Why Some Teams Get Results From AI (And Most Don't)

How structured prompts and repeatable workflows turn AI into a real business capability

Executive Summary

Most of us can remember the excitement when generative AI first went mainstream. Teams rushed to experiment with tools like ChatGPT, often ending up in a prompt frenzy of ad-hoc experiments. Marketers, for example, collected “magic prompts” and jumped between AI models in hopes of content nirvana. The result has been familiar across industries. Hours spent re-prompting. Inconsistent outputs. Results that look impressive one minute and unusable the next.

The problem is not the models. It is the way we are using them. One-off prompting does not scale. It creates no consistency, no shared learning, and no reliable capability. Teams get stuck in a cycle of trial and error, where every task starts from scratch and success depends on individual experimentation rather than a repeatable approach.

The businesses seeing real value from AI have made a simple shift. They have moved away from ad-hoc prompting and towards structured prompting. It takes more effort upfront, but it delivers better results, faster, and at scale.

As one practitioner put it, ad-hoc prompts “leave behind zero institutional knowledge”. In practice, that means each new project starts from scratch, with no continuous improvement. Worse, the inconsistency can hurt your brand: one B2B SaaS company found their lead quality dropped 23% after six months of unstructured AI content, because prospects were confused by wildly different tones in the messaging. It’s a common pattern across industries: without structure, AI efforts become just another tool that eats your valuable time and even introduce risk (imagine trying to explain to how an AI made a decision when you have no record of the prompt or rationale).

Why Ad-Hoc AI Prompting Falls Short

To understand the need for a new approach, let’s examine why ad-hoc prompting fails to deliver business value (in the long term). Some common issues include:

• Lack of context: Users assume the AI understands their business, audience, or constraints. It does not. Without background, the model fills in the gaps with generic assumptions.

• Vague instructions: Ad-hoc prompts tend to be loosely worded – “Summarise this report” or “Help with marketing”. Such ambiguity leads to unpredictable outputs. The AI isn’t told what success looks like, so each response might focus on different aspects or miss the mark entirely.

• Inconsistent output guidance: One prompt might yield a formal essay, the next a casual list, because there’s no guidance on format or tone. People often forget to specify the desired style or structure for the answer. The result is output chaos – unusable formats that require heavy editing.

• One-and-done thinking: Many teams treat an AI query as a one-off exchange rather than part of a larger process. They might copy a “magic prompt” they found online that works once, but it breaks when the context changes. There’s no framework to iterate or handle edge cases, just trial and error each time.

• No success criteria: Perhaps the biggest gap is the absence of any quality framework. Teams rarely define what a good outcome should include (or avoid) in advance. With no criteria, there’s no reliable way to judge or improve the outputs beyond subjective guesses.

This is why ad-hoc prompting wastes time and creates risk. It produces outputs that are difficult to explain, hard to replicate, and impossible to govern. In regulated or brand-sensitive environments, “we asked ChatGPT” is not an acceptable process.

Content created via one-shot prompts, for example, often swings in tone and quality from piece to piece, undermining brand trust. (One enterprise found that their Monday blog post sounded corporate and formal, while Thursday’s social media copy read like a teenager wrote it.) Such inconsistency is not just cosmetic; it directly impacts business outcomes.

From Ad-Hoc Prompts to AI‑Proof Workflows

Clearly, businesses need to move beyond ad-hoc prompting. The solution is to treat AI not as a toy or a one-time trick, but as part of a structured workflow. This is the core of AI‑Proof: making your AI usage robust and repeatable by design.

An AI‑Proof workflow means that whenever you use AI for a business task - whether it’s generating a report, drafting an email, or analysing data, you follow a consistent process with defined steps and standards. Instead of relying on individual ingenuity for each prompt, you create a system that anyone on your team can follow to get reliable, consistent and repeatable results.

Think of it as moving from chatting with AI to orchestrating AI.

In a conversation, anything goes; in a workflow, you have stages, checkpoints, and handoffs between human and machine. This kind of structure brings the same rigor to AI that we expect from any business process. In fact, companies that have adopted structured AI workflows report transforming their AI efforts from sporadic experiments into a dependable competitive advantage, with measurable ROI and maintained quality at scale.

