When beginning their AI initiatives, many corporations are trapped in silos and deal with AI as a purely technical enterprise, sidelining area consultants or involving them too late. They find yourself with generic AI purposes that miss {industry} nuances, produce poor suggestions, and rapidly turn out to be unpopular with customers. In contrast, AI techniques that deeply perceive industry-specific processes, constraints, and choice logic have the next advantages:
- Elevated effectivity — The extra area data AI incorporates, the much less guide effort is required from human consultants.
- Improved adoption — Consultants disengage from AI techniques that really feel too generic. AI should communicate their language and align with actual workflows to realize belief.
- A sustainable aggressive moat — As AI turns into a commodity, embedding proprietary experience is the simplest approach to construct defensible AI techniques (cf. this article to be taught concerning the constructing blocks of AI’s aggressive benefit).
Area consultants can assist you join the dots between the technicalities of an AI system and its real-life utilization and worth. Thus, they need to be key stakeholders and co-creators of your AI purposes. This information is the primary a part of my sequence on expertise-driven AI. Following my mental model of AI systems, it gives a structured strategy to embedding deep area experience into your AI.
All through the article, we are going to use the use case of provide chain optimisation (SCO) for example these completely different strategies. Fashionable provide chains are underneath fixed pressure from geopolitical tensions, local weather disruptions, and risky demand shifts, and AI can present the type of dynamic, high-coverage intelligence wanted to anticipate delays, handle dangers, and optimise logistics. Nevertheless, with out area experience, these techniques are sometimes disconnected from the realities of life. Let’s see how we are able to resolve this by integrating area experience throughout the completely different elements of the AI utility.
AI is simply as domain-aware as the info it learns from. Uncooked knowledge isn’t sufficient — it have to be curated, refined, and contextualised by consultants who perceive its which means in the true world.
Knowledge understanding: Educating AI what issues
Whereas knowledge scientists can construct refined fashions to analyse patterns and distributions, these analyses usually keep at a theoretical, summary stage. Solely area consultants can validate whether or not the info is full, correct, and consultant of real-world situations.
In provide chain optimisation, for instance, cargo data could comprise lacking supply timestamps, inconsistent route particulars, or unexplained fluctuations in transit occasions. A knowledge scientist would possibly discard these as noise, however a logistics knowledgeable might have real-world explanations of those inconsistencies. For example, they may be attributable to weather-related delays, seasonal port congestion, or provider reliability points. If these nuances aren’t accounted for, the AI would possibly be taught an excessively simplified view of provide chain dynamics, leading to deceptive danger assessments and poor suggestions.
Consultants additionally play a crucial function in assessing the completeness of information. AI fashions work with what they’ve, assuming that every one key components are already current. It takes human experience and judgment to establish blind spots. For instance, in case your provide chain AI isn’t skilled on customs clearance occasions or manufacturing facility shutdown histories, it received’t be capable to predict disruptions attributable to regulatory points or manufacturing bottlenecks.
✅ Implementation tip: Run joint Exploratory Knowledge Evaluation (EDA) classes with knowledge scientists and area consultants to establish lacking business-critical data, making certain AI fashions work with a whole and significant dataset, not simply statistically clear knowledge.
One frequent pitfall when beginning with AI is integrating an excessive amount of knowledge too quickly, resulting in complexity, congestion of your knowledge pipelines, and blurred or noisy insights. As a substitute, begin with a few high-impact knowledge sources and increase incrementally primarily based on AI efficiency and person wants. For example, an SCO system could initially use historic cargo knowledge and provider reliability scores. Over time, area consultants could establish lacking data — equivalent to port congestion knowledge or real-time climate forecasts — and level engineers to these knowledge sources the place it may be discovered.
✅ Implementation tip: Begin with a minimal, high-value dataset (usually 3–5 knowledge sources), then increase incrementally primarily based on knowledgeable suggestions and real-world AI efficiency.
