Turn AI hype into measurable ROI
Most AI projects fail not because the technology does not work, but because teams pick the wrong use cases, underestimate data work, or launch without adoption. We help leaders identify the highest-ROI AI opportunities for their specific business, build a 12-month roadmap with clear metrics, and enable internal teams to actually ship.
RAND Corporation research found that over 80% of AI projects fail — twice the failure rate of non-AI IT projects — with the top failure causes being misaligned problem statements, poor data readiness, and insufficient organisational buy-in. Gartner predicts that at least 30% of generative AI projects will be abandoned after proof of concept by end of 2025. The pattern is almost always the same: executives are told AI can do anything, teams chase demos instead of business outcomes, and six months later there is nothing in production. Our strategy engagements exist to prevent that — by starting with the P&L, working backwards to the specific workflows and data that matter, and shipping one provable win before expanding. We combine deep hands-on implementation experience with strategic clarity so our roadmaps are buildable, not just slideware.
80%+
of AI projects fail — twice the rate of non-AI IT projects
30%
of GenAI projects will be abandoned after PoC by end of 2025
11%
of enterprises have scaled AI enterprise-wide; the rest are stuck in pilots
Core capabilities
AI readiness assessment
Structured audit of your data, tooling, talent, security, and culture. Scored 0–100 across six dimensions with a prioritised gap-closure plan.
Use-case discovery & roadmap
12-month AI roadmap with scored use cases, expected ROI, dependencies, budget, and delivery sequence — reviewed quarterly.
Build-vs-buy advisory
Independent recommendations on what to build internally, buy from vendors, or embed via SaaS — with total cost of ownership over 3 years.
Custom LLM fine-tuning
Fine-tune smaller open-source models on your data for cost, privacy, or domain-specific performance improvements.
AI governance & policy
Acceptable-use policies, model-selection standards, data-handling procedures, and audit trails aligned with today’s leading AI governance and risk-management frameworks.
Team enablement & training
Executive workshops, team training on prompt engineering, role-specific AI toolkits, and coach-on-call support during pilots.
Why most AI strategies fail
The same three mistakes show up in almost every failed AI project. First, teams start with the technology ("we should use GPT-4") rather than the business problem ("our sales reps spend 40% of their time on CRM data entry"). Second, they underestimate data readiness — most enterprise data lives in fragmented systems, unstructured documents, or tribal knowledge that needs cleaning before any AI can use it. Third, they over-invest in building and under-invest in adoption, ending up with technically impressive systems that nobody actually uses. Our strategy engagements are designed to avoid all three — we start with P&L impact, we assess and remediate data gaps before building, and we invest as much in change management as in code.
What a KTech AI strategy engagement covers
- Business-outcome mapping: which P&L levers (revenue growth, cost reduction, risk) matter most, and which workflows connect to them.
- Use-case discovery: structured interviews across leadership, frontline teams, and IT to surface the 15–25 highest-potential AI applications.
- Use-case scoring: each use case scored on impact, effort, risk, data readiness, and alignment with strategy — delivered as a prioritised matrix.
- Technology architecture: recommended model choices, integration approach, vendor shortlist, and reference architecture diagrams.
- Financial modelling: bottom-up cost estimates, expected savings or revenue, ROI and payback period for each prioritised use case.
- Organisational plan: roles, responsibilities, RACI, governance board, and internal capability-building plan.
- 12-month delivery roadmap: quarterly milestones, dependencies, risks, and decision points — reviewed and adjusted quarterly.
Fine-tuning vs. prompting vs. RAG
One of the most common strategy questions we are asked is whether to fine-tune a custom model. The honest answer is: rarely. For 90% of enterprise use cases, a well-designed RAG system with a frontier model (Claude, GPT-4) outperforms a fine-tuned smaller model at lower cost and with better maintainability. Fine-tuning makes sense when you need (a) a specific output format or tone that prompting cannot reliably enforce, (b) dramatically lower inference cost at very high volumes, (c) domain-specific vocabulary not well-represented in pre-training, or (d) data residency requirements that prevent using managed APIs. We help you make this decision with clear cost and quality benchmarks, not vendor narrative.