At the heart of the AI‑Proof approach is a structured framework. These are the three pillars that turn prompt-crafting into a disciplined workflow.

• Designing Structured Prompts instead of free-form queries,

• Providing Explicit Context rather than assuming the AI knows anything, and

• Defining Outputs so the AI (and your team) know what a good result looks like.

The Shift: From Prompting to Prompt Structures

Teams that get consistent results from AI do not rely on better wording or clever tricks. They rely on structure.

Instead of improvising each time, they use repeatable prompt structures that force clarity before the AI ever responds. These structures make users slow down, think through the task, and provide the information the model actually needs.

At AI-Proof, we see two prompt structures used most effectively in business settings.

Structure 1: The Core Business Prompt

This structure is ideal for everyday tasks such as drafting content, summarising documents, or generating analysis.

<Role>        Expertise the AI should adopt  
<Context>     Relevant background and situation  
<Goal>        What needs to be done, and for whom  
<Format>      How the output should be structured  
<Constraints> Rules, tone, length, or exclusions  

This structure forces five critical decisions before the AI starts writing.

1. Who is it acting as?

2. What does it need to know?

3. What is success?

4. What should the output look like?

5. What boundaries must it respect?

The key is don’t add context that is not relevant to the task, provide relevant information. In addition to goal or the task - be very specific - what do you want.

Compared to a one-line prompt, this feels like more work (and it is). In practice, it removes guesswork and dramatically improves first-draft quality.

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Structure 2: The Workflow Prompt

For more complex or higher-risk tasks, teams extend the structure to guide the AI through a sequence rather than a single response.

<Role>        Expertise the AI should adopt  
<Context>     Background and business situation  
<Task>        The primary task to complete  
<Steps>       How the task should be approached  
<Audience>    Who the output is for  
<InputData>   Data, documents, or facts to use  
<Format>      Required structure of the output  
<Constraints> Rules, checks, and guardrails  

This structure turns an AI interaction into a lightweight workflow. Instead of asking the model to do everything at once, you tell it how to think about the task, what to prioritise, and what standards it must meet.

In practice, this is how teams move from “chatting with AI” to orchestrating AI.

Structured Prompts

The first pillar of SPEC is Structured Prompts. This means developing a standardised format or template for your prompts, rather than writing them haphazardly. A structured prompt is more like a well-formed instruction sheet than a casual question. It typically includes sections or components that ensure all necessary information is covered. For example, a structured prompt template for a report summary might always have: 1) a statement of your role or goal, 2) the specific task or question, 3) relevant details or data points to include, and 4) the desired format of the answer. By using the same template each time (with new content plugged in), you impose order and clarity on the AI’s job.

In practice, template-driven prompts make your interactions with AI far more consistent. Rather than letting each user prompt in their own style, the team might establish: “For any customer email draft, always start the prompt by stating the customer’s context and problem, then clearly ask for the resolution steps, and finally specify the tone and format of the email reply.” This structure leaves little to guesswork. It’s analogous to providing a form for the AI to fill out, instead of a blank page. Well-designed prompt templates often include slots for context setting, task specification, output formatting, and even guardrails or disclaimersxenoss.io – ensuring that the AI has everything it needs to produce the right output and nothing important is omitted.

Crucially, structured prompts can be reused and improved over time. They become assets for your organisation. If one department crafts a great prompt template for, say, analyzing sales data, that template can be shared and standardised across the company. Teams end up building a library of proven prompts (sometimes called prompt libraries or playbooks) rather than every employee improvising. This not only saves time, but it means best practices propagate quickly. When prompts are structured and documented, you can also version-control them and track changes, much like code – enabling continuous refinement. In short, “template standardisation” turns prompt engineering into a scalable, team-based practice, with each template incorporating business logic and quality controls for consistent behaviourxenoss.io.

Finally, a structured approach makes it easier to troubleshoot and optimise prompts. If the output isn’t right, you can pinpoint which section of the prompt needs adjustment (e.g. maybe the task description was too vague, or the format instructions weren’t clear enough) instead of scrapping the whole thing. This modularity and clarity are the foundation on which the rest of SPEC builds.

