Person
1 episodes · 11 quoted claims
Tomer Slaney said a four-person transformation office team studied by McKinsey spent 80% of its time on reporting, creating materials, preparing for meetings and follow-ups.
“One example team that we looked at four person team. How are they spending their time? And 80% of that time is spent on reporting, creating materials, preparing for meetings, doing the follow-ups”
Tomer Slaney said that in long-running transformations, offices are expected to shrink but instead gain more data sources, which increases their reporting workload.
“The expectation is that these transformation offices get smaller and they get more efficient. But in fact, what we actually see is they actually start getting access to more and more data sources”
Tomer Slaney said an AI meeting-transcript tool turned a sarcastic remark by a chief transformation officer into an action item for the CEO to call every employee, which was caught by a human reviewer.
“the chatbot picked up an action CEO to call every employee in the company, tell them to do their job.”
Tomer Slaney said transformations typically run on a weekly cadence in which data is pulled and then prepared for an executive committee meeting.
“data is pulled every week. We then look at the famous week over week change.”
Tomer Slaney said his teams may use any risk-approved tools, but anything put on a page is the author's own responsibility.
“if you put something on a page, it's yours.”
Tomer Slaney said the WAVE AI team built WAVE apps, letting teams work with transformation data in WAVE without repeated data extracts and with visibility set to match the audience.
“the wave AI team has built to start building something called wave apps, which allows allow this is it's actually designed for this.”
Tomer Slaney said a four-person team built four main agents covering materials creation, admin and checks, freed up an individual's worth of time, and shrank while doing more value-adding work.
“that team developed four main agents that they were using from materials creation, the admin, the checks.”
Tomer Slaney described a nightly agent that reviews all plans in a transformation, identifies what changed the previous day and flags expected follow-on changes that did not happen.
“Every night we have an agent that runs on a transformation, that transformation plugs into your performance in the infrastructure, or you can give it a download.”
Tomer Slaney said deck-drafting agents can read prior decks, finance reports and revenue reports and flag inconsistencies without changing them.
“it will flag, not necessarily change, it will flag where there are inconsistencies.”
Tomer Slaney said teams that switched to a newer large language model saw worse results because their workflows had been built around the earlier model.
“when teams have gotten, you know, well, we had we. We now have access to a newer, fancier LLM, have actually, um, seen a regression in what they, what they get.”
Tomer Slaney described a team in year two of a transformation that said it was more excited about its work because it no longer spent much time copy-pasting data into presentations.
“and realizing that a lot of that is possible because they are not spending forty five minutes of every hour copy pasting materials from data dumps into presentations.”