Can AI Really Save You a Day a Week? The Evidence Says Yes.

A research-backed look at where senior leaders lose time, and how much AI can give back.

Karl George MBE · Founder, The Governance Forum & Governance AI

If you are a director or senior leader in any organisation, you already know the feeling. You arrive on Monday morning with a clear intention to focus on strategy, stakeholder relationships, and the work that only you can do. By lunchtime, you have spent three hours on email, prepared for two meetings, and not yet started on the board paper due on Thursday.

The question is not whether this pattern exists. The research is clear that it does. The question is whether AI can meaningfully change it, and if so, by how much.

Having spent the last twelve months working with boards and senior leaders on responsible AI adoption through The Governance AI Journey, I wanted to put some honest, conservative numbers around the opportunity. Not the breathless claims you read in vendor marketing, but a grounded estimate rooted in credible research and practical experience.

Where does the time actually go?

Before we talk about what AI can save, it is worth understanding where the time is lost. The research paints a consistent picture.

McKinsey Global Institute found that the average knowledge worker spends 28% of their week managing email, approximately 11 hours, and nearly 20% searching for internal information or tracking down colleagues. IDC puts the information-searching figure even higher, at around 2.5 hours per day. The University of North Carolina found that 65% of senior managers said meetings interrupted their workflow, while 71% considered them unproductive. And The Economist calculated that knowledge workers lose 127 hours per year simply regaining focus after interruptions.

Add it up, and the typical senior leader has perhaps seven to ten hours per week of genuinely uninterrupted time for strategic thinking, relationship building and the high-judgement work that actually requires their expertise. Everything else is consumed by the machinery of modern work before they even begin.

The point is not that people are unproductive. It is that the structure of modern work consumes time before people can apply their expertise.

What does AI actually save?

The productivity evidence is now credible and growing. The Federal Reserve Bank of St. Louis, drawing on nationally representative U.S. survey data from late 2024, found that generative AI users save an average of 5.4% of their working hours, equivalent to approximately 2.2 hours per week. Among more intensive users, the picture is stronger: the Gartner Supply Chain Survey found individual desk-based workers saved 4.11 hours per week, while daily users in the Federal Reserve study reported savings of 4 or more hours per week (33.5% of daily users). Thomson Reuters, surveying over 2,200 professionals, found respondents predicted AI would save them four hours per week immediately and 12 hours per week within five years.

The Microsoft and LinkedIn Work Trend Index 2024, surveying 31,000 people across 31 countries, reported that 90% of AI users said the technology saves them time, with power users saving over 30 minutes per day.

These are not hypothetical projections. They are measured outcomes from people already using the tools.

The estimated hours: task by task

Drawing on this evidence base, I have built a conservative estimate of what a senior leader could expect to save per week if they adopted AI across their core activities. The table below uses a low, base and high range, with assumptions stated clearly.

A note on methodology: These estimates are based on general knowledge-worker data. No sector-specific time-use studies exist for UK directors. Actual savings depend on individual work patterns, AI proficiency and task mix. The base case assumes moderate, consistent use of AI across multiple task categories. The high case assumes full integration into daily workflows. I have deliberately erred on the conservative side.

6.5 hours. Almost a full working day.

The base case of 6.5 hours per week is not a fantasy figure. It sits comfortably within the range reported by the Federal Reserve, Gartner and Thomson Reuters research. For a senior leader who integrates AI into their daily email, meeting preparation, report writing and research routines, this is an achievable and realistic target.

To be clear, the low end (3 hours per week) is what you would expect from someone using AI occasionally for a few tasks. The high end (11 hours) represents someone who has fully embedded AI into their workflow with proper templates, a personal context document, and established routines. Most people I work with land somewhere in the middle within their first month.

This is not about replacing the leader. It is about giving them back the time for the high-value work only they can do: strategic thinking, relationship building, culture, and governance oversight.

The real multiplier: GPTs, Skills and reusable workflows

One of the things I consistently see is that people underestimate the “template-based outputs” category in the table above. They think of AI as something you prompt from scratch each time. In practice, the real productivity gain comes from systemising your repeatable work, and the platforms are now making this remarkably easy.

In ChatGPT, you can build a custom GPT: a tailored version of the model pre-loaded with your context, instructions, tone preferences and document structures, ready to produce a specific type of output every time. In Claude, the equivalent is a Skill or a Project, where you store your templates, business information, branding rules and standing instructions so that every output is consistent and bespoke without you having to brief the AI from scratch. You build it once, and it works for you every time.

Consider how many times a week you produce a version of the same thing: a board paper, a stakeholder briefing, a team update, a proposal, a speaker biography. Each time, you start from a previous version, manually adapt it, and spend 30 to 60 minutes producing something that is broadly similar to what you produced last time.

