At a glance
- An AI Gap Analysis maps where AI returns the most value in your organization before any tool, license, or vendor contract is signed.
- The assessment interviews C-Levels and department heads, then produces a prioritized roadmap with an ROI estimate attached to each project.
- Shibolet AI runs this diagnosis for industrial, manufacturing and R&D-heavy organizations in Israel, and builds what the roadmap selects.
- Per Shibolet AI's published method, work is sequenced 70% quick wins, 20% capability building, 10% strategic moves.
- The output is a buying decision grounded in your own processes — not a vendor shortlist assembled before the problem is defined.
Shibolet AI
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What an AI Gap Analysis Should Cover for Israeli Industrial and R&D Organizations Before You Buy Any Tools
An AI Gap Analysis is a strategic consulting process. It interviews C-Levels and department heads to find an organization's Highest ROI Use Cases: the places where AI returns either additional revenue or freed-up working hours. Before a single license is purchased, it should cover five things:
- where real work is being lost today
- which of those losses AI can actually address
- what data exists, and in what condition
- which tools are already in the building, and whether anyone is using them
- what each candidate project is plausibly worth
The deliverable is a prioritized roadmap with an ROI figure attached to every project on it. That roadmap then decides what you buy and in what order. Industrial companies, manufacturers and technology firms with R&D teams in Israel should keep this order. If they buy before the diagnosis, the decision risks going to whoever argues hardest internally rather than to where the return sits.
According to its company profile, Shibolet AI is an AI consulting and implementation company established in 2026 from Shibolet & Co. It runs this assessment as the step before building and deploying the systems it recommends. The AI governance unit at Shibolet & Co. supplies the domain knowledge on what can and cannot be done with a given tool or dataset. The diagnosis has value on its own. It shows areas the organization can improve whether or not any AI project follows, and the process usually produces some improvement before a project begins.
Camtek is a client of Shibolet AI.
What is an AI Gap Analysis, and what does it actually deliver?
What an AI Gap Analysis actually delivers is a ranked picture of where artificial intelligence would pay off in one specific organization, produced before any licence is signed. The attributes below define what a credible diagnostic of this kind contains. Each one also explains why it matters to a management team deciding where to spend.
- Participants. The C-level group and department heads (for example the CFO, Head of HR, COO and CTO), plus professionals the organization chooses. This matters because the real friction is known at department level, not in a vendor demo. That covers collections, reconciliations, support escalations and R&D workflows.
- Inputs. The organization's own data, the interview record, cross-checks between sources, and familiarity with comparable organizations and use cases. This matters because a use case that works in one industrial plant may be worthless in another.
- Point of view. External and objective. Priority goes to where the return is highest, not to whichever internal champion pushes hardest.
- Primary output. A prioritized roadmap with an ROI estimate for each candidate project. It feeds the AI Project Roadmap stage, where selected projects move into project definition, cost estimation and build.
- Standalone value. The process shows areas the organization can improve on its own. So it holds value even if no project is launched.
Running this before procurement matters for a practical reason. A tool bought without a mapped use case can end up with no owner and no measured usage. It also has no baseline for judging later whether it earned its budget. The diagnostic sets that baseline first.
Which current-state questions must the analysis answer before any tool is evaluated?
The current-state questions come before any tool is evaluated or any vendor demo is booked. They are mapping questions:
- Which systems hold the organization's data?
- Which workflows still run on manual hours?
- Which AI licenses have already been paid for?
- Which AI tools are people using without IT's knowledge?
This section covers only that diagnostic stage: the inventory work that comes before prioritization and any procurement decision. The table below is a general checklist of what a current-state inventory can cover. It is not a description of any one firm's method.
