03 · Value Acceleration
Value Targeting
Which of ten proven AI use cases will move this company's EBITDA inside the hold — and what are they worth? A priced, measured AI line in the value creation plan: in diligence, in the first 100 days, or on the way to exit.
3 moments
Diligence · first 100 days · exit
10 use cases
Proven impact, productized delivery
3 weeks
A priced AI line inside a live diligence
1 quarter
To the first measured result — built by DotKonnekt, against a week-one baseline
Where the next value lives
Three realities to act on — the exit clock does not wait, AI has not paid yet in most portfolios, and where it pays, it pays twice.
The exit clock is real
The average hold has reached a historic high of 6.6 years, against 6.1 over 2011–2020, and the backlog of companies on the books for more than four years and ready to exit stands at 16,000 globally.
SOURCES: McKinsey, Beating the odds — how private equity firms can improve exit prospects, March 2026
AI has not paid yet in most portfolios
Only 14% of PE-backed CEOs say AI has contributed to both higher revenues and lower costs; more than half report no upside at all. Nearly 20% of portfolio companies have operationalized generative-AI use cases with concrete results.
SOURCES: PwC, AI fitness and value creation in PE-backed companies, 2026 (564 PE-backed CEOs) · Bain, Global Private Equity Report 2025
Where it pays, it pays twice
PE-backed companies that systematically build AI capability across functions earn nearly twice the return on invested capital — and 40% of investors have taken a valuation haircut of 5% or more for lagging digital maturity.
SOURCES: BCG, Private Equity's Future: Digital First and AI Powered, January 2026
Lead: three moments. One method.
Door A: the deal team, in a live diligence
A target where AI is part of the bid case — or part of the risk. Three weeks: the AI line graded on evidence and priced — what to pay for now, what belongs in the earn-out.
Door B: the portfolio CEO, in the first 100 days
A new CEO, or a new value creation plan. Which two or three of the ten use cases move this P&L fastest, sized on the company's own data in week one — with the first measured result inside the quarter.
Door C: the operating partner, 18–24 months from exit
The levers that show in run-rate EBITDA a buyer's QoE can verify, and AI capability the company owns — in the data room, not in the pitch.
Not for pre-PMF or turnaround assets, pure financial restructuring plays, or CEO-replacement situations. Not an AI strategy deck.
Engagement triggers
Why this is the right instrument now.
Live bid with an AI thesis
Diligence window on a target where AI-amplifiable value is the bid case — or where the seller's AI story is the risk. The thesis needs a graded, priced AI line before close.
A new CEO, a new plan
The first 100 days decide which levers get funded. The ten screened against this P&L, the two or three with the biggest short-term impact in the plan.
The plan signed off, not delivering
Budget missed quarter over quarter. The value creation plan needs a reset, not a relaunch.
18–24 months before exit
An AI-fluent acquirer will spot the levers you have not pulled and price the asset accordingly — 40% of investors have already taken a haircut for lagging digital maturity. Find them first.
The AI bill is already material
Where AI running costs exceed roughly $300,000 a year, the savings on the next invoice can finance the plan.
The ten use cases
Four grow revenue, six take out cost — and every one leaves capability a buyer pays for at exit.
Grow revenue — demand, conversion, trade and pricing
01 · AI search visibility
Be found where demand now forms — AI-readable content and answer-engine pages.
02 · Owned shopping and design assistant
On the brand's own site, app and showroom — for consumers and the trade.
03 · Speed-to-lead and sales coaching
Every lead answered in minutes; every appointment coached.
04 · Pricing, quoting and promotion intelligence
Price, quote and offer decisions made with data and judgment.
Take out cost — service, content, planning, procurement, back office, and the AI bill itself
05 · Service and order-taking agents
Chat, email, voice and the drive-thru — with customer quality measured.
06 · Content production
Product and marketing content for large catalogs at a fraction of the cost.
07 · Forecasting, inventory and scheduling
Working capital and labor on data the company already has.
08 · Procurement and landed-cost agents
Cost of goods, terms, freight and duty.
09 · Back-office and document workflows
Finance, HR, compliance, onboarding — the highest-return, least-funded lever.
10 · Economize AI spend first
Optimize the AI the company already pays for, so the initiatives finance themselves.
The outcome of all ten: AI capability the company owns, run-rate P&L a QoE can verify, and a stronger equity story at exit.
Ten, not the whole map: the use cases with proven impact, productized — a published result behind each, a known path to the first measured result, sized on the company's own data in week one, never on forecasts. Where the screen finds more in this company, it is sized on its own evidence.
Which first? Cost levers bank fastest — they are measured against a cost line the company already reports; growth levers show in revenue with a lag. A 100-day plan usually starts with one of each.
The foundation, and what you walk out with
The foundation
Week one: the baseline frozen on the company's own data — the P&L lines, the volumes, the AI bill. The ten screened against that P&L and the data the company already has; the two or three with the biggest short-term impact chosen and sized. Every benchmark we use is published and named; every range is replaced by the company's own number.
Walk out with
In diligence: the AI line graded and priced — what to pay for now, what belongs in the earn-out.
In the first 100 days: the two or three levers in the value creation plan, with the first measured result inside the quarter.
Before exit: run-rate P&L a QoE can verify, and the capability in the data room.
In every case: built by DotKonnekt with the company's people, transferred by contract. No dependency.
One team to deliver
Executive and technology expertise from two angles — one team, one plan, one number.
The executive angle — The AI Amplifier
Robert Hackl — thirty years of P&L ownership (Tele2, Vodafone, T-Mobile US, Sprint), on the buy side of AI promises and the delivery side of large transformations. Sizes and prices the levers for the investment committee or the value creation plan; measures them against the week-one baseline and with the AI Perception Score.
The technology and industry angle — DotKonnekt
Enterprise AI execution built by retail, consumer and distribution operators. Agentic AI on Kompass Studio — independent of any cloud, model or framework, the models swapped and routed for cost, governance built in. Digital marketing that lowers acquisition cost: Sangria, AI-readable, shoppable pages generated from the catalog. Forward-deployed engineers inside the company — every one of the ten use cases is delivered by DotKonnekt.
Together
One engagement, one scorecard: the right lever and the right build, chosen from both angles at once · weeks to a prototype, a quarter to production, no platform procurement first · measured against the week-one baseline, owned by the company at the end — a sellable asset at exit.
Where the team, not only the system, has to carry the capability: Humaine Labs for enterprise AI upskilling — 3,000+ executives trained, 50+ enterprises. See Executive Readiness.
Value Targeting
When AI showed up in your last deal — was it priced?
Ten proven use cases. Three moments in the hold. One team — The AI Amplifier and DotKonnekt — that sizes, builds, measures and leaves the capability behind. Tell us which company, and which moment.