TLDR: Consulting firms spend 20-40% of billable hours on presentation creation. Published research from Harvard Business School, BCG, and McKinsey shows AI tools deliver 25-40% productivity gains on knowledge work tasks. For a 10-person analyst team, the math is straightforward: a 25% efficiency gain on 15 hours per week of deck work recovers over $200,000 annually in billable capacity, against a tool cost measured in thousands. This article provides the framework to calculate your firm’s specific ROI and build the business case for partners.
The Cost of Manual Slide Building
Consulting presentations are expensive to produce. Not because PowerPoint licenses cost money, but because the people building decks bill at $150 to $500 per hour. According to McKinsey research on knowledge worker productivity, professionals spend roughly 28% of their workweek managing information and communicating findings. For consultants, that communication overwhelmingly takes the form of slide decks.
The actual time allocation breaks down into identifiable phases:
Research and data gathering (30-40% of deck time). Before a single slide is created, the analyst must find, verify, and organize the data that will populate it. This means pulling from industry reports, financial databases, internal knowledge bases, and client-provided materials. For a market-sizing deck, research alone can consume a full day.
Structuring and outlining (15-20%). Organizing findings into a logical narrative, applying frameworks like the Pyramid Principle or SCQA, and determining which findings support which arguments. This is the most intellectually demanding phase and the one where senior consultants add the most value.
Slide creation and formatting (30-40%). The mechanical work: building slides in PowerPoint, aligning text boxes, formatting tables, creating charts, applying the brand template. This phase consumes disproportionate time relative to its intellectual content. The analyst already knows what the slide should say; the bottleneck is making PowerPoint render it correctly.
Review and revision (15-20%). Multiple review cycles with senior team members, each generating comments that require edits, additional data pulls, and reformatting.
For a typical 20-slide strategy deck, the total investment is 15 to 25 analyst-hours. At a blended internal cost of $75 to $100 per hour (salary, benefits, overhead), each deck costs the firm $1,125 to $2,500 to produce before any client billing. Multiply by the dozens of decks a team produces monthly, and the annual cost of slide building becomes a material line item.
The question is not whether AI can make this cheaper. The question is how much cheaper, and whether the quality holds.
The Evidence: What Research Actually Shows
Harvard Business School and BCG: The Landmark Study
In September 2023, researchers from Harvard Business School, Wharton, and MIT published what remains the most rigorous study of AI’s impact on consulting productivity. The experiment involved 758 BCG consultants performing 18 realistic consulting tasks using GPT-4.
The results were unambiguous:
- Consultants using AI completed tasks 25.1% faster than the control group.
- AI-assisted output scored 40% higher on quality as evaluated by independent graders.
- The performance improvement was largest for below-average performers, who saw a 43% quality increase, suggesting AI acts as a skill equalizer.
- Tasks included market analysis, creative ideation, persuasive writing, and data synthesis, all core activities in building consulting presentations.
The study also identified a critical nuance: AI was most effective when consultants remained actively engaged with the output, editing and refining rather than accepting generated content wholesale. Consultants who simply copied AI output without critical engagement produced lower-quality work than those who treated AI as a first-draft generator. This finding has direct implications for presentation workflows: the productivity gain comes from compressing the creation phase, not from eliminating human review.
McKinsey’s Lilli: Internal Deployment Data
McKinsey’s internal AI tool, Lilli, was deployed across the firm’s global consulting teams in 2023. While McKinsey has not published detailed productivity metrics, public statements from firm leadership indicate that Lilli reduced research time by approximately 30% for consultants who adopted it regularly. The tool primarily accelerated the research and data gathering phase by providing rapid access to McKinsey’s proprietary knowledge base of past engagement materials, published research, and frameworks.
The relevance for presentation ROI is direct: if research consumes 30-40% of deck creation time, and AI reduces research time by 30%, the net impact on total deck production time is a 9-12% reduction from research acceleration alone, before accounting for gains in the creation and formatting phases.
Enterprise AI Adoption: Market-Wide Data
Broader enterprise adoption data reinforces the consulting-specific findings. Forrester research reported that 67% of consulting firms were piloting or deploying AI tools for client deliverables as of 2025. IDC research found that knowledge workers spend approximately 23% of their time verifying AI-generated content before use, which represents both a productivity cost and a quality investment.