AI fashions be taught by detecting patterns in knowledge, however generally, the precise studying alerts aren’t but current in uncooked knowledge. That is the place knowledge annotation is available in — by labelling key attributes, area consultants assist the AI perceive what issues and make higher predictions. Contemplate an AI mannequin constructed to foretell provider reliability. The mannequin is skilled on cargo data, which comprise supply occasions, delays, and transit routes. Nevertheless, uncooked supply knowledge alone doesn’t seize the total image of provider danger — there aren’t any direct labels indicating whether or not a provider is “excessive danger” or “low danger.”
With out extra specific studying alerts, the AI would possibly make the flawed conclusions. It might conclude that every one delays are equally unhealthy, even when some are attributable to predictable seasonal fluctuations. Or it’d overlook early warning indicators of provider instability, equivalent to frequent last-minute order adjustments or inconsistent stock ranges.
Area consultants can enrich the info with extra nuanced labels, equivalent to provider danger classes, disruption causes, and exception-handling guidelines. By introducing these curated studying alerts, you’ll be able to be certain that AI doesn’t simply memorise previous tendencies however learns significant, decision-ready insights.
You shouldn’t rush your annotation efforts — as an alternative, take into consideration a structured annotation course of that features the next elements:
- Annotation pointers: Set up clear, standardized guidelines for labeling knowledge to make sure consistency. For instance, provider danger classes must be primarily based on outlined thresholds (e.g., supply delays over 5 days + monetary instability = excessive danger).
- A number of knowledgeable evaluate: Contain a number of area consultants to cut back bias and guarantee objectivity, notably for subjective classifications like danger ranges or disruption influence.
- Granular labelling: Seize each direct and contextual components, equivalent to annotating not simply cargo delays but additionally the trigger (customs, climate, provider fault).
- Steady refinement: Often audit and refine annotations primarily based on AI efficiency — if predictions constantly miss key dangers, consultants ought to regulate labelling methods accordingly.
✅ Implementation tip: Outline an annotation playbook with clear labelling standards, contain no less than two area consultants per crucial label for objectivity, and run common annotation evaluate cycles to make sure AI is studying from correct, business-relevant insights.
To this point, our AI fashions be taught from real-life historic knowledge. Nevertheless, uncommon, high-impact occasions — like manufacturing facility shutdowns, port closures, or regulatory shifts in our provide chain state of affairs — could also be underrepresented. With out publicity to those eventualities, AI can fail to anticipate main dangers, resulting in overconfidence in provider stability and poor contingency planning. Artificial knowledge solves this by creating extra datapoints for uncommon occasions, however knowledgeable oversight is essential to make sure that it displays believable dangers moderately than unrealistic patterns.
Let’s say we wish to predict provider reliability in our provide chain system. The historic knowledge could have few recorded provider failures — however that’s not as a result of failures don’t occur. Reasonably, many corporations proactively mitigate dangers earlier than they escalate. With out artificial examples, AI would possibly deduce that provider defaults are extraordinarily uncommon, resulting in misguided danger assessments.
Consultants can assist generate artificial failure eventualities primarily based on:
- Historic patterns — Simulating provider collapses triggered by financial downturns, regulatory shifts, or geopolitical tensions.
- Hidden danger indicators — Coaching AI on unrecorded early warning indicators, like monetary instability or management adjustments.
- Counterfactuals — Creating “what-if” occasions, equivalent to a semiconductor provider out of the blue halting manufacturing or a protracted port strike.
✅ Actionable step: Work with area consultants to outline high-impact however low-frequency occasions and eventualities, which may be in focus once you generate artificial knowledge.
Knowledge makes area experience shine. An AI initiative that depends on clear, related, and enriched area knowledge can have an apparent aggressive benefit over one which takes the “quick-and-dirty” shortcut to knowledge. Nevertheless, remember that working with knowledge may be tedious, and consultants must see the result of their efforts — whether or not it’s enhancing AI-driven danger assessments, optimising provide chain resilience, or enabling smarter decision-making. The secret is to make knowledge collaboration intuitive, purpose-driven, and immediately tied to enterprise outcomes, so consultants stay engaged and motivated.
