McKinsey wonders how to sell AI apps with no measurable benefits
theregister.com
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The rank and file generally have a really good grasp on their subset of the domain -- they have expertise and experience, as well as local context. Small teams, their managers -- those are the ones who actually perform, and deliver value.
As you move up the hierarchy, access to information does not scale. People in the middle are generally mediocre performers, buried in process, ritual and politic. In addition to these burdens, the information systems do their best to obscure knowledge, with the usual excuses of Safe and Secure (tm) -- things are siloed, search does not work, archives are sunsetted, etc.
In some orgs tribalism also plays an outsized role, with teams acting competitive, which largely results in wasted resources and seven versions of the same failed attempt at New Shiny Thing.
Then as we look higher yet in the hierarchy, the so-called decision makers don't really do anything that cannot be described as "maximize profit" or "cut costs", all while fighting not to get pulled down by the Lord of the Flies shenanigans of their underlings. They are the most replaceable.
A successful "AI Transformation" would come in top-down, going after the most expensive headcount first. Only truly valuable contributors would remain at that level. Organizational knowledge bases would allow to search, analyze and reason about the institutional knowledge accrued in corporate archives over the years, enabling much more effective decision making. Meanwhile, the ICs would benefit from the AI boost, outsourcing some menial tasks to the machine, with the dual benefit of levelling up their roles, and feeding the machine more context about the lower-level work done across the org.
> A successful "AI Transformation" would come in top-down, going after the most expensive headcount first.
This isn't a mistake. McKinsey consultants and their executives at their clients are a part of the same clique. You don't get into either without going to the right schools, being in the right fraternities, and knowing the right people. "Maximize profit" and "cut costs" are to be read as "keep the most money for ourselves in the form of earnings per share and dividends" and "pay fewer people". And since you can convert shares to money by gutting companies, there's no real incentive to remain competitive in the greater marketplace.
When I look at top-level decision-makers at my Mag-7 employer, they are smart people. Many of them were go-getters in their earlier career, responsible for driving some very successful initiatives, and that's why they're at the top of the company. And they're very intentional about team structure: being close enough to senior directors and VPs to see some of their thinking, I can tell that they understand exactly who the competent people are, who gets things done, who likes to work on what, and then they put those people at the bottom of the hierarchy with incompetent risk-averse people above them. Then they'll pull them out and have them report directly to a senior person when there's a strategic initiative that needs doing, complete it, and then re-org them back under a middle-manager that ensures nothing gets done.
I think the reason for this is that if you have a wildly successful company, the last thing you want to do is screw it up. You're on top of the world, money is raking in from your monopoly - and you're in zugzwang. Your best move is not to play, because any substantive shift in your product or marketplace risks moving you to a position where you aren't so advantaged. So CEOs of successful companies have a job to do, and that job is to ensure that nothing happens. But people's natural inclination is to do things, and if they aren't doing things inside your company they will probably be doing things outside your company that risk toppling it. So you put one section of the company to work digging holes, and put the other section to work filling them in, and now everybody is happy and productive and yet there's no net external change to your company's position.
Why even have employees then? Why not just milk your monopoly, keep the team lean, and let everybody involved have a big share of the profits? Some companies do actually function like this, eg. Nintendo and Valve famously run with fairly small employee counts and just milk their profits, some HFT trading shops like RennTech just give huge employee dividends and milk their position.
But the problem is largely politics. For one, owning a monopoly invites scrutiny; there are a lot of things that are illegal, and if you're not very careful, you can end up on the wrong side of them. Two, owning an incredibly lucrative business makes you a target for competition, and for rule-changes or political action that affect your incredibly lucrative business. Perhaps that's why examples of highly-profitable businesses that stay small often involve staying secret (eg. HFT) or being in an industry that everybody else dismisses as inconsequential (eg. gaming or dating).
By having the huge org that does nothing, the CEO can say "Look, I provide jobs. We're not a monopoly because we have an unfair advantage, we compete fairly and just have a lot of people working very hard." And they can devote a bunch of people to that legal compliance and PR to make sure they stay on the right side of the government, and it also gives them the optionality to pull all those talented people out and unmuzzle them when there actually is a competitive threat.
https://www.ribbonfarm.com/2009/10/07/the-gervais-principle-...
So we're seeing this play out. There are two factors that exist in tension here:
- The valuation of many of these companies depend on the perception that they are The Future. Part of that is heavy R&D spending and the reputation that they hire The Best. Even if the company mostly just wants to sit and milk its market position, keeping the stock price afloat requires looking like they're also innovative and forging the future.
