Fast Track AI Strategy
Strengths × Opportunities
How do we attack?
Launch a fixed-price AI-enabled risk and compliance diagnostic for mid-market clients within 6 months.
Big 4 are pricing AI-enabled assurance at a premium, but the firm has the regulatory depth and partner credibility to enter the same shelf at a more accessible price point. First 60 days: scope the diagnostic with two anchor clients from financial services and one from government, ideally clients who renewed in the last 18 months and trust the partner. Build it around three prebuilt risk models (operational risk, regulatory change response, third-party risk) so the AI work is reusable across engagements. Target a $25k fixed price for a three week delivery, with a defined upgrade path into traditional advisory work. Owned by the practice lead for risk, co-launched by the AI lead once hired. Success signal: three paid pilots inside six months and 60%+ converting to follow-on advisory.
Build a Document Intake Accelerator that halves new-client onboarding effort.
Onboarding currently absorbs 8 to 12 hours of senior time per engagement and frustrates new clients who expect fast turnaround. First step: pick the three most repetitive intake artefacts (engagement letter, prior-year financials, regulatory permissions) and prototype AI extraction against them using last quarter's actual files. Get partner sign-off on the extracted structure before any client sees the output. Build around a single LLM provider with a documented data residency posture so partners can answer client questions confidently. Owned by the AI lead. Success signal: 50% reduction in senior hours per onboarding and a measurable drop in cycle time from accepted proposal to first deliverable, within four months of go-live.
Launch a three-tier productised diagnostic ladder priced $15k to $50k for mid-market entry.
Mid-market clients increasingly want fixed-price diagnostics they can buy without an RFP. Slot the three tiers as: lightweight readiness check ($15k), functional diagnostic with one priority workstream ($30k), full strategic diagnostic with board presentation ($50k). Use the ladder as the front door; price the lower tiers with explicit intent to convert at least one in three into a follow-on engagement. Partner-led delivery is the differentiator: every tier includes named partner time. First 90 days: define the three tiers, pre-sell the lightweight check to five existing clients at introductory pricing, refine, then launch publicly. Owned by the head of advisory. Success signal: eight productised diagnostics sold in the first 12 months with a 40%+ follow-on conversion.
Weaknesses × Opportunities
How do we transform?
Hire a Knowledge Operations lead to convert partner know-how into AI-usable assets.
The firm's most valuable input (partner pattern-recognition from 20-plus year careers) sits in personal folders and email threads. Without a deliberate effort to extract and structure it, every AI initiative is starved of the differentiator. First step: scope a six month build phase covering taxonomy design, partner-interview cadence and a single source-of-truth knowledge store. Budget for one full-time hire plus 0.5 day per week from each senior partner. Hire someone with information architecture or technical writing background, not another consultant. Reports to the COO with a dotted line to the AI lead. Success signal: three priority practice areas with structured, AI-queryable knowledge bases by month nine, and measurable reuse on at least five proposals.
Adopt a single AI proposal-generation tool firmwide with senior-partner review.
Bespoke proposals currently absorb 20 to 40 hours each, which is unsustainable as the firm scales and a poor use of senior time. Standardise on one tool (not three competing pilots) so prompt patterns, brand voice and quality bar converge. First step: pick a defensible vendor with data residency, redaction features and an audit trail; train the top five proposal writers; require senior-partner sign-off on every AI-drafted output until quality is proven. Pair with a short style guide so the tool produces drafts that sound like the firm, not generic consulting prose. Owned by the head of business development with AI lead support. Success signal: average proposal cycle drops from around 30 hours to 8 hours within 90 days of rollout, with no measurable drop in win rate.
Hire one dedicated AI lead within 60 days. A builder, not another consultant.
Three of the four quadrants in this strategy depend on a single owner who can ship infrastructure, not write decks about it. The hire should have shipped AI into production at a services or platform business, not a research background. Reports directly to the managing partner during the first 12 months to signal seniority and unblock procurement, vendor and partner conversations. Write the role spec around three concrete outcomes (Document Intake Accelerator live in four months, proposal tool firmwide in six, risk and compliance diagnostic supported in nine) so candidates self-select. Expect to pay 20% above standard senior consulting salary to attract the right profile. Without this hire, the rest of the playbook is a wishlist.
Strengths × Threats
How do we protect?
Position partner-led delivery as the trust counter to Big 4 productisation.
Big 4 productised AI offerings are powered by junior staff and tooling. The firm's structural advantage is that every engagement has a named partner involved, which is exactly what clients ask for when an AI-augmented recommendation needs a defensible human sign-off. First step: rewrite the firm's three top-of-funnel materials (website lead page, sales deck, sample SOW) around the phrase "AI-augmented expertise", not "AI-replaces-expertise". Brief partners on a three minute pitch that contrasts the two models explicitly. Owned by marketing with practice-lead input. Success signal: in client conversations and lost-deal debriefs, the "partner-led" framing is recalled unprompted in at least half of engagements within six months.