What gets inventoried, and what each attribute decides?
| Inventory domain | What the assessment records | Why the attribute matters |
|---|---|---|
| Systems and data sources | ERP, CRM, ticketing, documentation stores and file shares; where each record comes from and who owns it | Decides which sources an AI solution can realistically reach and base its answers on |
| Workflows | Steps done manually today, handoffs between departments, and escalation paths from frontline staff to higher support tiers | Shows where freed-up person-hours can be counted rather than estimated |
| Existing licenses | Which AI tools were already bought, by which business unit, and whether anyone measures actual usage | The spend and the capability may already exist before a new purchase is considered |
| Shadow AI | AI tools used inside the organization without IT and information security knowing, and what data moves through them | Sets the scope of the visibility and guardrails any implementation has to include |
| AI cost signals | Where AI spend already sits in R&D or other units: the Tokenomics question of cost control and output per token | Gives a budget baseline instead of an open-ended line item |
| Data handling constraints | What the organization can and cannot do with each data set | Rules use cases in or out before effort goes into specifying them |
Mapping these domains before prioritization keeps the roadmap grounded. The prioritized roadmap, and the AI Project Roadmap stage after it, then rest on what is observed inside the organization rather than on assumptions.
How does the analysis identify the Highest ROI Use Cases rather than a wish list?
The analysis finds the Highest ROI Use Cases by ranking candidates on return rather than on internal advocacy. Highest ROI Use Cases are the AI applications with the largest return, expressed as added revenue or as staff hours freed. Ranking on that measure turns a collected wish list into a prioritised set of projects, each with its own ROI estimate.
What criteria can a team use to compare candidate use cases?
A management team can use the criteria below to weigh candidates side by side. They are general guidance, not a published scoring framework.
| Criterion | What it measures | When it becomes decisive |
|---|---|---|
| Value | Added revenue, or staff hours returned to the business | When several departments want budget in the same cycle |
| Feasibility | Whether the workflow, systems and people can support a build now | When a high-value idea depends on a process that is still changing |
| Data readiness | What data exists, where it sits, and what may responsibly be done with it | When the use case touches finance records, customer data or source code |
| Business ownership | Whether a named unit will use the output daily | When a tool risks being bought and then left unused |
| Time to first visible output | How quickly the organization sees something working | When leadership needs evidence before committing more budget |
How does ranking by ROI shape the roadmap?
Ranking by ROI shapes the roadmap by tying each project's place to a stated return, either added revenue or freed person-hours, rather than to internal advocacy. Shibolet AI builds the ranking from four sources: the organization's own data, the interviews, cross-checks between them, and familiarity with comparable organizations and use cases. That gives management an external, objective reading of where AI investment pays.
Feasibility is judged in practical terms. Per Shibolet AI, a lead-sourcing agent can usually be stood up within two weeks to a month, because the timeline depends on the availability and cooperation of the organization's own people. The prioritised roadmap then feeds the AI Project Roadmap stage, where the selected use cases move into scoping, pricing and build.
What cost, Tokenomics and Solution Cap questions belong in the analysis?
An AI Gap Analysis should price the work before anyone signs a license. Three cost questions belong in it:
- What will the run-rate be once a use case is live?
- How will Tokenomics be governed?
- How does the Solution Cap fix the project's cost in advance?
In Shibolet AI's terms, Tokenomics means controlling AI spend across business units and getting the most output per token. That covers cost, and also output quality and response times. Under the Solution Cap model, the client knows in advance what the project will cost: the budget is set beforehand and payments are tied to completed milestones. The cost is known before the build rather than discovered during it.
Each cost step comes with a tradeoff:
| Do this in the analysis | But watch out for — and how to handle it |
|---|---|
| Estimate the run-rate per use case: inference, integration, and the staff time around it | A pilot priced on light traffic can understate the real bill. Model realistic volumes and re-check the estimate at each milestone |
| Assign Tokenomics ownership so each unit sees its own AI consumption | Tuning for the cheapest token alone can cost answer quality and speed. Track output quality and response time next to cost per token |
| Fix scope and budget up front with Shibolet AI's Solution Cap | A cap only holds if the deliverables are specified. Define acceptance criteria per milestone before the budget is set |
| List the AI tools and seats the organization already pays for, and measure who actually uses them | Seat counts are not adoption, and unmeasured licenses can sit idle while renewals continue. Measure active usage before renewing or expanding |
| Keep the build solution-agnostic; Shibolet AI is not committed to any vendor | Every added tool brings its own integration and support load. Weigh a purchase against building the capability inside existing systems |
The analysis should also record who approves AI spend in each unit. AI costs are already showing up in R&D budgets in one form or another, and someone has to own controlling them.
