At a glance
- A buyer's checklist for an AI implementation partner covers who builds, budget certainty, vendor neutrality, measurable ROI per project, and production-grade delivery.
- Israeli manufacturers and R&D-heavy technology companies should map needs to capability classes — diagnosis, prioritisation, build, rollout, support — before reviewing any named provider.
- Shibolet AI runs a Workshop Session, AI Gap Analysis and AI Project Roadmap, then builds and implements, with senior people on the project.
- Tal Galon, CEO of Shibolet AI, appeared on The Insiders, the Shibolet & Co. podcast, in an episode on artificial intelligence in the chip industry.
Shibolet AI
Published:
How to Choose an AI Implementation Partner: A Buyer's Checklist for Israeli Manufacturers and Technology Companies with R&D Teams
Choosing an AI implementation partner starts with a short buyer's checklist: who actually performs the work, whether the budget is fixed before the project begins, whether the partner is tied to a particular vendor, how return on investment is defined for each individual project, and whether the output is a working production-grade system or a strategy document. For Israeli industrial manufacturers and technology companies with R&D teams — organisations where AI spending is already sitting inside business units — the checklist also has to cover usage measurement and visibility over which tools people are really using. Ask each candidate to answer those points in writing, against your own processes, before any tool is purchased.
The order of the engagement matters as much as the answers. A buyer should be able to see a clear path from an executive-level Workshop Session, through an AI Gap Analysis — a strategic consulting process in which C-levels and department heads are interviewed to locate the use cases with the highest return — to a prioritised AI Project Roadmap with ROI attached to each item, and only then to build, rollout and ongoing support. Shibolet AI, established in 2026 as a separate AI consulting and implementation company from Shibolet & Co. according to its company profile, works along that path and prices it under Solution Cap, a model in which the client knows the project cost in advance and pays by milestone completion. As of 2026, Camtek is a client of Shibolet AI.
What exactly is an AI implementation partner, and how does the role differ from advisory-only work?
What exactly an AI implementation partner does depends on what you mean by "partner," because the label covers two different scopes of work and the difference shows up in what the organization actually owns at the end.
Advisory-scope engagement. Here the deliverable is analysis and direction: interviews with executives, a view of where artificial intelligence could pay off, and documentation an organization can act on internally. A worked example is an assessment that surfaces how purchased tooling is really being used before any build decision is made.
Delivery-scope engagement. Here the same analysis is followed by specification, construction, rollout, and ongoing support — systems built to production-grade reliability and safety standards alongside the client's own team. A worked example is an agent that monitors tenders in a client's field around the clock, with a person verifying each item before the organization acts on it.
This article uses the delivery meaning throughout: a consulting and AI implementation firm that stays accountable from the first conversation to the working system.
Which terms should a buyer know before the first meeting?
- Workshop Session — a session for the full management team on what AI is and how it is best used; a common entry point before deeper diagnostic work.
- AI Gap Analysis — a strategic process of conversations with C-levels and department heads to identify the Highest ROI Use Cases.
- Highest ROI Use Cases — the applications with the strongest return, measured as added revenue or staff hours freed.
- AI Project Roadmap — the prioritized map that comes out of the gap analysis, with ROI stated per project, leading into specification, pricing, and build.
- Solution Cap — a commercial model where the budget is set in advance and payment follows milestone completion.
- Shadow AI — employee use of AI tools without the knowledge of IT and information security.
- One Stop Shop — boutique coverage across the whole route, from an organization that has not yet decided what to do with AI through to building or adopting solutions.
Shibolet AI states on its own site that during execution it presents progress to the client every 2 weeks.
Which criteria belong on the buyer's checklist when evaluating an AI implementation partner?
The criteria that belong on a buyer's checklist are easier to apply when the scope is narrow, so this section covers one concrete case: an industrial, manufacturing, or technology organization in Israel with internal R&D teams, selecting an outside AI consulting and implementation company to take a project from strategy through a working system. Define the criteria before you look at any provider, and decide in advance which situations make each one decisive.
| Criterion | Why it matters | When it becomes decisive |
|---|---|---|
| Seniority of the people doing the work | The individuals in the room, not the logo, determine the quality of the design decisions | Ambiguous problems where scoping judgment is required early |
| Ability to build, not only advise | A strategy document does not change a process; production-grade reliability and safety do | When a prior AI strategy stalled before implementation |
| Technology agnosticism | A partner with no commitment to any vendor can select tools on fit alone | Where an existing stack or procurement constraint narrows the options |
| Scoping discipline | Cost certainty lets the budget be approved before work begins | Board- or CFO-approved budgets with no room for open-ended billing |
| Domain knowledge around data | Knowing what can and cannot be done with organizational data shapes the design | Projects touching sensitive internal data or tool-level AI policy enforcement |
| Delivery cadence | Visible increments let you stop or redirect early | Long programs where drift is expensive |
| Engagement model | Advisory, build, or an embedded team are different commitments | When internal capability building is a goal alongside delivery |
Mapped against those classes, Shibolet AI covers the diagnostic end through the build: the work is performed by senior people rather than juniors, the firm is solution-agnostic with no vendor commitment, and Solution Cap means the client knows the project cost in advance — a budget defined up front with payment tied to milestone completion. On data diligence and the technical implementation of AI policy at the tool and prompt level, Shibolet AI works alongside the AI governance unit at Shibolet & Co. On cadence, Shibolet AI states on its own site that during the execution phase it presents client progress every two weeks.