- Some companies are embracing the milk-it-for-all-its-worth life stage of their company. You see this in some of the Mag-7 where compensation targets are scaling down, explicit and implicit layoffs, etc. This gear-shifting takes time but IMO is in fact happening.
The tightrope they're all trying to walk is how to do the latter without risking their reputation as the former, because the mythos that they are the engines of future growth is what keeps the stock price ticking.
i work at a large music streamer and this perfectly describes my workplace. when i was outside i never understood why that company needs thousands and thousands of ppl to run what looks like stagnant product that hasn't changed much in years.
Do you still need an "AI Transformation" then? Sounds like just axe the CEO or cut their enormous salary = profit?
But particularly we're always dealing with IT security "experts" running dumb checklists taking things away and breaking everything and never bothering figure out how we're supposed to use computers to actually get any work done ("hmmm. we didn't think about that... we'll get back to you" is a common response from these certified goons). Apparently the security gods have decided we can't have department file servers anymore because backups are too difficult to protect against ransomware or something so we're all distracted with that pronouncement from the mountain trying to figure out how to get anything done at the moment.
I said before but in a pre-2000 world when I worked at a public company I got lectured when my employees timecards utilization were too high because you can't actually utilize people at those levels full time continuously and management wouldn't sign off on incorrect timecards. But the modern world pretends it's fine and is trying to optimize to it and it just won't work. You can't optimize out the key knowledge, and you can't keep the key knowledge and be 80% utilized doing something else.
This is all based on some ideal world that doesn't exist in reality.
I worked in tech support at a big company long ago. Tech support, sales, and engineering used to have a week (for each employee) where we would leave our team and follow the other team around.
It provided incredible efficiency. I now knew what sales was talking about when they called me, they understood how I worked, engineering and I got along so well they used to invite me to their team when they had lunch catered.
Who didn't we need anymore? The middle managers between the groups who brokered what info each group could see, and how we communicated among groups. We solved problems before they started all on our own.
The middle managers won in the end, ending the cross training, too costly they said, but I think they realized that we just didn't need them / weren't engaging them anymore...
I'm surprised McKinsey convinced someone to say the quiet part out loud
- AI companies of course will try and sell you that you can reduce headcount with AI
- CEO's will parrot this talking point without ever talking a closer look.
- Everyone lower down on the org chart minus the engineers are wondering why the change hasn't started yet.
- Meanwhile engineers are ripping their hair out cause they know that AI in it's current state will likely not replace any workers.
Pretty soon we will have articles like "That time that CEO's thought that AI could replace workers".
But managers will not obsolete themselves.
So right now AI should be used to monitor and analyze the workforce and find the efficiency that can be achieved with AI.
I am not kidding. In any large corps, the decision makers refuse to take any risks, show no creativity, move as a flock with other orgs, and stay middle-of-the-road, boring, beige khaki. The current AIs are perfect for this.
It's hard to take this sentiment seriously from a source that doesn't have direct experience with the c-suite. The average person only gets to see the "public relations" view of the c-suite (mostly the CEO) so I can certainly see why a "LLM based mouthpiece" might be better.
The c-suite is involved in thousands of decisions that 90% of the rest of the world is not privy to.
FWIW - As a consumer, I'm highly critical of the robotic-like external personas the c-suite take on so I can appreciate the sentiment, but it's simply not rooted in any real experience.
That is exactly what it can't do. We need someone to hold liable in key decisions.
....hey, wait a sec....
No one wants to say on their resume, "I manage 5 people, but trust me, with AI, its like managing 20 people!"
Managers also don't pay people's salaries. The Tech Tools budget is a different budget than People salaries.
Also keep in mind, for any problem space, there is an unlimited number of things to do. 20 people working 20% more efficiently wont reach infinity any faster than 10 people.
In my previous company, we would speculate about where to use AI and we were never sure.
In the new company we use AI for everything and produce more with substantially fewer people
AI (LLMS) act as a pre-filter, auto-approving or auto-rejecting before they get to the humans for review.
I don't mean to be dismissive and crappy right out of the gate with that question, I'm merely drawing on my experience with AI and the broader trends I see emerging: AI is leveraged when you need knowledge products for the sake of having products, not when they're particularly for something. I've noticed a very strange phenomenon where middle managers will generate long, meandering report emails to communicate what is, frankly, not complicated or terribly deep information, and send them to other people, who then paradoxically use AI to summarize those emails, likely into something quite similar to what was prompted to be generated in the first place.
