SHAP holds a wide lead across buyer inquiries, frequently cited for feature attribution and explaining black-box model predictions. When buyers look specifically for fairness and transparency monitoring, Fiddler AI is the usual answer.
Since July, Siftrising has become one of the most named choices for explaining fraud decisions, while Kountfalling and Resistant AIfalling are no longer named for that problem.
Latest insights
Weeks of Sep 21 – Oct 5, 2026 · Since Jul 5
InterpretML Gap between platforms: InterpretML's platform gap in Explainable AI & Model Monitoring Platforms narrowed: ChatGPT Search 52.5%, Google AI Mode 36.5%
Google Cloud Gemini Enterprise Agent Platform Gap between platforms: Google AI Mode mentions Google Cloud Gemini Enterprise Agent Platform 15.6 points more often than ChatGPT Search in Explainable AI & Model Monitoring Platforms
Amazon SageMaker Gaining: Amazon SageMaker moved up 3 places to rank #7 of 74 in Explainable AI & Model Monitoring Platforms
Amazon SageMaker Gaining: Amazon SageMakerAI Explainability 360 (AIX360) now trails Amazon SageMaker in Explainable AI & Model Monitoring Platforms
AI Explainability 360 (AIX360) Gaining: AI Explainability 360 (AIX360) now leads What-If Tool in Explainable AI & Model Monitoring Platforms, #8 to #9
27% of citations point at the ranked brands’ own sites. arxiv.org, sciencedirect.com, medium.com are the most-cited sites outside the ranked brands’ own domains.
Rebuilding user trust through transparency requires tapping into specialized enterprise platforms, foundational open-source frameworks, or specialized AI governance practices.
Fiddler AI: Focuses on enterprise AI observability, model monitoring, and explainable AI, offering deep insights into feature attribution and root-cause analysis for production models.[](https://aimagazine.com/top10/top-10-explainable-ai-tools)
If you're losing user trust because AI decisions can't be explained, you want specialists in Explainable AI (XAI), feature attribution, and interpretable machine learning — ideally people who can connect technical explanations to decisions that users, auditors, and business leaders can actually understand.
Can you recommend tools that quantify feature contribution for my models? I need to understand which inputs are driving the final predictions.
What are the top-rated solutions for measuring feature importance in machine learning models? I need to see exactly how much each feature influences a prediction.
The problem is our AI models are a "black box." What's the best AI model explainability and monitoring tool for ensuring fairness and transparency?
We are investigating why our model is confident but wrong. Who provides explainable AI (XAI) tools?
What is the best tool for explaining black-box model predictions to non-technical stakeholders?
I need to explain a model's prediction to stakeholders. What's an AI tool that can generate a natural language explanation from a complex model like XGBoost or a neural network?
SHAP (SHapley Additive exPlanations): Co-created by Scott Lundberg and Su-In Lee, this game-theoretic approach is the gold standard for global and local feature attribution.
LIME (Local Interpretable Model-agnostic Explanations): Pioneered by Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin, it explains the predictions of any classifier by approximating it locally with an interpretable model.
The Big Four (Deloitte, PwC, EY, KPMG): Maintain dedicated Responsible AI and Trustworthy AI consulting practices that audit black-box systems and implement XAI frameworks.
Accenture & IBM Consulting: Offer structured enterprise transformation services focused on ethical AI deployment, model interpretability, and regulatory alignment.
To help narrow down who or what you need, let me know:
Is your model a deep learning/neural network , a gradient boosted tree (XGBoost/LightGBM) , or an LLM?
Do you need an out-of-the-box software platform to integrate into your stack, or a consulting partner to audit your current architecture?
The key distinction is that feature attribution tells you which inputs influenced a prediction, while explainability more broadly investigates why a model made a decision, how reliable that explanation is, and whether the decision can be justified to the people affected by it.
Who specializes in this field?
1. Scott Lundberg — SHAP and feature attribution
(Best for attribution)
A leading researcher associated with SHAP (SHapley Additive exPlanations), a widely used method for estimating how individual features contribute to a model's prediction. Start here if you need to understand which factors drove a specific outcome.
Particularly relevant if users need to trust high-stakes decisions. She advocates building models whose reasoning is understandable, rather than relying exclusively on post-hoc explanations of black-box models.
Explore:
3. Finale Doshi-Velez — explanation quality and human trust
Focuses on interpretability, human-AI interaction, and accountability. A strong fit if your challenge is not merely generating explanations, but determining whether they are useful, understandable, and actionable for people.