Stand up an internal AI training programme tied to chargeable certifications.
Graduates are choosing larger firms with structured AI training programmes, so the firm needs a credible answer at offer-letter stage. Tie the programme to certifications clients recognise (vendor certifications, sector-specific accreditations) so the training shows up on engagement letters and CVs, not just internal email. First step: scope a six month curriculum covering tool use, prompt engineering for regulatory work and AI governance; allocate four hours per week per analyst for the first cohort. Pair with a measurable expectation that certified staff carry an AI-augmented service line on at least one chargeable engagement per quarter. Owned by the people lead with AI lead input. Success signal: graduate offer acceptance rate improves measurably year on year and the certification is referenced in 30%+ of client proposals.
Publish a quarterly "AI in Mid-Market Regulatory Advisory" benchmark to own the conversation.
Big 4 dominate publicly-cited AI commentary even though the mid-market is where most of the buying happens. A quarterly benchmark, anchored on the firm's actual client work and partner observations, creates a defensible reason for journalists, peer firms and prospects to cite the firm rather than a Big 4 white paper. First step: scope the benchmark around three to five indicators the firm can report on credibly (AI adoption among ASX 200, regulatory commentary volume, productised offering price points) and commit to four issues in the first year. Pair with a short partner podcast or roundtable per issue to amplify. Owned by marketing with a named partner editor. Success signal: two unsolicited media citations and at least one inbound enquiry per issue by the end of year one.
Weaknesses × Threats
How do we mitigate?
Set a 90-day AI-baseline policy: every active engagement uses at least one approved AI tool.
AI adoption today is bottom-up and inconsistent, which means the firm cannot reliably tell a client that "our analysis is AI-augmented". That gap is exactly what Big 4 productised offerings are pricing against. The policy doesn't dictate which tool, it just makes the use visible and approved. First step: publish a short list of approved tools (one for document review, one for proposal drafting, one for research synthesis), require engagement leads to nominate which they're using at kickoff and track it in the project management system. Pair with a fortnightly stand-up to surface friction. Owned by the COO with AI lead support. Success signal: 100% of active engagements report AI tool use within 90 days; tool-use commentary appears in at least 30% of client status reports.
Carve out a defensive mid-market segment focused on regulatory niches the Big 4 deprioritise.
Reduces head-to-head competition in commoditising service lines and protects the most defensible part of the book: complex regulatory work for clients too small for Big 4 partner attention. First step: identify three regulatory niches where the firm has demonstrable depth (e.g. APRA Prudential Standard work for non-bank lenders, AUSTRAC remediation for emerging fintechs, ATO disputes in the $50m to $500m revenue band) and explicitly market against them. Repackage existing partner CVs around these niches and decline opportunities that don't match. Owned by the head of advisory with a 12 month commitment to the focus. Success signal: 50%+ of new business in year two comes from one of the three niches with measurably stronger price realisation than the firm average.
Triage low-margin service lines that AI productisation will commoditise within 18 months.
Some current revenue is propped up by manual work that Big 4 productised tools will do at 20% of the cost inside 18 months. Surface this honestly before the margin compression hits, while the firm still has bargaining power on what it accepts. First step: with the practice leads, score each service line on (a) defensibility against AI-productised competition and (b) margin contribution, then identify the bottom two or three service lines for active managed decline. Re-deploy the freed senior time into the offensive moves above. Don't fire clients, but stop proactively chasing more of the same work. Owned by the managing partner. Success signal: at least two service lines formally deprioritised in the next planning cycle; partners' chargeable mix shifts visibly toward AI-augmented and productised offerings.
Three of the four quadrants depend on a single owner who can ship infrastructure: Knowledge Operations, proposal tooling, the productised risk and compliance diagnostic and the Document Intake Accelerator. Without this hire in the next 60 days, the rest of the playbook becomes a wishlist that surfaces in next quarter's strategy review with the same urgency and no progress.
This is the single move that simultaneously grows revenue, defends against Big 4 productisation, signals AI capability to the market and pulls the firm out of bespoke-proposal cycles. Highest leverage and fastest proof point. The other offensive moves can wait a quarter; this one cannot.
The market is bifurcating between AI-replaces-expertise (Big 4 productised) and AI-augments-expertise (the firm's natural position). Owning that distinction publicly is cheaper and faster than building AI capability the Big 4 way, and it locks in a story the firm can credibly defend even as Big 4 cut prices.
Same structure, your data. Need the full board-level strategy? See the Complete AI Strategy.