I've also noticed it being leveraged heavily in spaces where a product existing, like a news release, article, social media post, etc. is in itself the point, and the quality of it is a highly secondary notion.
This has led me to conclude that AI is best leveraged in such cases where nobody including the creator of a given thing really... cares much what the thing is, if it's good, or does it's job well? It exists because it should exist and it's existence performs the function far more than anything to do with the actual thing that exists.
And in my organization at least, our "cultural opinion" on such things would be... well if nobody cares what it says, and nobody is actually reading it... then why the hell are we generating it and then summarizing it? Just skip the whole damn thing and send a short, list email of what needs communicating and be done.
He's either lying or hard-selling. The company in his profile "neofactory.ai" says they "will build our first production line in Dallas, TX in Q3." well, we just entered Q4, so not that. Despite that it has no mentions online and the website is just a "contact us" form.
That's what I've wondered. We don't just run out of work, products, features, etc. We can just build more but so can the competition right?
Ding ding ding!
AI can absolutely reduce headcount. It already could 2 years ago, when we were just getting started. At the time I worked at a company that did just that, succesfully automating away thousands of jobs which couldn't pre-LLMs. The reason it ""worked"" was because it was outsourced headcount, so there was very limited political incentive to keep them if they were replaceable.
The bigger and older the company, the more ossified the structures are that have a want to keep headcount equal, and ideally grow it. This is by far the biggest cause of all these "failed" AI projects. It's super obvious when you start noticing that for jobs that were being outsourced, or done by temp/contracted workers, those are much more rapidly being replaced. As well as the fact that tech startups are hiring much less than before. Not talking about YC-and-co startups here, those are global exceptions indeed affected a lot by ZIRP and what not. I'm talking about the 99.9% of startups that don't get big VC funds.
A lot of the narrative on HN that it isn't happening and AI is all a scam is IMO out of reasonable fear.
If you're still not convinced, think about it this way. Before LLMs were a thing, if I asked you what the success rate of software projects at non-tech companies was, what would you have said? 90% failure rate? To my knowledge, the numbers are indeed close. And what's the biggest reason? Almost never "this problem cannot be technically solved". You'd probably name other, more common reasons.
Why would this be any different for AI? Why would those same reasons suddenly disappear? They don't. All the politics, all the enterprise salesmen, the lack of understanding of actual needs, the personal KPIs to hit - they're all still there. And the politics are even worse than with trad. enterprise software now that the premise of headcount reduction looms larger than ever.
Trucks in the oil sands can already operate autonomously in controlled mining sites, but wide adoption is happening slowly, waiting for driver turnover and equipment replacement cycles.
I don't know, most of the companies doing regular layoffs wheneveer they can get away with it are pretty big and old. Be it in tech - IBM/Meta/Google/Microsoft, or in physical things - car manufacturers, shipyards, etc.
The execs aren't the ones directly choosing, overseeing and implementing these AI efforts - or in the preceding decades, the software efforts. 9 out of 10 times, they know very little about the details. They may ""spearhead"" it in so far that's possible, but there's tonnes of layers inbetween with their own incentives which are required to cooperate to actually make it work.
If the execs say "Whole office full-time RTO from next month 5 days a week", they really don't depend on those layers at all, as it's suicide for anyone to just ignore it or even fake it.
which company is this? surely they wouldve made a big splash for doing something no one else has been able to do.
This was a big, traditional non-tech company.
Also as implied, these were cheap offshore contracting jobs being replaced. Still magnitudes more expensive than LLMs, making it very "worth it" from a company perspective. But not prime earnings call material.
Everyone in the industry also knows that it's not particularly unique, far away from something no one has been able to do. Go look at the job markets for translation, data entry, customer support compared to 2 years ago. And as mentioned, even junior web devs.
I mean, nah, we've seen enough to these cycles to know exactly how this will end.. with a sigh and a whimper and the Next Big Thing taking the spotlight. After all, where are all the articles about how "that time that CEOs thought blockchain could replace databases" etc?
Yup, it's just the latest management fad. Remember Six Sigma? Or Agile (in its full-blown cultish form; some aspects can be mildly useful)? Or matrix management? Business leaders, as a class, seem almost uniquely susceptible to fads. There is always _some_ magic which is going to radically increase productivity, if everyone just believes hard enough.
This is a puzzling assertion to me. Hasn’t even the cheapest Copilot subscription arguably replaced most of the headcount that we used to have of junior new-grad developers? And the Zendesks of the world have been selling AI products for years now that reduce L1 support headcount, and quite effectively too since the main job of L1 support is/was shooting people links to FAQs or KB articles or asking them to try restarting their computer.