As soon as AI has entry to high-quality knowledge, the subsequent problem is making certain it generates helpful and correct outputs. Area experience is required to:
- Outline clear AI aims aligned with enterprise priorities
- Guarantee AI appropriately interprets industry-specific knowledge
- Repeatedly validate AI’s outputs and suggestions
Let’s have a look at some frequent AI approaches and see how they’ll profit from an additional shot of area data.
Coaching predictive fashions from scratch
For structured issues like provide chain forecasting, predictive fashions equivalent to classification and regression can assist anticipate delays and counsel optimisations. Nevertheless, to ensure these fashions are aligned with enterprise targets, knowledge scientists and data engineers must work collectively. For instance, an AI mannequin would possibly attempt to minimise cargo delays in any respect prices, however a provide chain knowledgeable is aware of that fast-tracking each cargo by way of air freight is financially unsustainable. They will formulate further constraints on the mannequin, making it prioritise crucial shipments whereas balancing value, danger, and lead occasions.
✅ Implementation tip: Outline clear aims and constraints with area consultants earlier than coaching AI fashions, making certain alignment with actual enterprise priorities.
For an in depth overview of predictive AI methods, please seek advice from Chapter 4 of my e-book The Art of AI Product Management.
Navigating the LLM triad
Whereas predictive fashions skilled from scratch can excel at very particular duties, they’re additionally inflexible and can “refuse” to carry out some other activity. GenAI fashions are extra open-minded and can be utilized for extremely numerous requests. For instance, an LLM-based conversational widget in an SCO system can enable customers to work together with real-time insights utilizing pure language. As a substitute of sifting by way of rigid dashboards, customers can ask, “Which suppliers are prone to delays?” or “What various routes can be found?” The AI pulls from historic knowledge, dwell logistics feeds, and exterior danger components to offer actionable solutions, counsel mitigations, and even automate workflows like rerouting shipments.
However how can you make sure that an enormous, out-of-the-box mannequin like ChatGPT or Llama understands the nuances of your area? Let’s stroll by way of the LLM triad — a development of methods to include area data into your LLM system.
As you progress from left to proper, you’ll be able to ingrain extra area data into the LLM — nevertheless, every stage additionally provides new technical challenges (in case you are taken with a scientific deep-dive into the LLM triad, please take a look at chapters 5–8 of my e-book The Art of AI Product Management). Right here, let’s deal with how area consultants can leap in at every of the phases:
- Prompting out-of-the-box LLMs would possibly seem to be a generic strategy, however with the precise instinct and ability, area consultants can fine-tune prompts to extract the additional little bit of area data out of the LLM. Personally, I believe this can be a huge a part of the fascination round prompting — it places essentially the most highly effective AI fashions immediately into the arms of area consultants with none technical experience. Some key prompting methods embrace:
- Few-shot prompting: Incorporate examples to information the mannequin’s responses. As a substitute of simply asking “What are various transport routes?”, a well-crafted immediate contains pattern eventualities, equivalent to “Instance of previous state of affairs: A earlier delay on the Port of Shenzhen was mitigated by rerouting by way of Ho Chi Minh Metropolis, lowering transit time by 3 days.”
- Chain-of-thought prompting: Encourage step-by-step reasoning for complicated logistics queries. As a substitute of “Why is my cargo delayed?”, a structured immediate may be “Analyse historic supply knowledge, climate experiences, and customs processing occasions to find out why cargo #12345 is delayed.”
- Offering additional background data: Connect exterior paperwork to enhance domain-specific responses. For instance, prompts might reference real-time port congestion experiences, provider contracts, or danger assessments to generate data-backed suggestions. Most LLM interfaces already assist you to conveniently connect further information to your immediate.
2. RAG (Retrieval-Augmented Technology): Whereas prompting helps information AI, it nonetheless depends on pre-trained data, which can be outdated or incomplete. RAG permits AI to retrieve real-time, company-specific knowledge, making certain that its responses are grounded in present logistics experiences, provider efficiency data, and danger assessments. For instance, as an alternative of producing generic provider danger analyses, a RAG-powered AI system would pull real-time cargo knowledge, provider credit score scores, and port congestion experiences earlier than making suggestions. Area consultants can assist choose and construction these knowledge sources and are additionally wanted with regards to testing and evaluating RAG techniques.