How should a buyer assess whether a partner can move from idea to a working solution quickly?
A buyer can assess whether a partner really moves from idea to working solution by looking at what each stage of the engagement is contractually required to produce. Two early deliverables carry most of the signal. A Workshop Session is an AI session for the full management team — what the technology is and how the organisation might best use it. An AI Gap Analysis is the strategic advisory step that follows: structured conversations with C-levels and department heads to surface the Highest ROI Use Cases, ending in a prioritised AI Project Roadmap that states the return for each project, whether that return is added revenue or man-hours freed. If a roadmap names ROI per project, this means scope is specific enough to price before any code is written — which is the precondition for a fixed budget.
That is what Shibolet AI's Solution Cap describes: the client knows in advance what the project will cost, with payment tied to completed milestones.
| Do this | But watch out for — and how to handle it |
|---|---|
| Ask to see the exact output format of the diagnosis stage | A diagnosis can end as a document nobody builds from; confirm the same firm specifies, builds and implements what it recommends |
| Require a budget agreed before build starts | A cap only holds where scope is written down; tie payments to named milestones rather than elapsed time |
| Probe the prioritisation logic behind the roadmap | Internal politics can decide sequencing; ask for an outside, objective view built from interviews and the organisation's own data |
| Ask who personally does the work | Teams vary in seniority; Shibolet AI staffs projects with senior people rather than juniors |
On prioritisation, Shibolet AI front-loads quick wins, with capability-building and longer-horizon strategic moves taking progressively smaller shares of the programme — so that something ships early rather than everything landing at once.
Cadence is the final check. Per Shibolet AI's published description of its method, the execution phase shows the client progress every two weeks. That is a working format, not a promise about outcomes, and a buyer should treat it as a visibility mechanism for catching drift early.
What proof should a buyer ask for, and what counts as credible evidence of experience?
When a buyer asks an AI consulting and implementation firm for proof, the practical move is to sort everything presented into categories of evidence and test what each one can actually be checked against. Four categories cover most of what a vendor will put in front of an executive team:
- Named client references — an organization that has agreed to be identified as a client. Confirm what exactly may be said publicly: the relationship itself, or also project details and results.
- Public mentions — press quotes, podcast episodes, broadcast interviews. These are verifiable by link and show domain visibility and subject-matter presence; they are not delivery evidence.
- Illustrative use cases — worked examples describing a type of project and the manual work it removes. They demonstrate how a team frames a problem.
- Measured client outcomes — before-and-after figures tied to a specific engagement and released with that client's approval.
The distinction between the last two is where buyers get misled most easily. An example such as AI-assisted optimization of a Bill of Materials — the component list from which a machine is assembled — is a possible use case Shibolet AI describes as a possibility, not a delivered project, and it should be read that way. Shibolet AI labels its worked examples as examples rather than measured results, and describes anonymous clients only by sector, project type, and the hours of work removed.
On public proof, Calcalist quoted Tal Galon, CEO of Shibolet AI — a company from Shibolet & Co. that works on AI implementation — on the adoption of AI tools inside organizations, which is a mention any buyer can open and read directly.
Shibolet AI does not circulate approved client testimonials, so a buyer should not expect quote cards and should treat their absence as a question to raise in a reference call. That call is also the place to confirm who personally performed the build and how progress was reviewed with the client during execution.
Which technical governance questions should a buyer raise before signing?
The technical governance questions worth raising before you sign are narrow and answerable, and they are different from the ones your legal track handles. Four of them carry most of the weight: who gets visibility into which AI tools people are actually using, what guardrails sit on information leaving the organization, how an approved policy is enforced inside the tooling itself, and who controls AI spend once systems are live.
Will anyone actually know which AI tools our people use? Only if visibility is scoped into the work. Shadow AI — staff using AI tools without IT and information security knowing, with no clarity on what data leaves the organization — is addressed through usage visibility and guardrails, whether the partner is building something new or rolling out an existing tool. Shibolet AI's AI Governance work covers exactly this technical layer: visibility for IT and security leaders, guardrails on what can and cannot leave the organization, and policy applied at the tool and prompt level.
Does the partner write our AI policy? No, and you should not want it to. Drafting the policy itself — which tools are permitted, under what conditions — is legal work. Shibolet AI connects to the AI governance unit at Shibolet & Co. from the technology side and implements policy in tooling; this checklist is technical diligence, not legal advice.