If you ask me, that's the real long game on AI. That is exactly why all these billionaires keep pouring money in. They know it's the only way to continue growth is to start taking over large sections of the economy.
Classic enshittification combined with embedding internally to company operations to become indispensable.
The quiet part out loud phrase is overused.
Each human can be a bit more productive, I fully believe 10-15% is possible with today's tools if we do it right. But each human has it unique set of experience and knowledge. If I do my job a bit faster, and you do your job a bit faster. But if we are a team of 10, and we do all our job 10% faster, doesn't mean you can let one of us go. It just means, we all do our job 10% faster, which we probably waste by drinking more coffee or taking longer lunch breaks
I have to admit, the results they demonstrated — which we validated using our own data — were impressive.
The challenge, however, is that outcome-based contracts are hard for companies to manage, since they still need to plan and budget for potential costs upfront.
So even when you have measurable benefits - it's still not so easy either.
EDIT:
To clarify the issue — companies are used to budgeting for initiatives with fixed costs. But in an outcome-based contract, the cost is variable.
As a result, finance teams struggle to plan or allocate budgets because the final amount could range widely — for example, $200K, $2M, or even $20M — depending on the results achieved.
Additionally, you almost then need a partial FTE just to manage these contracts to ensure you don't overpay because the results are wrongly measured, etc.
None of these challenges are insurmountable, but it's also not easy for companies either.
You model it as a fixed %, variable cost and run revenue sensitivities. It either meets your investment criteria or doesn't.
If the company doesn't have the resources available to execute something they've validated, then that's a funding issue that can be solved.
Either way, McK's structure doesn't make it "hard for a company to manage." The investment committee approves or rejects.
e.g., complaining about having to provision an FTE to manage the earnout doesn't make sense because that should be in the business plan considered for approval. You'd only approve if your NPV is positive, including the FTE overhead.
Either
- the execs are leaving a laughably easy 20m on the table McKinsey knew they'd make (how did they know, and why didn't we)
- they're dealing with insider information - especially dangerous if McKinsey is changing dependencies around.
- they're doing some creative accounting
I'd be surprised if they'd do that for GenAI projects, maybe only for really good clients that pay them 50mln+ a year anyway
Same reason they ask for "estimates" which they then later try to hold accountable as "quotes" when it suits them. Same reason I 3x my initial estimates.
Now maybe Notion customers love all these AI features but it was super weird to see that stuff so prominently given my understanding of what the company was all about.
But I'm guessing their growth was linear, and hard fought, after initial success over tools like Atlassian's which are annoying and expensive.
So to get back to hypergrowth, they had to stuff AI in every nook and cranny.
Is your product a search engine? It's AI now. [1][2]
Is it a cache? Actually, it's AI. [3]
A load balancer? Believe it or not, AI. [4]
(And I develop "AI" tools at my day job right now...)
"Only" 30%. Interesting framing.
Very successful in my domain on a very successful project.
I wrote an insane amount of code but more importantly I wrote libraries across multiple languages that prevented an insane amount of code from being written.
We would have literally 1k people during quarterly planning and did distributed agile and all this org stuff
(It was interesting anthropologically to me because I operated outside the game, I was just waiting for a ~non-compete I had signed for a profitable technical co-founder exit to end to jump back into starting a new company in the same space.)
And the whole thing worked, and I was very high profile on the project as probably the highest paid IC and the company hired me away from the agency and I worked their until starting my company.
There are 3 layers, the deal makers, the coordinators and the implementers.
You cannot easily automate out the deal makers because they are trust, legal/contracting and power (they use the resources of the firm to allocate: people, resources, etc.) loci. Someone has to hang if stuff goes wrong and someone has to deal with executive petulance and fragile egos.
Now lets look at the middle layer and implementers.
Let's assume for a minute we are looking at a big project where the existing company has hamstrung itself with silos and infighting and low productivity teams, this is just framing to understand the next part, it can cut either way.
The middle layer in consulting is big because other companies have big middle layers as well, and basically what is happening is tribal warfare, you need bodies and voices and change management teams to propagate what is happening otherwise the existing group will slow play the leadership and the project never gets done. If the middle layer is 1 to 1 every native sees they can be replaced. Many big 4 allow poaching for this very reason. The threat of non compliance and then also giving an easy congenial out for people who are ready to exit the consultant lifestyle.