✅ Implementation tip: Work with area consultants to curate and construction data sources — making certain AI retrieves and applies solely essentially the most related and high-quality enterprise data.
3. Wonderful-tuning: Whereas prompting and RAG inject area data on-the-fly, they don’t inherently embed provide domain-specific workflows, terminology, or choice logic into your LLM. Wonderful-tuning adapts the LLM to assume like a logistics knowledgeable. Area consultants can information this course of by creating high-quality coaching knowledge, making certain AI learns from actual provider assessments, danger evaluations, and procurement selections. They will refine {industry} terminology to forestall misinterpretations (e.g., AI distinguishing between “buffer inventory” and “security inventory”). In addition they align AI’s reasoning with enterprise logic, making certain it considers value, danger, and compliance — not simply effectivity. Lastly, they consider fine-tuned fashions, testing AI in opposition to real-world selections to catch biases or blind spots.
✅ Implementation tip: In LLM fine-tuning, knowledge is the essential success issue. High quality goes over amount, and fine-tuning on a small, high-quality dataset can provide you wonderful outcomes. Thus, give your consultants sufficient time to determine the precise construction and content material of the fine-tuning knowledge and plan for loads of end-to-end iterations of your fine-tuning course of.
Encoding knowledgeable data with neuro-symbolic AI
Each machine studying algorithm will get it flawed now and again. To mitigate errors, it helps to set the “exhausting information” of your area in stone, making your AI system extra dependable and controllable. This mixture of machine studying and deterministic guidelines is named neuro-symbolic AI.
For instance, an specific data graph can encode provider relationships, regulatory constraints, transportation networks, and danger dependencies in a structured, interconnected format.
As a substitute of relying purely on statistical correlations, an AI system enriched with data graphs can:
- Validate predictions in opposition to domain-specific guidelines (e.g., making certain that AI-generated provider suggestions adjust to regulatory necessities).
- Infer lacking data (e.g., if a provider has no historic delays however shares dependencies with high-risk suppliers, AI can assess its potential danger).
- Enhance explainability by permitting AI selections to be traced again to logical, rule-based reasoning moderately than black-box statistical outputs.
How are you going to resolve which data must be encoded with guidelines (symbolic AI), and which must be discovered dynamically from the info (neural AI)? Area consultants can assist youpick these bits of data the place hard-coding makes essentially the most sense:
- Data that’s comparatively secure over time
- Data that’s exhausting to deduce from the info, for instance as a result of it isn’t well-represented
- Data that’s crucial for high-impact selections in your area, so you’ll be able to’t afford to get it flawed
Usually, this information can be saved in separate elements of your AI system, like choice bushes, data graphs, and ontologies. There are additionally some strategies to combine it immediately into LLMs and different statistical fashions, equivalent to Lamini’s memory fine-tuning.
Compound AI and workflow engineering
Producing insights and turning them into actions is a multi-step course of. Consultants can assist you mannequin workflows and decision-making pipelines, making certain that the method adopted by your AI system aligns with their duties. For instance, the next pipeline exhibits how the AI elements we thought of up to now may be mixed right into a workflow for the mitigation of cargo dangers:
Consultants are additionally wanted to calibrate the “labor distribution” between people in AI. For instance, when modelling choice logic, they’ll set thresholds for automation, deciding when AI can set off workflows versus when human approval is required.
✅ Implementation tip: Contain your area consultants in mapping your processes to AI fashions and belongings, figuring out gaps vs. steps that may already be automated.
Particularly in B2B environments, the place employees are deeply embedded of their day by day workflows, the person expertise have to be seamlessly built-in with current processes and activity constructions to make sure effectivity and adoption. For instance, an AI-powered provide chain device should align with how logistics professionals assume, work, and make selections. Within the improvement part, area consultants are the closest “friends” to your actual customers, and choosing their brains is without doubt one of the quickest methods to bridge the hole between AI capabilities and real-world usability.