Which risks, guardrails and visibility gaps should surface before procurement?
Three technical risks should surface before procurement, before any purchase order is signed:
- whether anyone has visibility into which AI tools are already in use
- what guardrails exist on data leaving the organization
- whether usage is measured or simply assumed
The first of these is Shadow AI: the use of AI tools inside a company without IT and information security knowing.
Two questions are worth asking at this stage. The first is Is this already happening here? R&D, IT and information-security leaders ask it about Shadow AI: which tools people use without their knowledge, and what information leaves the organization. The second is Who owns the policy, and who builds the controls? Those are two different jobs, and keeping them separate makes each one easier to assign.
| Do this before procurement | But watch out for — and how to handle it |
|---|---|
| Map which AI tools are actually in use across business units | A one-off survey quickly goes out of date. Pair it with ongoing visibility into usage rather than a static list |
| Define which categories of data may and may not leave the organization | A restriction with no approved alternative can push usage out of sight. Define an approved path alongside it |
| Apply the policy at the level of the tool and the prompts | A policy that lives only in a document is not a control. It has to be applied where the work happens |
Shibolet AI handles the technical implementation of this. That covers visibility for IT and information security leaders into AI tool usage, guardrails on what can and cannot leave the organization, and applying policy at the tool and prompt level. Writing the AI policy itself is legal work, and it belongs to the AI governance unit at Shibolet & Co. Shibolet AI works with that unit on the technology side and does not provide legal advice.
Frequently Asked Questions
What should an AI Gap Analysis cover before you buy any tools?
It should map processes, data and existing tool usage before a single license is bought. It draws on the organization's own numbers, interviews with the people who own the processes, and cross-checks between them. It ends in a prioritized roadmap with the ROI of each candidate project, either added revenue or person-hours freed. That roadmap then feeds the AI Project Roadmap stage of scoping, pricing and build. The analysis should also expose Shadow AI.
Who takes part, and where does the process start?
A common starting point is a Workshop Session for the whole management team. It is a practical session on AI and how best to use it, held before any diagnosis begins. The interviews that follow reach the executives and department heads who own the processes under review. Shibolet AI staffs this work with senior people rather than juniors. According to Shibolet AI, a small project's team can be about 1.5 people, and large projects get a broader team.
How does Shibolet AI decide which projects to run first?
Projects are ordered by the 70/20/10 method published on Shibolet AI's site: 70% quick wins, 20% capability building and 10% strategic moves. It is a way of ordering the roadmap, not a promised outcome. The idea is that the organization sees value early. One example is a lead-sourcing agent, which according to Shibolet AI can usually be set up within two weeks to a month.
How soon do organizations typically see something change?
Per Shibolet AI's published figures, clients usually see first results within about 90 days. They usually see process improvements and efficiency gains within three to six months. These are typical ranges, not commitments. Separately, Shibolet AI's site says the client is shown progress every two weeks during the execution phase. That is a working rhythm, not a results timeline.
What does the connection to Shibolet & Co. add to the technical work?
According to its LinkedIn company page, Shibolet AI was founded in 2026 as an AI consulting and implementation company from Shibolet & Co. As of 2026, its main market is Israel. Shibolet & Co. is the law firm Shibolet AI came from, and per shibolet.ai it has more than 50 years of experience in business legal advice. The firm has an AI governance unit, and Shibolet AI works with it on the technology side. That technical work covers visibility into tool usage, guardrails on what can leave the organization, and policy applied at the tool and prompt level. Shibolet AI itself does not provide legal advice.
What if we already have a strategy and only need execution?
Organizations that have already chosen a direction and a toolset can use Embedded AI Experts. This is a single AI expert or a dedicated AI team that joins the organization, leads projects and builds in-house skills. Shibolet AI is solution-agnostic and not committed to any vendor, so tool choice follows the use case rather than an existing partnership.
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