Governance, risk, and regulation
AI governance is not optional — it is a condition for scaled AI adoption. Major global regulators have moved from guidance to enforcement, imposing significant obligations on "high-risk" AI systems including recruitment, credit scoring, healthcare, and law enforcement applications, with fines that can reach tens of millions or a meaningful percentage of global turnover for non-compliance. International AI-management and AI risk-management standards are emerging as de facto global norms. Our governance work aligns you with these frameworks pragmatically — not with 200-page documents nobody reads, but with lightweight policies, model-selection guidelines, and audit procedures that fit how your organisation actually operates.
Real-world use cases
Regional bank
Commissioned a readiness audit and 12-month roadmap after six failed AI pilots.
Outcome: Identified 9 prioritised use cases, scrapped 3 in-flight pilots, launched 2 production systems within 6 months with measurable ROI.
Manufacturing group
Executive team wanted an AI strategy but had no internal data science capability.
Outcome: Delivered 12-month roadmap, hired a 4-person internal AI team (via our retained search), and shipped first production automation within 90 days.
Healthcare SaaS
Needed fine-tuned model for clinical-note summarisation with HIPAA-compliant self-hosting.
Outcome: Fine-tuned Llama 3 on de-identified clinical notes, deployed inside VPC, achieved 94% accuracy at 1/8th the cost of GPT-4.
Professional services firm
Needed AI training and governance policy covering 400+ knowledge workers.
Outcome: Delivered tiered training programme, published AI acceptable-use policy, measured 73% active adoption within 90 days.
Our delivery process
- 1
Discovery (2 weeks)
10–15 structured interviews across leadership, frontline teams, and IT. Document review of current data architecture, tools, and in-flight AI initiatives.
- 2
Use-case workshop (1 week)
Facilitated executive workshop to surface candidate use cases, score them collaboratively, and align on top priorities. You leave with a clear scored matrix.
- 3
Roadmap & business case (2 weeks)
We build bottom-up cost estimates, ROI models, and delivery plans for top 5–7 use cases. Technology architecture, vendor shortlist, and reference diagrams included.
- 4
Governance & readiness (1–2 weeks)
Lightweight policies, model-selection standards, data-handling procedures, and org-design recommendations aligned with today’s leading AI governance and risk-management frameworks.
- 5
Executive readout & quarterly reviews
Final strategy delivered to your leadership team with a live decision session. We offer optional quarterly reviews to refresh priorities and track progress.
What you get
Frequently asked questions
How is this different from a Big 4 AI strategy engagement?
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Two main differences. First, we ship code — our strategists have actually built and deployed production AI systems, so the roadmaps we write are buildable, not aspirational. Second, we are a fraction of the cost and timeline — a typical strategy engagement is 4–6 weeks and $25,000–80,000 rather than 4–6 months and $500,000+. We are not trying to staff armies of consultants; we are trying to give you a working plan you can execute.
We are a small business — is AI strategy still relevant?
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Yes, but scoped differently. For small businesses (under 100 employees) we run a compressed 1-week engagement: half-day workshop, use-case prioritisation, and a focused 90-day action plan targeting 1–2 quick wins. Cost is typically $4,000–10,000 and delivers more practical value than reading a dozen industry reports.
Do you work with us after the strategy is delivered?
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If you want us to. Many clients hire us to build the first two or three use cases from the roadmap (bridging strategy to execution). Others take the roadmap to internal teams or other delivery partners. We have no lock-in — the deliverables are vendor-agnostic enough to execute with anyone.
How do we know the use-case scores are objective?
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We use a consistent six-factor scoring rubric (business impact, technical feasibility, data readiness, time to value, ongoing cost, risk) weighted by your leadership team's priorities in the workshop. Every score is defended in writing with the evidence behind it. You can challenge and adjust any score — the model is fully transparent.
Can you help us hire AI talent?
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We offer retained search for AI/ML engineers, AI product managers, and data engineers as an add-on to strategy engagements. We have placed senior AI hires at manufacturing, SaaS, and financial services clients. Typical fees are 20–25% of first-year compensation.
How current is your AI governance and regulatory knowledge?
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We track the major global AI regulations, management-system standards, and risk-management frameworks actively, and refresh our playbooks as enforcement milestones hit. Our governance deliverables map specifically to the articles, clauses, and obligations that apply to your industry and geographies.