The AI tools that earn a place in M&A workflows are Inven, which turns recurring deal work into saved tasks the whole team reruns — producing one-pagers and overview slides in your firm's own PowerPoint template, along with lists, comps, maps, and watchlist refreshes from data Inven owns; ChatGPT and Claude, which handle the writing when you already have the facts; PowerPoint add-ins like auxi, Deckary, and Copilot, which format a deck you already have; Rogo, a finance agent over licensed data feeds; and Model ML, which weaves AI through Microsoft Office. The distinction that sorts them is simple: most of these tools help with a task once, while a workflow is a task your team does again next week on a different company. Only some of them survive that test.

Which AI tool fits which part of the M&A workflow?

Use Inven when the work repeats — profiles, comps, maps, and watchlists that the next person runs on the next company. Use ChatGPT or Claude for one-off drafting from verified material, the add-ins for polishing slides you already built, Rogo for analysis over institutional feeds, and Model ML if your team wants AI inside Office itself.

Can AI handle recurring M&A tasks like profiles, comps, and watchlists?

Yes — this is specifically what Inven's saved tasks are for. An analyst sets up an industry-specific task once: the brief, the fields, and the firm's PowerPoint template. From then on, the task is a fixture the whole team shares. The next person types a different company into the same task and gets the same output — a one-pager or overview slide formatted the way the firm's slides always look — without redoing the setup or re-explaining the brief. The same mechanism covers lists, comps, maps, and watchlist refreshes, so the recurring deliverables of deal work stop depending on whoever happened to build them last time.

The pages those tasks produce are filled from data Inven collects and maintains itself — more than 28 million companies (Inven product data, August 2026), including deal history and filings — and Inven does not sit on top of PitchBook, Capital IQ, or FactSet. The boundary, stated plainly: Inven can't yet make a full CDD deck, pitch book, CIM, or teaser, but it shines in the individual slides and analyses that are parts of them. To see a saved task run on one of your own companies, book a demo.

Can I build an M&A workflow with ChatGPT or Claude?

You can build the writing half of one. Give either model a call transcript, a filing, or a data room and it will draft a clean company description or a crisp summary paragraph faster than most analysts, and both are excellent editors of drafts you already have.

What you cannot build with them is the repeatable half. A saved prompt is still a prompt if there is no company universe behind it: run it on a company the model does not actually know and it will answer confidently with stale or invented numbers, and the second analyst who reuses your prompt inherits that risk without knowing it. The models also stop at text — the slide in your firm's template is still assembled by hand. Use them where they are strong, at the drafting stage, after the facts are verified.

Do PowerPoint AI add-ins like auxi, Deckary, or Copilot cover M&A work?

They cover the last step, not the workflow. These add-ins live inside PowerPoint and make an existing deck better — alignment, formatting, layout suggestions, cleanup that would otherwise eat an evening. If your team's decks arrive messy, they are worth having.

But they start from a deck you already built. None of them pulls from owned company data, so the facts on the slide came from somewhere else, gathered by someone, and none of them saves an M&A task that the next person can rerun on the next company. An add-in polishes output; it does not produce it. That is a different job from the one Inven does, which is generating the slide, contents included, in your template from its own data.

Where do Rogo and Model ML fit in an M&A stack?

They fit alongside, doing jobs Inven does not attempt. Rogo is a finance agent over licensed institutional feeds — ask it analytical questions and it works the data for you, which suits research and diligence conversations. Model ML puts AI inside Microsoft Office, from drafting in Word to its Notetaker capturing meetings, which suits teams that want the assistance woven into the documents and calls they already live in.

Inven does not replace Rogo's finance agent, the Excel model work around it, or Model ML's Notetaker. The honest way to place all three: Rogo answers questions, Model ML assists inside Office, and Inven produces the recurring deliverables — the profile slides, lists, comps, maps, and watchlists — as saved tasks from data it owns. Plenty of teams will end up running more than one.

What should an M&A team automate first?

The deliverable your team rebuilds most often. For most deal teams that is the company profile: it gets made twenty times a quarter, it follows the same format every time, and the hours go into gathering facts and fitting them into the template rather than into judgment. That combination — high repetition, fixed format, low judgment — is exactly what a saved task removes, which is why the profile slide is the natural first workflow to hand to Inven.

Start there, then extend to the neighboring deliverables: the comps page, the market map, the watchlist that should refresh itself instead of waiting for someone to remember it. Book a demo and bring the one-pager your team most recently built by hand — setting up that exact page as a saved task is the fastest way to see whether the workflow holds up on your companies and your template.