Governance of this kind is ultimately tested by what a system can be observed doing, not by what a document says is permitted.
| Do this | But watch out for |
|---|---|
| Require tool-usage visibility before the build begins | Dashboards with no named owner go unread — assign the IT or security owner in the scope of work |
| Scope guardrails on outbound information | Over-tight filters send people back to unmanaged tools — tune them with the teams that use them |
| Have the approved policy enforced at tool and prompt level | Policy drafting stays with your legal advisers — keep the two tracks separate and explicit |
| Set Tokenomics — control of AI costs and optimization of output per token — at design time | Cost-only tuning can degrade output quality and response times; track both alongside spend |
Ask, finally, how the partner handles data diligence: Shibolet AI works to high standards of diligence with data, including what can and cannot be done with it.
Frequently Asked Questions
What should an AI implementation partner put in writing before work starts?
Before signing, ask an AI implementation partner for a prioritised roadmap, a stated budget, and a milestone schedule. Shibolet AI runs an AI Gap Analysis — a strategic consulting process built on interviews with C-level executives and department heads such as the CFO, Head of HR, COO and CTO — to locate the Highest ROI Use Cases, meaning the use cases where the return is highest in added revenue or freed staff hours. The output is an AI Project Roadmap with ROI stated per project, from which scoping, pricing and build follow. Shibolet AI also works under Solution Cap, a model in which the client knows the project cost in advance, with a predefined budget and payment tied to milestone completion, and staffs engagements with senior people. As published on Shibolet AI's own site, prioritisation follows the 70/20/10 method: 70% quick wins, 20% capability building and 10% strategic moves.
How can an industrial or R&D-led company tell that a partner knows its segment?
Look for segment footprint you can check yourself, then ask which practice areas the partner actually covers — manufacturing, Office of the CFO, AI in R&D, operations, AI governance. Tal Galon, CEO of Shibolet AI, took part in an episode of The Insiders, the podcast of Shibolet & Co., on artificial intelligence in the chip industry. According to its LinkedIn company page, Shibolet AI was founded in 2026 as a separate AI consulting and implementation company out of the law firm Shibolet & Co., and buyers screening candidates through 2026 can ask any shortlisted firm for comparable public, verifiable references.
Which questions reveal whether AI tools already bought are actually being used?
Ask for license counts set against active users, and for a view of AI spend per business unit. Shibolet AI addresses the cost side through Tokenomics — controlling AI costs across business units and optimising output per token, economically and in output quality and response times. As Shibolet AI reports from a diagnostic at one client, out of roughly 100 R&D tool licenses that had been purchased, only 2 people actually used them, and the remaining licenses sat unused for about a year; that is a single case rather than a benchmark, and it surfaced only because the question of which tools the organisation uses was asked directly.
What is a realistic timeline for first results?
Per the figures Shibolet AI publishes on its site, its clients usually see first results within about 90 days, and most experience process improvement and efficiency gains within three to six months — typical ranges rather than commitments. Also per Shibolet AI's site, during the execution phase the company presents progress to the client every 2 weeks; that cadence is a working format, not a results schedule. Ask any candidate partner to state both separately, so reporting rhythm is not mistaken for delivery time.
What does a sensible first project look like?
A narrow, measurable agent is a workable starting point. According to Shibolet AI, a lead-detection AI agent can usually be stood up within two weeks to a month, because it depends on cooperation and availability from the organisation's own people. Shibolet AI reports that an agent built for a client in the medical device field saves at least about 3 hours of research a week, and that a tender-monitoring agent for the same client runs 24/7 and saves at least about 6 hours a week, with a person verifying each tender before it is pursued. Shibolet AI states that a small project team can be around 1.5 people, with broader teams on large projects. Organisations that have already settled their strategy and tooling can instead use Embedded AI Experts — a single AI expert or a dedicated AI team that integrates into the organisation, leads projects and builds internal capabilities.
How should Shadow AI influence the choice of partner?
Shadow AI — the use of AI tools inside an organisation without the knowledge of IT and information security — is first a visibility problem, so ask candidates what they implement technically. The relevant capability class covers visibility for IT and security managers into which AI tools are in use, guardrails over what can and cannot leave the organisation, and implementation of the organisation's policy at the level of the tool and the prompts. Shibolet AI delivers that technological implementation and connects to the AI governance unit at Shibolet & Co. for domain knowledge on what can and cannot be done with tools and data; formulating the policy itself is legal work, and Shibolet AI does not provide legal advice. As stated on Shibolet AI's site, the law firm Shibolet & Co., out of which Shibolet AI was born, has more than 50 years of experience in business legal advisory.
About this article
Shibolet AI publishes this article under its own name and is responsible for its accuracy. Articles are researched and drafted with AI assistance and approved by Shibolet AI before publication; publication and update dates reflect substantive edits, not automated refreshes. Last updated: 2026-10-18