Explicit Context

Explicit Context, is all about information. Context means any background, facts, or parameters the AI should know to do the task properly. “Explicit” means you spell it out every time, you do not rely on the AI to infer or remember things you haven’t told it (AI’s memory as of today is still very patchy). One of the most common failure modes in prompt usage is when users provide a bare instruction with either no context or irrelevant context, essentially forcing the model to make assumptions. For example, asking “Help me draft a proposal” in isolation could yield a generic proposal unrelated to your business. The AI has the ability to generate text, but it has no inherent knowledge of your specific situation unless you provide it.

Structured prompts allow you to explicitly feed in the who/what/why of the task: Who is the audience or end-user of the output? What are you trying to achieve? Why does it matter or what constraints should the AI keep in mind? By giving this context, you significantly increase the chances of a relevant and useful result.

For instance, instead of saying “Summarise this report,” an explicit-context prompt might say: “<Role>You are an assistant helping a financial analyst. <Goal>Summarise the attached quarterly report in plain English for a non-financial executive, focusing on revenue trends and any risks. The executive should grasp the company’s financial health in under 5 minutes of reading.” Here we have provided role, audience, purpose, and key focus areas.

Explicit context can include specific data and references as well. If you have numbers, names, or excerpts the AI should consider, include them in the prompt or as attachments, this is where the <InputData> is often used.

The guiding principle is: never make the AI assume. If the tone should be friendly but formal, say that explicitly. If the advice must align with UK regulations, state that. If the AI’s answer should refer to a specific product or scenario, describe it. By being explicit, you prevent the AI from filling gaps with its own (often incorrect) assumptions. This not only improves relevance, but also reduces risks like misinformation. In a business setting, persistent, governed context is what elevates AI from a nifty demo to a reliable tool – you maintain control over what knowledge the AI uses.

Over time, capturing context in your prompts also serves as a form of documentation; new team members can read past prompt+context setups and quickly understand the situation and solution, which again builds institutional knowledge.

Criteria-Driven Outputs

The format and the goal outputs addresses the “what does good look like?” question head-on. In traditional prompting, users often accept whatever the AI returns and then judge it by feel. By contrast with this approach, you define the success criteria before the AI generates the output. You effectively tell the model how to format or judge its answer, and you give yourself (or your team) a checklist to evaluate the result.

What do criteria-driven outputs look like in practice?
They can be specific instructions about the form of the answer, such as:
“The output should be a bulleted list of 5 key findings, each 1–2 sentences long.”

They can also include content requirements:
“Make sure to mention our three core product benefits and include a call-to-action in the final paragraph.”

They might involve style and tone guidelines:
“Use a professional tone, avoid jargon, and write in short paragraphs.”

And importantly (in the constraints section), they can outline what not to do:
“Do not make up any data; if you don’t have a certain detail, state that it’s not available.”

By encoding these criteria into the prompt (or having them clearly in mind to verify against), you set a quality bar for the AI’s work.

Many teams fail to do this, resulting in outputs that wander or miss key points. and then they wonder why the AI didn’t deliver. The truth is that vague prompts lead to vague outputs. As experts note, effective prompt engineering starts with clear, specific requirements: you must tell the model exactly what task to perform, under what constraints (length, format, tone, etc.), and ideally how success will be measured.

For example, instead of prompting “Write a press release for our new product” (and hoping it’s decent), a criteria-driven prompt would say:
<Role> You are an experienced PR Director and press release copywriter for fintech and banking trade media.
<Context> [FictionalProduct] is a SaaS platform for banks that detects anomalous transactions and account behaviour in near real time, streamlines fraud investigations, and creates audit-ready case files for compliance. It integrates with core banking systems and payment rails via API connectors. Launch date: 15 February 2026 (fictional) Pricing: from £4,500 per month (fictional)
<Goal> Write a one-page press release announcing the product launch, aimed at banking and financial crime industry media.
<Format> AP-style press release structure: Headline, dateline, lead paragraph, body, two executive quotes, release date and pricing, boilerplate, media contact.
<Constraints> One page (450–650 words). British English. Professional and factual. No hype or buzzwords. Use placeholders for any missing facts. Do not invent data beyond what’s in this prompt.
This prompt doesn’t guarantee a perfect output, but it significantly raises the floor, the draft will contain the key elements you specified, because you told the AI those were needed.