✅ Implementation tip: Contain area consultants early in UX design to make sure AI interfaces are intuitive, related, and tailor-made to actual decision-making workflows.
Guaranteeing transparency and belief in AI selections
AI thinks otherwise from people, which makes us people skeptical. Usually, that’s a superb factor because it helps us keep alert to potential errors. However mistrust can be one of many greatest limitations to AI adoption. When customers don’t perceive why a system makes a selected suggestion, they’re much less more likely to work with it. Area consultants can outline how AI ought to clarify itself — making certain customers have visibility into confidence scores, choice logic, and key influencing components.
For instance, if an SCO system recommends rerouting a cargo, it will be irresponsible on the a part of a logistics planner to simply settle for it. She must see the “why” behind the advice — is it as a result of provider danger, port congestion, or gas value spikes? The UX ought to present a breakdown of the choice, backed by further data like historic knowledge, danger components, and a cost-benefit evaluation.
⚠️ Mitigate overreliance on AI: Extreme dependence of your customers on AI can introduce bias, errors, and unexpected failures. Consultants ought to discover methods to calibrate AI-driven insights vs. human experience, moral oversight, and strategic safeguards to make sure resilience, adaptability, and belief in decision-making.
✅ Implementation tip: Work with area consultants to outline key explainability options — equivalent to confidence scores, knowledge sources, and influence summaries — so customers can rapidly assess AI-driven suggestions.
Simplifying AI interactions with out dropping depth
AI instruments ought to make complicated selections simpler, not tougher. If customers want deep technical data to extract insights from AI, the system has failed from a UX perspective. Area consultants can assist strike a stability between simplicity and depth, making certain the interface gives actionable, context-aware suggestions whereas permitting deeper evaluation when wanted.
For example, as an alternative of forcing customers to manually sift by way of knowledge tables, AI might present pre-configured experiences primarily based on frequent logistics challenges. Nevertheless, knowledgeable customers must also have on-demand entry to uncooked knowledge and superior settings when vital. The secret is to design AI interactions which can be environment friendly for on a regular basis use however versatile for deep evaluation when required.
✅ Implementation tip: Use area knowledgeable suggestions to outline default views, precedence alerts, and user-configurable settings, making certain AI interfaces present each effectivity for routine duties and depth for deeper analysis and strategic selections.
Steady UX testing and iteration with consultants
AI UX isn’t a one-and-done course of — it must evolve with real-world person suggestions. Area consultants play a key function in UX testing, refinement, and iteration, making certain that AI-driven workflows keep aligned with enterprise wants and person expectations.
For instance, your preliminary interface could floor too many low-priority alerts, resulting in alert fatigue the place customers begin ignoring AI suggestions. Provide chain consultants can establish which alerts are Most worthy, permitting UX designers to prioritize high-impact insights whereas lowering noise.
✅ Implementation tip: Conduct think-aloud sessions and have area consultants verbalize their thought course of when interacting along with your AI interface. This helps AI groups uncover hidden assumptions and refine AI primarily based on how consultants really assume and make selections.
Vertical AI techniques should combine area data at each stage, and consultants ought to turn out to be key stakeholders in your AI improvement:
- They refine knowledge choice, annotation, and artificial knowledge.
- They information AI studying by way of prompting, RAG, and fine-tuning.
- They help the design of seamless person experiences that combine with day by day workflows in a clear and reliable means.
An AI system that “will get” the area of your customers is not going to solely be helpful and adopted within the short- and middle-term, but additionally contribute to the aggressive benefit of your enterprise.
Now that you’ve got discovered a bunch of strategies to include domain-specific data, you may be questioning the right way to strategy this in your organizational context. Keep tuned for my subsequent article, the place we are going to take into account the sensible challenges and methods for implementing an expertise-driven AI technique!
Observe: Except famous in any other case, all photos are the creator’s.
