue-generating AI workflows

1. The New Literacy of the AI Office

In 2023, typing a clever sentence into a chatbot felt like a party trick. By late 2025, the same act has become a core business skill with real money at stake.

Marketing teams now rely on prompts to generate campaign concepts and copy variants. Strategy teams use prompts to synthesize research and pressure-test scenarios. Sales teams lean on prompts to tailor outreach, refine pitches, and coach reps in real time. Reports from Salesforce, McKinsey, and others show that generative AI is reshaping marketing and sales, with hundreds of billions in potential productivity gains and a significant share of that value tied directly to how well humans can drive these systems. Salesforce+3McKinsey & Company+3McKinsey & Company+3

Yet inside many companies, prompting is still treated as a casual skill: something people pick up by scrolling social media threads full of “magic prompts.” The reality is more demanding and more interesting.

Effective prompting is emerging as a form of operational literacy: the ability to translate business intent into structured instructions that AI systems can reliably act on. In that sense, learning to prompt is less about clever phrasing and more about learning a new way to think.

2. What Prompting Actually Is (and Isn’t)

If you strip away the hype, prompting is straightforward to define. Google Cloud calls prompt engineering “the art and science of designing and optimizing prompts to guide AI models toward desired responses,” emphasizing the importance of context, instructions, and examples. Google Cloud OpenAI frames it similarly in its best-practices guidance: clear instructions, constraints, and iterative refinement dramatically improve output quality. OpenAI Help Center

In other words, prompting is not about guessing magic keywords. It is about:

• Making your intent unambiguous

• Supplying the right context at the right time

For business users, that translates into prompts that define:

• The objective – “Draft three alternative product one-pagers tailored to CFOs at mid-market SaaS companies.”

• The constraints – “Use a confident but not aggressive tone; keep each under 250 words.”

• The process – “First list assumptions, then create the drafts. Ask me to confirm or correct any assumptions before writing.”

Harvard Business Review has argued that “prompt engineering” as a narrow job title may fade, but the skill of communicating with AI will become embedded in many roles, particularly in management. Harvard Business Review+1 That shift lines up with what we see in practice: prompting is becoming part of how people write, analyze, and decide—not a specialist activity.

3. Prompting for Content Creation: From Blank Page to Branded Asset

Content is the most visible arena where prompting has changed daily work. Marketers, product marketers, and comms teams use prompts to go from idea to draft in minutes. But the difference between “okay” and “excellent” AI-generated content usually comes down to how purposeful the prompt is.

A vague prompt like “Write a blog post about our AI product” tends to produce generic, forgettable copy. A well-structured prompt does something different: it embeds the business logic into the request.

For example, a content lead might write:

“You are a B2B SaaS marketing copywriter. Draft a 700-word blog post explaining how our new forecasting feature reduces last-minute revenue surprises for mid-market CFOs. Use a practical, no-hype tone. Structure: short intro with a relatable problem, three grounded sections with examples, then a concise CTA. Ask me two clarifying questions about the product before drafting.”

That single prompt encodes audience, benefit, tone, structure, and workflow. It also forces a brief “clarification loop” before the AI writes anything, which dramatically improves relevance.

Marketing-focused guides on prompt engineering stress just this sort of structure: specifying audience, channel, desired emotional response, and brand constraints so the AI is not guessing. Foundation Marketing+2Coursera+2

Over time, teams formalize these into prompt templates—reusable starting points for blog posts, landing pages, email nurtures, FAQs, internal announcements, and more. Prompting becomes part of the content operations stack, not just a one-off trick someone uses on the side.

4. Prompting for Strategic Planning: AI as a Thinking Partner

Beyond content, prompting is reshaping how leaders and analysts approach strategy work. Generative AI is particularly strong at synthesizing large volumes of information, generating scenarios, and highlighting trade-offs. McKinsey’s research on generative AI’s impact on marketing and B2B sales notes that strategy teams can use AI to quickly analyze internal and external data, then explore potential moves and risks. McKinsey & Company+2McKinsey & Company+2

But “ask the AI what we should do” is not a strategy. Here, good prompting emphasizes framing and boundaries:

A strategy manager might prompt an internal AI assistant like this:

“Act as a strategy analyst for a mid-market cybersecurity vendor focused on North American financial institutions. Given the attached pipeline data and last two years of win/loss reports, identify three patterns that explain our slower growth in enterprise deals. Summarize each pattern with supporting evidence, then propose two plausible strategic responses for each. Flag any data limitations that make your conclusions uncertain.”

Notice how the prompt:

• Defines a role and domain

• Points to specific data sources

• Specifies the deliverable format

• Asks explicitly for uncertainty and limitations

This kind of prompting turns the AI into a structured thinking partner, not an oracle. Leaders can then challenge, refine, or discard the AI’s hypotheses using their own experience and additional data.

Harvard Business Review has cautioned against over-relying on AI summaries in management context, noting that managers risk overlooking insights from their own teams if prompts focus only on what the AI can access. Harvard Business Review+1 The most effective strategists therefore craft prompts that treat AI as one input among many, not the only voice in the room.

5. Prompting for Sales Enablement: Coaching at Scale

Sales may be where prompting’s business impact is most immediate and measurable. Salesforce and others describe how generative AI can draft emails, tailor talk tracks, summarize calls, and provide just-in-time coaching, all guided by prompts keyed to the sales process. Salesforce+2Salesforce+2

Consider three common enablement scenarios:

1. Drafting and tailoring outreach
Instead of a rep starting from scratch, an enablement team can maintain prompt templates like:

“You are an SDR writing to a VP of Operations at a logistics company. Using the notes from our last call and the attached case study, write two follow-up emails. One should emphasize cost reduction, the other risk mitigation. Keep each under 150 words, avoid buzzwords, and end with a specific next step for a 20-minute call.”