By defining output criteria, you also make it easier to evaluate and refine the results. You can check the AI’s answer against your checklist: Does it have all the parts we asked for? Does it follow our format and tone? If not, you know exactly what to fix (either adjusting the prompt or doing a quick edit).

In some cases, you can even have the AI self-check its output by providing criteria as a separate prompt, for example, asking:

• “List any ways the above output fails to meet the criteria”,

• “Play back what I have requested and highlight any ambiguities”,

which is only possible if you had clear criteria in the first place.

Criteria-driven outputs turn prompting into a criteria-driven dialogue with the AI. You’re effectively giving the model a target to hit. Without targets, the AI is shooting in the dark. This approach echoes practices from software and quality engineering: think of it as akin to having acceptance criteria for a user story, or test cases that the output should pass. The result is more consistent and measurable quality. Rather than just saying “this looks good,” teams can quantify improvements (for instance, did the output follow the template? include all 5 required points? stay within 500 words?). Over time, these criteria can be refined to raise the quality bar further. In short, if Structured Prompts and Explicit Context set the AI up for success, Criteria-Driven Outputs define what success means.

Why the Extra Effort Pays Off

Structured prompting feels slower at first. But it creates three compounding advantages.

Consistency
Outputs follow the same logic, tone and format every time. Brand voice stabilises. Quality stops swinging.

Speed at scale
Once a prompt structure exists, it can be reused, refined, and shared. What takes ten minutes to set up saves hours of re-prompting later.

Institutional learning
Prompt structures become assets. Teams improve them over time instead of starting from zero. Knowledge compounds rather than disappearing.

This is the difference between using AI as a novelty tool and using it as part of a business process.

What This Means for Businesses and Professionals

AI will not replace good thinking. But it will amplify poor thinking at speed.

The professionals and teams who benefit most from AI are not those with the cleverest prompts, but those with the clearest structure. They invest a little more effort upfront so that results become repeatable, explainable, and scalable.

If your AI usage still relies on ad-hoc questions and trial-and-error, the ceiling is low. If you adopt structured prompting, AI becomes something your whole organisation can use with confidence.

That is how teams move from experimentation to capability. And that is how AI stops being unpredictable and starts being useful.

Turn prompt structures into a shared team capability

One reason ad-hoc prompting fails inside organisations is that it often lives in silos, people’s heads. Someone finds a “good prompt”, it works once, and then it disappears into a Slack thread or a personal notes file.

A practical way to fix this is to standardise the environment your team works in, not just the wording of individual prompts.

Modern tools like ChatGPT & Claude.ai now support two simple building blocks that make this much easier: Projects and custom GPTs (ChatGPT).

Projects are shared workspaces where you can group related chats, upload reference files, and add custom instructions so the AI stays on-topic and remembers what matters for that specific workstream. You can also share projects with teammates, so everyone is working with the same context, files, and house rules.

Custom GPTs are reusable versions of ChatGPT that can be configured with instructions, knowledge files, conversation starters, and optional capabilities, so that users do not have to reinvent the setup every time. For a business, this is a lightweight way to bake in your brand tone, prompting structure, and definition of “good”. (OpenAI Help Center)

Put simply:

• Projects are great for ongoing initiatives where context builds over time.

• Custom GPTs are great for repeatable tasks where you want consistent behaviour on demand.

If your goal is to share and refine prompting strategies across a team, you can combine both.

A simple implementation you can do this week

1. Create a Project called “Prompt Playbook”
   Upload a one-page brand tone guide, your do and do not list, and your two preferred prompt structures:

• <Role> <Context> <Goal> <Format> <Constraints>

• <Role> <Context> <Task> <Steps> <Audience> <InputData> <Format> <Constraints>

2. Add Project Instructions that enforce the structure
   Example: “Always request missing inputs. Always respond using the chosen template. Always confirm audience and output format. Never guess facts.”