The implementation layer is then able to be done, and it's done by juniors mostly because juniors don't have to be politically savvy, they can work to task.
Just a small slice or things I realized while consulting.
https://www.mckinsey.com/industries/technology-media-and-tel...
> The firm’s earlier research suggested that 2027 would be the first year when AI technology would be able to match the typical human’s performance in tasks that involve “natural-language understanding.” Now, McKinsey reckons it will happen this year."
> "Generative AI will give humans a new “superpower”, and the economy a much-needed productivity injection, said Lareina Yee, a senior partner at the firm and chair of McKinsey Technology, in the report.
All of this said, using AI in your back end takes a huge amount of time from your users and employees. You have to vary multiple prompts, you have to make the output sane, touch it up, etc. The most useful part of AI for me has been using it to learn something new, or push through a task that I otherwise couldn't do. I was able to partially rewrite a logging window to reduce CPU use significantly. It took me over two weeks of back and forth with AI to figure out a workable solution and implement it into the software. I competent programmer probably could have done it better than I did in less than an hour. There's no business benefit to a help desk person being able to spend 2 weeks writing code that an engineer would be much better suited to handling. But maybe that engineer could write it in 10 minutes instead of an hour if they used AI to understand the software first.
Likely, no. In my industry, I see a fraction of ICs using it well, a fraction of leadership using it for absolute dog shit idea generation, and the remainder using it to make their jobs easier in the short run, while incurring debt in the long run since nobody is "learning" from AI summaries and most people don't seem to be reading the generated "AI notes" sent in emails.
By and large, I think AI is going to hurt my workplace based on the current trajectory, but it won't be realized until we are in a hard hole to dig out of.
Really companies failed to make remote work because it meant giving up a lot of middle-management power and some people realising their job means very little.
They found a hack and that is loading up the intangible column. In that list reputation/brand risk always makes an appearance. "If you don't do this project this terrible thing might happen and we might suffer reputational risk. We estimate 2x millions of loss due to reputation being harmed". And presto! there is a case for the project.
Its like cloud migration project. Total run costs are part of the pitch but you add on extra things like added security, automatic updates etc. it becomes an easier sell.
AI tools being so hyper focused on "productivity gains" it is going to be tough sell. Especially because users will resist it and the productivity boosts if any will remain low.
From what I know of the firm, it looks like clients have come to the right place if they want a consultant with great experience at hiking prices without cutting costs or boosting productivity.
All this is pretty textbook setup for how this bubble finally implodes as companies fail to deliver on their AI investments and come under fire from shareholders for spending a ton with little return to show for it.
Additionally once the AI vendors have locked these companies to their ecosystem, the enshittification will start and the companies who reduced their headcount to bare minimum will start to see why that was a really, really bad idea.
What I’d be more interested in is an AI paralegal that works for me, not for the signing tool or the counterparty, where I control its prompt so I can try to focus it on possible ways I can be screwed with this contract, and what recourses I will or won’t have.
- The moment AI is actually good enough to replace us, it will also be incredibly easy to create new software/apps/whatever. There could/would be a billion solo dev SAAS companies eating the lunch of every traditional tech org.
- People (Executives) seem to underestimate just how much of the work is iterating and refining a product over a long time. Getting an LLM good enough to complete a Jira task is missing the point.
- IMO LLM's are also completely draining the motivation of workers. A lot of software devs are intrinsically motivated by solving the problem. If your role is being watered down to "prompt the chat bot and baby sit what comes out", the motivation disappears. This also absolutely destroys any of the creativity/discovery that comes out of solving the task hands-on.
I used to love coding, and did it a ton. Then it became less and less part of my job, and I started hating coding. It was so frustrating when I knew exactly what needed to be done in the code, but had to spend the time doing low value stuff like typing syntax, tracing through the code to find the right file to edit, etc when I'm already strapped for time.
LLMs and agentic coding tools have allowed me to not spend time on the low-value tasks of typing, but instead on the high-value tasks of solving problems like you mentioned. Just interesting the different perspectives we have.
I think both of the viewpoints are valid depending on where you're at in your career.
We can imagine a junior developer who isn't quite bored with those low-value tasks just yet.
As you grow more senior/experienced, the novel problems become harder to find - and those are the one's you want to work on. AI can certainly help you cut through the chaff so you have more time to focus on those.
But trends are trends and AI is increasingly getting better at solving the novel/interesting problems that I think you're referring to.
Everyone's different and I know there are folks who are excited to not have to write a single line of code. I'd wager that's not actually most engineers/developers though.