With a custom GPT or Claude Skill, you provide the raw inputs (a few bullet points, some notes, the audience and context) and AI produces a polished, bespoke version in minutes, using your preferred structure, tone and format every time. The two-part approach, your inputs plus AI’s drafting, means the quality goes up while the time comes down. It is not a small gain; it is a structural change in how you work.

And it is only going to get better

The time savings in the table above reflect where AI is today, and today’s tools are the least capable they will ever be. What is coming next will make the current generation of AI assistants look like a starting point.

The shift is best understood through three lenses: automation, augmentation and agency.

Automation is where most organisations start, and where the table above sits. AI takes a task you do repeatedly, such as drafting an email, summarising a document, or producing a first draft of a report, and completes it faster than you could manually. You still review, approve and send. The human is in the loop at every step.

Augmentation goes further. AI does not just draft; it enhances your thinking. It spots patterns in data you would not have time to analyse, surfaces risks you had not considered, connects information across documents you would never have read side by side, and generates options you had not thought of. This is where AI becomes a genuine thinking partner, not just a drafting assistant. Tools like Claude’s deep research capability and ChatGPT’s data analysis features are already moving into this territory.

Agency is the frontier. Agentic AI refers to systems that can take a goal, break it down into steps, execute those steps across multiple tools and data sources, and come back to you with a completed outcome rather than a single draft. Think of the difference between asking AI to “draft a board paper” (automation) and asking it to “prepare for next Tuesday’s board meeting: summarise the papers, flag the decisions required, draft my briefing note, prepare questions for each agenda item, and send a calendar reminder with the pack attached” (agency). The AI plans, acts, checks and delivers, with you providing oversight and approval at key points.

We are already seeing early versions of agentic AI in tools like Claude’s computer use and coworker features, ChatGPT’s custom GPTs with actions, and Microsoft Copilot’s integration across the Office suite. Within the next 12 to 18 months, these capabilities will mature rapidly. The 6.5 hours per week in the table above will look modest by comparison.

We are moving from AI as a drafting tool to AI as a capable colleague: one that can plan, research, draft, check and deliver, with you providing the judgement and the final sign-off.

This is precisely why getting set up properly now matters so much. The leaders who build their personal context, establish their templates as GPTs or Skills, and develop their AI literacy today will be the ones best positioned to benefit from agentic AI when it arrives at scale. The learning curve is not going to get easier; the tools are going to get more powerful. Starting now, even with simple use cases, builds the muscle memory and the governance frameworks that will be essential when AI can do significantly more.

But it only works if you set up properly

The research is equally clear on this point: the difference between someone who saves two hours a week and someone who saves eight is not intelligence or technical ability. It is setup. The Microsoft Work Trend Index found that “power users” who save the most time share specific habits: they build personal context into their AI tools, they establish routines, they create reusable templates, and they approach every task by asking “could AI help with this?”

This is exactly why The Governance AI Journey begins with a structured setup process. In Module 1 (the AI Wake-Up Call Workshop), we do not just raise awareness; we get people started. And in Module 2 (the GovernIQ™ Diagnostic), we identify individual development needs so that each person’s setup reflects their actual capability gaps, not a generic checklist.

What about the risks?

Every honest conversation about AI time savings has to acknowledge the guardrails. AI does not replace judgement. It does not eliminate the need to read your own board papers. It does not remove accountability for accuracy. Every AI output needs human review before it is sent, published or relied upon.

The organisations getting this right are the ones that treat AI as a drafting assistant with clear boundaries, not as an autopilot. They have simple policies in place, staff know what they can and cannot share with AI tools, and there is a named person responsible for oversight. None of this is complicated. It just needs to be done deliberately.

The bottom line

If you are a senior leader spending your weeks consumed by email, meeting preparation, report writing and routine admin, the evidence now says clearly that AI can give you back between three and eleven hours per week, with a realistic base case of around 6.5 hours. That is almost a full working day, every week, recovered for the work that actually requires your expertise, experience and judgement.

And this is just the beginning. As we move from automation through augmentation to agentic AI, the savings will compound. The leaders who set themselves up properly today, who build their GPTs and Skills, establish their governance frameworks and develop their AI literacy, will be the ones who benefit most as the technology matures.

The question is no longer whether AI works. It is whether you are set up to benefit from it, and whether you are ready for what comes next.

Ready to find your 6.5 hours?

The Governance AI Journey is a seven-module programme that helps boards and senior leaders adopt AI responsibly, starting with awareness and building to accreditation. It begins with the AI Wake-Up Call Workshop.