The MIT Technology Review’s analysis of enterprise AI adoption found that firms reporting measurable ROI from AI tools had one pattern in common: they deployed AI for specific, well-defined workflows rather than as a general productivity enhancer. Presentation creation, with its clear phases and measurable outputs, is exactly the type of well-defined workflow where AI consistently delivers returns.
Calculating Your Firm’s ROI
The framework for calculating presentation AI ROI is straightforward. You need three inputs and one subtraction.
Input 1: Current Hours Spent on Deck Creation
Survey your project managers or pull data from time-tracking systems. The relevant metric is total analyst-hours spent per week on presentation-related work across the team. Include research, structuring, slide building, formatting, and revision cycles.
For a team of 10 analysts, a typical range is 10 to 20 hours per analyst per week on presentation work. Use 15 hours as a conservative midpoint.
Team deck hours per week: 10 analysts x 15 hours = 150 hours/week
Input 2: Blended Analyst Cost Per Hour
Ask HR for the fully loaded cost per analyst-hour. This includes base salary, benefits, overhead, and office costs. For most consulting firms, this ranges from $75 to $100 per hour at the analyst level. Do not use the client billing rate; use the internal cost, since the ROI calculation is about recovered capacity, not direct revenue.
Weekly cost of deck work: 150 hours x $85/hour = $12,750/week
Annual cost of deck work: $12,750 x 50 weeks = $637,500
Input 3: Expected Efficiency Gain
Based on the Harvard/BCG study, a 25% efficiency gain is conservative for presentation-related tasks. This accounts for the fact that not all phases of deck creation are equally accelerable by AI. Research and slide creation see the largest gains (30-40%); review and strategic structuring see smaller gains (10-15%).
Annual hours recovered: 150 hours/week x 25% = 37.5 hours/week = 1,875 hours/year
Annual capacity recovered: 1,875 hours x $85/hour = $159,375
Subtraction: Tool Cost
Marvin’s Professional plan costs $20 per month per seat. For a team of 10:
Annual tool cost: 10 seats x $20/month x 12 months = $2,400
Net ROI
Annual net savings: $159,375 - $2,400 = $156,975
ROI multiple: $159,375 / $2,400 = 66x return on tool investment
Break-even point: The tool pays for itself when it saves the team approximately 28 hours per year, which is less than 3 hours per analyst per year, or roughly one deck produced 25% faster.
These are conservative figures. They assume only a 25% efficiency gain (the Harvard study measured 40% quality improvement on top of 25% speed improvement), they use the internal cost rate rather than the billing rate, and they do not account for the revenue upside of recovered billable capacity.
Where the Savings Come From
The aggregate ROI figure is useful for the business case, but understanding where the savings materialize helps with adoption planning.
Research Compression
AI with retrieval-augmented generation accelerates the research phase by 30-50%. Instead of spending hours searching industry databases, financial filings, and internal knowledge bases, the analyst queries the AI tool and receives cited findings in minutes. For a task that typically consumes 5-6 hours per deck, the savings are 1.5-3 hours per deck.
Marvin’s research pipeline retrieves data from curated sources and presents findings with citations, eliminating the manual search and verification cycle. The analyst’s role shifts from data gathering to data evaluation, which is a higher-value use of their time.
Slide Creation Acceleration
The mechanical work of building slides in PowerPoint is the most compressible phase. AI generates formatted slides that conform to the firm’s brand template, apply consistent formatting, and present one finding per slide with supporting data. The analyst reviews and refines rather than building from scratch.
For a 20-slide deck that previously required 6-8 hours of PowerPoint work, AI reduces this to 1-2 hours of review and refinement. This is where the 25% overall efficiency figure often understates the true impact: on pure slide construction, the time savings frequently exceed 50%.
Review Cycle Compression
AI-generated slides with embedded citations make review cycles faster. A reviewing partner can verify any data point by clicking through to the source, rather than asking the analyst to “check that number.” Questions about data accuracy are resolved in seconds rather than generating email threads that delay the next revision.
Each review cycle that previously consumed half a day can be compressed to two to three hours. For decks requiring two to three review cycles, this saves three to six hours per deck.
Quality-Driven Revenue Protection
The 40% quality improvement measured in the Harvard/BCG study has an indirect but significant revenue impact. Higher-quality deliverables increase client satisfaction, which drives repeat engagements and referrals. While this is harder to quantify than direct time savings, consulting partners consistently identify deliverable quality as a primary driver of client retention.