Rep-level prompting skill then comes from adjusting these templates: swapping in different value drivers, industries, and tones while staying aligned with what actually converts.

2. Coaching after customer calls
With call transcripts and an AI assistant, prompts can turn raw conversations into feedback:

“Review this call transcript. Identify two moments where the rep handled objections well and one moment where they missed an opportunity to ask a discovery question. Provide concrete, timestamped suggestions in a supportive tone.”

Salesforce’s guidance on AI sales enablement highlights exactly this kind of workflow, where AI accelerates coaching content and personalizes it for each rep. Salesforce+1

Here, prompts don’t just generate artifacts; they shape behavior by making good selling patterns more visible, timely, and contextual.

6. From One-Off Prompts to Prompt Systems

In the early days, people treated prompts like spells. Today, leading teams treat them more like design assets. OpenAI, Palantir, Google Cloud, and university programs on prompt engineering all converge on the same pattern: prompting works best when you treat it as an iterative, documented process. Fisher College of Business+3OpenAI Help Center+3Palantir+3

In practice, that means:

• Capturing prompts that consistently produce good outputs and turning them into shared templates

• Versioning and improving those templates as products, markets, or internal policies change

A revenue operations leader might maintain a small internal “prompt library” covering content, strategy, and sales use cases, each with:

• A canonical “golden prompt”

• Example inputs and outputs

• Notes on common pitfalls or modifications

Over time, these evolve into semi-formal prompt playbooks for entire teams. And as AI agents become more capable—able to call APIs, update CRMs, or schedule campaigns—those prompts increasingly define workflows: what data to fetch, what actions to take, and when to request human review.

7. Guardrails: Avoiding Workslop and Hallucinated Strategy

There is a dark side to careless prompting. Harvard Business Review recently warned about “AI-generated workslop”—a flood of low-quality, lightly edited AI content that bloats inboxes, slide decks, and shared drives without improving actual outcomes. Harvard Business Review The problem is rarely the AI itself; it is vague intent, poor prompts, and weak review.

Two countermeasures make a big difference.

First, precision over volume. Instead of asking for “20 ideas” or “a 30-page deck,” prompt for a small number of well-structured outputs you know what to do with. It’s easier to evaluate three solid options than to sift through twenty mediocre ones.

Second, explicit verification steps. A good prompt often includes instructions like “List your sources” or “Flag any assumptions you made,” especially for strategy or financial work. That forces both the AI and the human to confront uncertainty rather than gloss over it.

Interestingly, some experts argue that as models improve, “lazy prompting”—supplying minimal, raw context like an error message—can occasionally outperform over-engineered instructions in technical tasks such as debugging. Stanford professor Andrew Ng has described this as an advanced strategy, suitable when the model already has strong background knowledge and humans can iterate quickly on the outputs. Business Insider

The practical takeaway is not that detailed prompts are obsolete, but that prompting is situational. Sometimes you need rigorous structure; sometimes you want the model’s unconstrained perspective first, then refine with follow-up prompts. Learning when to use which is part of becoming truly fluent.

Closing Thoughts and Looking Forward

Prompting began as a curiosity. It is now a crucial connector between human intent and AI capability, especially in business domains like content creation, strategic planning, and sales enablement.

Over the next few years, prompting will likely fade as a buzzword and solidify as a background competency—much like email etiquette or spreadsheet literacy. What will matter is not who has “prompt engineer” in their title, but which teams can consistently:

• Translate business outcomes into clear, testable instructions for AI systems

• Build and maintain prompt templates and playbooks that encode their best practices

• Combine AI-generated insights with human judgment, ethics, and domain expertise

As AI agents grow more autonomous and integrated into CRMs, marketing platforms, and planning tools, prompts will increasingly define process, not just text. A single instruction may trigger research, content generation, experiment setup, and reporting—making it even more critical that those instructions are clear, safe, and aligned with strategy.

Learning to prompt, then, is really learning to design collaboration between humans and machines. For individuals, that means practicing structured, outcome-focused requests and building a small personal library of prompts that genuinely save time or improve quality. For organizations, it means treating prompt patterns as assets, investing in training, and pairing prompting skills with governance and measurement.

If we approach prompting with that seriousness—somewhere between craft and engineering—it can move beyond “magic words” and become what it’s capable of being: a practical, everyday way to turn business intent into reliable, AI-accelerated execution.

References

1. Prompt Engineering for AI Guide
   Google Cloud.
   https://cloud.google.com/discover/what-is-prompt-engineering

2. Best Practices for Prompt Engineering with the OpenAI API
   OpenAI Help Center.
   https://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-the-openai-api

3. AI Prompt Engineering Isn’t the Future
   Harvard Business Review.
   https://hbr.org/2023/06/ai-prompt-engineering-isnt-the-future

4. AI-Powered Marketing and Sales Reach New Heights with Generative AI
   McKinsey & Company.
   https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/ai-powered-marketing-and-sales-reach-new-heights-with-generative-ai

5. AI Sales Enablement: A Complete Guide + Use Cases
   Salesforce.
   https://www.salesforce.com/ap/sales/ai/ai-sales-enablement/

Author: Serge Boudreaux – AI Hardware Technologies, Montreal, Quebec
Co-Editor: Peter Jonathan Wilcheck – Co-Editor Miami, Florida

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