3. Create one custom GPT for a high-frequency use case
   For example: “Sales Email Drafter” or “Board Pack Summariser”. In the GPT instructions, hard-code your prompt structure and tone rules, then add a few conversation starters that make the right behaviour the default.

4. Make ownership a team sport
   Where your plan supports it, enable shared edit access so prompt improvements are not trapped with one individual. That is how prompt quality compounds over time.

The outcome is simple but powerful. Instead of relying on individuals to “be good at prompting”, you give the whole team a tuned environment where good prompting is the default. That is how structured prompting becomes repeatable at scale.

Next Steps: Making Your Business AI‑Proof

Adopting the AI‑Proof approach is a strategic move, but you can start with small, practical steps. Here are some concrete next steps to get started with workflow-based AI in your organisation:

1. Identify a High-Value Use Case: Begin with one area where AI is currently used in an ad-hoc way (or could be used) and where consistency matters, for example, generating minutes of meetings, drafting standard emails, or creating marketing content. Talk to the team involved and pinpoint the pain points (e.g. “We spend too much time editing the AI’s output” or “The style keeps coming out wrong”).

2. Create a structured prompt, project or Custom GPT for that Workflow: Assemble the team and design a structured prompt template for the chosen use case. Write out what sections the prompt should have: context/background information, the specific task request, and the output guidelines/criteria. Don’t worry about getting it perfect on the first try. Include explicit context (who is the audience or any data the AI needs) and define the output format and quality criteria as clearly as possible. You might draw from existing best prompts your team has used, but now you’ll formalise them. Document the template in a place everyone can access. This template will be the starting point every time the task is run. (If you’re using an AI platform that allows custom instructions or chaining prompts, you can encode the template there as well.)

3. Test, Tweak, and Roll Out: Run a few real-world tasks through the new structured prompt workflow. Evaluate the results against your criteria – did the AI output meet the expectations? Gather feedback from users: Was anything unclear in the template? Did we forget any context or constraints? Adjust the prompt template and criteria based on this feedback. You may do a couple of iterations until the workflow reliably produces good results. Once it’s working, formally adopt it as the new standard process for that task. Train the relevant team members on using the template (which might simply be walking them through filling it in, since it should be straightforward). Encourage them to suggest improvements as they use it. Over time, make sure to capture those improvements in the template (versioning changes as needed). Finally, you can scale up: apply SPEC to additional use cases, and build a library of AI workflow templates across departments. Regularly review these workflows to ensure they still align with business goals and update them if requirements change or if a better prompting technique is discovered.

By taking these steps, you’ll begin to AI-proof your business processes, turning sporadic AI experiments into dependable components of your operations. Remember, the goal is not to remove the creativity or flexibility of AI, but to channel it through well-defined processes so that it consistently delivers value.

As you implement AI-Proof workflows, you’ll likely find not only efficiency gains and better output, but also a cultural shift: your teams will start thinking in terms of systems and capabilities rather than one-off tricks. In an era where every company is looking to harness AI, those that build solid, workflow-based foundations will leap ahead. AI-Proofing your business today means you’re not just chasing the latest AI hype, you’re creating an enduring capability, one structured prompt at a time.

Next stop: The key is to keep the momentum: iterate on your workflows, share successes internally, and gradually expand AI-Proof practices to more areas. By doing so, you ensure that AI becomes a trusted ally in your business – delivering results predictably, efficiently, and on your terms.

What’s coming next and why you should subscribe

If structured prompting is how you get better answers, workflows are how you get repeatable outcomes.

In the next AI-Proof edition, we’ll show you how teams are turning good prompts into end-to-end workflows using tools like n8n and Zapier, plus the workflow features now built into platforms like ChatGPT and Google.

Not theory. Practical patterns you can copy.
The goal is simple: take a task that currently eats hours each week and turn it into a workflow that runs reliably in the background.

If you want that walkthrough, subscribe so you don’t miss it.