A single lost engagement due to a subpar deliverable can cost more than a year of AI tool subscriptions for the entire firm. While attributing engagement losses to deliverable quality is imprecise, the directional impact is clear: better decks contribute to better client relationships.
The Hidden Costs of NOT Adopting AI
The ROI calculation above measures the gains from adoption. Equally important are the costs of non-adoption, which compound over time and across the firm.
Lost Billable Capacity
Every hour an analyst spends on mechanical slide work is an hour not spent on billable strategic work. For a firm with 50 analysts, the annual cost of manual presentation work exceeds $3 million in internal costs. Even a 25% reduction frees $750,000 in capacity that can be redirected to revenue-generating work or used to pursue additional engagements without adding headcount.
The opportunity cost is particularly acute during peak periods. When the firm turns down an engagement because the team is at capacity, the lost revenue dwarfs the cost of any AI tool that could have freed the necessary bandwidth.
Talent Attrition
Top-performing junior consultants increasingly expect access to modern tools. A firm that requires analysts to spend 15 hours per week on manual PowerPoint work when competitors offer AI-assisted workflows will lose talent to those competitors. The replacement cost for a departing analyst is 1.5 to 2 times their annual salary when accounting for recruiting, onboarding, training, and the productivity ramp-up period.
For an analyst earning $85,000, the replacement cost is $127,500 to $170,000. If AI tool adoption prevents even one departure per year, the retention value alone exceeds the firm-wide tool cost by an order of magnitude.
Competitive Disadvantage
Firms that adopt AI tools can deliver faster turnarounds, pursue more engagements with the same headcount, and allocate more senior attention to each client because junior team members handle the mechanical work more efficiently. Over a 12 to 24 month period, this compounds into a meaningful competitive advantage in win rates, client satisfaction, and margin.
The competitive disadvantage of non-adoption is not visible in any single quarter. It manifests gradually as rivals win more pitches, deliver faster, and grow their practices while the non-adopting firm’s growth stalls at the capacity ceiling of manual workflows.
Margin Erosion
Client expectations for speed and quality continue to increase. Firms that rely on manual processes must either accept lower margins (more hours per deliverable at the same client fee) or charge higher fees (risking client attrition to more efficient competitors). AI adoption is increasingly a margin-preservation tool, not just a productivity enhancement.
Making the Business Case to Partners
Partners at consulting firms make technology adoption decisions based on three factors: financial return, risk mitigation, and competitive positioning. The business case for AI presentation tools should address all three.
The Financial Argument
Present the ROI calculation using your firm’s actual numbers. The framework above can be populated with data from your time-tracking system and HR’s cost figures. The key metrics to highlight:
- Annual savings per team: Use the formula (team hours/week on decks) x (blended cost/hour) x (25% efficiency gain) x (50 weeks) - (tool cost).
- ROI multiple: Divide annual savings by annual tool cost. For most firms, this exceeds 50x.
- Break-even timeline: Calculate how many hours of savings are needed to cover the annual tool cost. For Marvin at $20/month per seat, this is typically less than one week of use.
Support the financial argument with the Harvard/BCG study as independent, peer-reviewed evidence that AI delivers measurable productivity gains on consulting tasks.
The Risk Argument
Address the risks of adoption and non-adoption. The primary adoption risk is quality degradation if AI outputs are not properly reviewed. Mitigate this by establishing review protocols and choosing tools with citation-first architecture that enables rapid verification.
The non-adoption risks are larger: talent attrition, competitive disadvantage, and margin erosion. Frame AI adoption as risk mitigation, not just productivity enhancement.
The Competitive Positioning Argument
67% of consulting firms are already piloting or deploying AI tools for client deliverables. Early adoption creates advantage; delayed adoption requires catching up. Present the competitive landscape: which of your direct competitors have announced AI tool deployments? What do client RFPs increasingly ask about your firm’s use of technology?
The Pilot Proposal
Partners are more likely to approve a pilot than a firm-wide rollout. Propose a 90-day pilot with a single team, measuring three metrics: hours spent on deck creation (before and after), deliverable quality scores from client feedback, and analyst satisfaction. Define success criteria in advance: if the pilot team shows a 20%+ reduction in deck creation time with maintained or improved quality, recommend firm-wide rollout.
The pilot structure reduces the perceived risk of the decision while generating firm-specific data that makes the broader business case irrefutable. At $20 per seat per month, the financial commitment for a 10-person pilot is $600 over 90 days, an amount that requires no formal procurement process at most firms.