People still garden by hand because it's innately satisfying.
And I did not speak out
Because I was not an artist
It doesn't matter. People are convinced it's a miracle technology, so I'm just a backwards luddite resisting progress
First they came for the Communists
And I did not speak out
Because I was not a Communist
Then they came for the Socialists
And I did not speak out
Because I was not a Socialist
Then they came for the trade unionists
And I did not speak out
Because I was not a trade unionist
Then they came for the Jews
And I did not speak out
Because I was not a Jew
Then they came for me
And there was no one left
To speak out for me
I love AI image generation, but many certainly do not enjoy the results. I can see some people skimping on paying artists.
First I thought translators would be hit hard by AI, but you probably still need them as well to be decently sure about correctness.
And it remains true that any creativity produced by AI is basically still just a function of the creativity of other people.
Remember that when they're wrecking your productivity by trying to twist your job into something they can measure in a spreadsheet.
I think the general point here is true, but it's also brilliant framing from a company selling consulting services.
> Price levels: How should vendors set price levels when the cost of inferencing is dropping rapidly? How should they balance value capture with scaling adoption?
This is written for B2B target clients as if it's pulling back the veil on pricing strategy and negotiating. Hire McKinsey to get you the BEST™ deal in town.
These things are clearly useful once you know where they excel and where they will likely complicate things for you. And even then, there's a lot of trial and error involved and that's due to the non-deterministic nature of these systems.
On the one hand it's impressive that I can spawn a task in Claude's app "what are my options for a flight from X to Y [+ a bunch of additional requirements]" while doing groceries, then receive a pretty good answer.
Isn't it magic? (if you forget about the necessity of adding "keep it short" all the time). Pretty much a personal assistant without the ability of performing actions on my behalf, like booking tickets - a bit too early for that.
Then there's coding. My Copilot has helped me dive into a gigantic pre-existing project in an unfamiliar programming language pretty fast and yet I have to correct and babysit it all the time by intuition. Did it save me time? Probably, but I'm not 100% sure!
The paradoxicality is in that there's probably no going back from AI where it already kind of works for us individually or at org levels, but most of us don't seem to be fully satisfied with it.
The article here pretty much confirms the paradox of AI: yes, orgs implement it, can't go back from it and yet can't reduce the headcount either.
My prediction at the moment is that AI is indeed a bubble but we will probably go through a series of micro-bursts instead of one gigantic burst. AI is here to stay almost like a drug that we will be willing to pay for without seeing clear quantifiable benefits.
We will eventually reach a point where people are teaching each other how to perform evaluation. And then we’ll probably realize that it was being avoided because it’s expense to even get to the point where you can take a measurement and perhaps you didn’t want to know the answer.
With AI you have a thing you can't quite trust under any circumstance even if it's pretty good at everything.
And don't even get me started with the "AI Assistants" that every support company has which are utterly useless and just make it more difficult to get to an actual human.
The only use cases I've actually seen in production that are high performance are: 1) writing code (CC is good), 2) code inline editing suggestions (Zed does this pretty well, so does JetBrains), 3) document summaries.
I also use image generation extensively, but that is for hobby purposes.
I trained some models (RAG) and build some tools, but quickly noticed that is quiet a bit of work even to generate and audit the knowledge base you want to consume. I think it warrants to be a new job position. Problem is that this position needs someone who is very familiar with the processes within a company, so external help here is hard to come by and it would be a longer time investment. If you have success here, I would assume it could boost productivity for workers that aren't that close to AI tech and maybe just used AI for search and questions.
Until now we (only?) have an AI that reads mails and imports the relevant information into our ERP, CRM or general content systems. It queries relevant information about support cases to provide helpful context for case handlers. I think it works quite well, but I cannot imagine it replacing anyone. Exception might be if you specialize on scamming people, that could probably work.
... Wait, why would the _vendor_ care about that? It's the customers who should be cautious; unscrupulous vendors will absolutely sell them useless snake oil with no qualms, if they're willing to buy it.
> These leaders are increasingly making budget trade-offs between head count investment and AI deployment, and expect vendors to engage them on value and outcomes, not just features.
The cheek of them! Actually demanding that the product be useful!
That's easy. Reduce the headcount first, and then let the remaining team of poor and desperate, I mean, elite engineers and support teams <buzzword for use> AI for <more buzzwords for make dollars go up> /s.
When will boards replace executive leadership with AI? If Return to Office taught us anything, it was that we already need a couple, and the rest of them copy and paste. Well, AI can do that! Also /s, but maybe just 50%.