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Annual Research — 2022

The Year the Music Slowed

Two reports, one operator-grade conclusion: the firms that survive a downturn aren't the ones that cut hardest — they're the ones whose systems were already lean.

Q4 2022·11 min read·2 primary sources
−65%

Public SaaS multiples vs. 2021 peak

14

Strategic tech trends McKinsey tracked — most still pre-revenue

1 in 3

Tech companies announcing efficiency programs by Q4

Sources Analyzed
Technology Report 2022
Bain & Company · 2022
Source
The Top Trends in Tech 2022
McKinsey & Company · 2022
Source
The Source Material

01Two reports, one underlying story

Bain's 2022 report pivoted hard from the prior year's enthusiasm. Tech M&A volume dropped sharply. Public SaaS multiples reset to roughly a third of their 2021 peak. The talent market loosened in pockets. And inside operating businesses, the conversation moved from 'how fast can we hire' to 'how lean is our cost-to-serve.'

McKinsey's Tech Trends Outlook 2022 tracked fourteen distinct technology arenas — applied AI, industrializing ML, cloud and edge, Web3, immersive reality, trust architectures, advanced connectivity, the future of mobility, bioengineering, space, sustainable energy, clean energy transitions, and more. The report's quiet finding: investment was still flowing into all of them, but commercial impact was concentrating in a much smaller number — primarily applied AI and industrialized ML.

Read together, the two reports describe a market that was simultaneously sobering up and concentrating its bets.

02What the operators saw

By mid-2022 the 'efficiency' word started appearing in every earnings call. Most teams interpreted it as 'cut headcount.' Bain's data suggests the better-performing companies interpreted it differently: cut the work itself.

The companies that emerged from 2022 in the strongest position weren't the ones with the largest layoffs. They were the ones whose pre-existing operational maturity meant they had less duplicated work, fewer manual handoffs, and clearer ownership when leaders started asking 'who actually does this?'

"Cutting people is fast. Cutting work is durable. Most companies confuse the two and pay for it twice."

03Where the AI thesis quietly hardened

McKinsey's 2022 outlook is, in retrospect, one of the last reports written before the generative-AI moment. It doesn't single out generative AI as a standalone trend — that came in 2023. But its framing of 'applied AI' and 'industrializing machine learning' is exactly the muscle that companies needed to be ready to absorb what was about to arrive in November 2022.

The companies that already had clean data pipelines, documented decision logic, and instrumented workflows were going to be able to plug AI in. The companies that didn't were going to spend the next two years cleaning up before they could move.

Our Take

How this maps to the work

Our 2022 engagements were dominated by one request, in different vocabulary: 'help us do this with the team we have.' Hiring freezes were on. Boards wanted to see operational leverage. The work moved from 'add capability' to 'remove friction.'

The tactical pattern was consistent. We'd come in, instrument the workflows that owned the most labor, find the three to five process steps that were absorbing disproportionate hours, and either redesign them out or move them onto a system. The output was almost always the same shape: same revenue, fewer hours, cleaner data, less human coordination required.

How We Add Value

Four engagements we run against this thesis.

None of these require a multi-year transformation. Each is scoped to land specific operating-model improvements with a measurable result.

01

Cost-to-serve teardown

We trace one customer's journey from signed contract to fully onboarded and quantify every human touch. Most teams discover they're spending 2-3x what they thought, and 60% of it is non-differentiating coordination work.

02

Workflow consolidation, not headcount

Before any headcount conversation, we surface the duplicated work — the same data being entered three places, the same approval being sought twice, the same status being asked for in four channels. Removing the work is almost always cheaper than removing the person.

03

AI-readiness assessment

We score the workflows you already have against what an AI integration would actually need: clean inputs, documented decision logic, defined success criteria. Most teams are 6-9 months of cleanup away from being able to deploy AI safely. We compress that.

04

Operating cadence redesign

When budgets tighten, the meeting layer is usually the first thing that bloats — because nobody trusts the data anymore. We rebuild the weekly and monthly rhythm so the right people see the right numbers without a 40-minute readout.

Continue Reading

Other reports in the series.

2026Frontier

Cross-Architecture Model Diffing with Crosscoders

Anthropic on a method for surfacing — without telling it where to look — what's actually different between two LLMs. The operating-model implication for every team about to swap a model in production.

2026Frontier

Emotions in Models — Interpretability Research

Anthropic's interpretability team on emotional patterns inside frontier models — and why that matters when one is sitting inside a client workflow.

2025Annual

Agents Arrived. Most Operating Models Aren't Ready.

McKinsey, Bain, and Deloitte's 2025 outlooks tell three versions of the same story: the unit of automation moved from a task to a workflow to an agent — and most companies are about to find out their workflows weren't built for software that acts.

Working with us

If this maps to what you're carrying — let's talk.

Most engagements start with a 30-minute conversation about the specific operating-model question on your desk this quarter.

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