Consulting firms can use AI to outline pitch decks, draft supporting slides, and tighten language, while the strategy, insight, and pricing stay with the consultants who win the work. The benefit is less time on slide mechanics and more on the argument. Generative AI is widely applied to knowledge work, as McKinsey describes, which fits deck preparation.
The deck time drain
Building a deck from scratch each time wastes senior hours on structure and formatting. AI can produce a first structure and draft slides so consultants focus on the insight.
Where AI helps
- Drafting a logical deck structure from a brief
- Writing first-pass content for standard slides
- Tightening wording and headlines
- Reusing and tailoring approved frameworks
Keep the win human
The strategic story and the pricing are what close engagements. Keep those with the team. Use AI to remove busywork, not to make the argument.
A deck workflow
- 1
Brief the AI
Provide the client context and your goal..
- 2
Draft structure
Get an outline you can challenge and reorder..
- 3
Fill standard slides
AI drafts the routine content..
- 4
Add the insight
Consultants shape the story and set the price..
Get the team using it
Adoption is the hard part. Our guide on training a skeptical team helps.
A real-world example
Google Cloud's use case library documents professional teams using AI to speed up content creation; the attributed examples translate to deck preparation.
These figures are third-party research shared for context, not a promise about your business. Your own results depend on your tools, your data, and how your team adopts them.
Can AI build our whole deck? +
It can draft structure and standard slides, but the strategy, insight, and pricing must come from consultants.
Will decks look generic? +
Only if you skip tailoring. Use AI for the base and add your argument and design.
Can we upload client information? +
Only into business-grade tools and only what the client has approved sharing.
Does it save time? +
Often on structure and first drafts, but measure your own hours to confirm.
Common mistakes to avoid
The most common mistakes are predictable, and avoiding them is most of the work. Firms run into trouble when they skip a clear review step, when they paste confidential client information into the wrong tool, or when they expect AI to handle judgment it cannot. None of these are technical failures; they are process gaps that a short policy and a habit of review will close.
- Treating AI output as final instead of as a first draft to verify
- Putting confidential or privileged data into consumer-grade tools
- Rolling out across the whole firm before testing on one task
- Measuring only minutes saved and ignoring quality and rework
- Letting AI make decisions that require a licensed or qualified professional
What to measure before you commit
Before you decide whether a tool earns its place, set a simple baseline and track a few honest numbers over a few weeks. Time per task matters, but so do rework, error rates, and how the work feels to the people doing it. A tool that saves time but creates anxious double-checking is not a win, and a tool that quietly improves consistency may be worth more than the clock alone suggests. Keep the measurement light enough that you actually do it, and revisit the decision as your workload and the tools change.
How to get started this week
If you are ready to try this, keep the first step small and concrete. Pick one task you do often, agree on who reviews the output and which tool is approved, and run it for a couple of weeks alongside your normal way of working. Write down what you notice. A narrow, well-reviewed start builds the confidence and the evidence you need before you expand, and it keeps your clients protected while your team learns. The firms that get value from AI tend to be the ones that started small, measured honestly, and grew only when the results were clear.