In the last year, “AI for presentations” has become a crowded category. Plenty of tools can generate a decent-looking deck from a prompt, until you ask for the kind of slide that actually survives a leadership meeting. The difference between a pretty slide and a CEO-ready slide isn’t the text. It’s the engineering: constraints, hierarchy, brand rules, pixel-level alignment, editability inside PowerPoint, and the ability to produce the same quality output again and again.
To unpack why this problem is deceptively hard, TechGrid spoke with Andrew Persh, former McKinsey London leader of digital and AI transformations and now founder of Oria.one, a PowerPoint-native tool designed to turn rough outlines and notes into complex, executive-grade slides. In this interview, Andrew breaks down what “CEO-ready” really means in technical terms, why most slide generators fail on complex layouts, and what a production-grade architecture looks like when AI must deliver deterministic, editable output under real enterprise constraints.
What makes a slide CEO-ready in engineering terms?
“A CEO-ready slide isn’t about looking nice. It’s engineered for fast understanding and decisions. The real test is whether someone can grasp the content in the first five seconds looking at a slide and understand the conclusion in the next five seconds. That requires clear hierarchy, controlled density, and consistency across the deck. It also has to be editable in PowerPoint without falling apart, because in real leadership settings slides are always tweaked.”
Why do most AI presentation tools do fine on simple slides but fail on complex layouts?
“Because most tools are built on approaches that cap out quickly. One is template stuffing: pick a preset layout and force content into it, which works for simple pages but can’t cover the variety of real consulting slides. The other is HTML-first rendering: generate something that looks like slides as a web page, then convert it into PowerPoint. That tends to produce blocky, repetitive structures and breaks down on dense exhibits, mixed elements, and precise, consulting-style layouts.”
How do you represent a slide internally, and why does it matter?
“We represent a slide in two ways at once. We treat it as an image to capture the overall visual logic, balance, grouping, and hierarchy that are hard to express structurally. And we treat it as data to capture the exact PowerPoint object model: coordinates, text runs, fonts, and shape properties. You need both. Visual-only loses editability, and structure-only loses the design intent.”
What’s harder: generating content, or producing deterministic, editable PowerPoint layouts?
“Content generation is becoming commoditized. The hard part is reliably turning that into a deterministic, editable PowerPoint slide. Text is relatively easy; everything else is difficult: separating icons from images and shapes, recovering borders, fills, line weights, grouping, and then making consistent typography decisions. That last-mile “make it real in PowerPoint” layer is where most tools fail.”
How do you keep output consistent and brand-compliant while still being novel?
“Brand isn’t just colors. It’s typography, spacing rules, design patterns, master slide elements, and even safe zones for where content can live. We treat brand as a layered constraint system: style references, palette, fonts, masters, and placement rules. That still allows novelty, but only inside a controlled design space, so you get variation without breaking the brand.”
How do you prevent layout hallucinations like overlaps and drifting alignment?
“You reduce the model’s freedom and then verify everything. Strong references and brand constraints limit what the model can “invent.” Prompting helps, but it has to be rule-based and battle-tested, not vague. And then you need deterministic guardrails: overlap checks, alignment checks, safe-zone checks, and corrections so the final slide is mechanically valid even if the generation is probabilistic.”
What does your execution layer look like?
“It’s a set of specialized agents by element type: text, icons, charts, lines, shapes, and so on. Each agent runs multi-step logic and outputs clean PowerPoint-native objects. The text agent, for example, decides merging vs splitting, font sizes, hierarchy, and formatting like emphasis. The agents run in parallel, and their outputs can be validated independently, which improves speed and reliability.”
How do you evaluate quality at scale?
“Privacy limits traditional “collect everything and measure” evaluation, because we don’t store client slide data. So we rely heavily on power users and champions in pilots who push the tool hard and report what breaks. We review failures, categorize them, and iterate. Over time you build a practical failure taxonomy and harden the system against the most common real-world breakpoints.”
Biggest real-world reliability issues, and mitigations?
“A big one is model-provider volatility: migrations, deprecations, outages, latency spikes, and occasional API weirdness. You mitigate with monitorin with fallbacks and treating model changes like production releases. On the PowerPoint side, complex masters, and tricky chart/table edge cases, so you need detection and defensive rules rather than assuming a clean template.”
Engineering checklist for AI for Slides in 2026?
“Differentiate clearly, then build for production, not demos. You need deterministic layout under constraints, brand compliance as a first-class system, validators for overlaps/alignment/safe zones, and an execution layer that produces clean PowerPoint-native objects. Add strong observability, security and privacy foundations, auditability, and disciplined rollout/rollback for model and engine changes. In 2026 the winners won’t be the ones who can generate slides, but the ones who can do CEO-ready output reliably inside PowerPoint every day.”