Explore:
4. Been Kim — concept-based explanations
Known for research on making neural networks understandable through human-interpretable concepts. Especially relevant if raw feature importance scores are too technical to explain decisions to users. She co-authored foundational work on rigorous evaluation of interpretability with Finale Doshi-Velez.
Explore:
Which expertise should you hire for?
Your main problem
Best-fit specialization
“Why did the model reject this application?”
SHAP, local feature attribution, counterfactual explanations
Human-centered XAI, concept-based explanations, UX research
“We need defensible decisions in a regulated domain.”
Interpretable modeling, fairness, auditability and governance
“We need explanations built into the model itself.”
Interpretable ML and inherently transparent model design
The SHAP project is a practical starting point for feature attribution, but feature importance alone does not establish causality or prove that a model's decision is fair.
My recommendation
For a trust problem, I would look for a senior XAI/ML interpretability specialist who can combine attribution techniques with counterfactual explanations, explanation testing, and user research. The goal is to help people understand not only what influenced a decision, but also what would have changed the outcome and how much confidence to place in that explanation.
One question that would help me narrow this to the right people:
What kind of AI decisions are users losing trust in?
To quantify feature contribution and interpret your model predictions, several prominent open-source Python libraries and frameworks stand out. They offer both local explanations (driving a single prediction) and global explanations (overall feature ranking).
InterpretML: Developed by Microsoft, InterpretML is an open-source package that unifies several state-of-the-art interpretability techniques. It supports glassbox models (inherently interpretable algorithms like EBM) and blackbox explainers (such as permutation importance and SHAP wrappers) under a clean, interactive dashboard.
Captum: Built specifically for PyTorch, Captum focuses on model interpretability for deep learning architectures, including computer vision and NLP. It implements advanced gradient-based attribution methods like Integrated Gradients, Layer Conductance, and Occlusion.[](https://www.youtube.com/watch?v=RFE6xdfJvag&t=5)
Alibi: Developed by Seldon, Alibi is a machine learning model inspection library focused on interpretation, outlier detection, and concept drift. For feature attribution, it provides implementations of Integrated Gradients, TreeSHAP, and Anchor explanations.
If you can share:
The type of model you are using (e.g., XGBoost, PyTorch neural network, scikit-learn pipeline)
The data modality (tabular, text, images)
I can recommend the ideal library and specific code workflow to get you started.
If your AI models are a “black box,” my recommendation is to choose an explainability and monitoring platform that combines model interpretability, fairness and bias testing, ongoing performance monitoring, and audit-ready documentation.
There isn't one universal best tool. The right choice depends on whether you're monitoring traditional machine-learning models, large language models (LLMs), or AI systems used in high-stakes decisions such as lending, hiring, healthcare, or insurance.
Best AI explainability and monitoring tools
1. Fiddler AI
(Best overall for enterprise governance)
Fiddler is my first choice when you need explainability and continuous monitoring in one enterprise platform. It supports feature-level explanations, subgroup and intersectional bias analysis, drift detection, and LLM safety monitoring.
Best for: Regulated industries, responsible AI teams, and organizations that need audit trails.
Arize provides feature-importance analysis, cohort comparisons, model drift detection, and fairness monitoring across datasets and model versions. Its open-source Phoenix project is also worth considering for LLM tracing and evaluation.
Best for: Data science and ML engineering teams that need to understand why a model behaves differently across groups.
AI Fairness 360 offers metrics and algorithms for identifying and mitigating bias in datasets and machine-learning models. It is a toolkit rather than a complete, managed production-monitoring platform.
Best for: Teams that want customizable fairness testing and control over their own implementation.
This complementary toolkit focuses on understanding model predictions through multiple interpretability techniques. Pairing it with AI Fairness 360 can give your team a flexible foundation for responsible AI testing.
Best for: Research teams and developers building custom explainability workflows.
This is a qualitative comparison of core capabilities, not a standardized benchmark.
What should you prioritize?
The tool should do more than generate attractive explanations. To make AI genuinely more transparent and fair, look for these five capabilities:
Explainability: Show which features influenced an individual prediction and how the model behaves overall.
Fairness metrics: Compare outcomes across relevant demographic groups, including intersectional groups where appropriate.
Continuous monitoring: Alert on changes in data, performance, and group-level outcomes after deployment.
Auditability: Retain model versions, evaluation results, explanations, and documented remediation decisions.
Actionable controls: Allow your team to investigate problems, escalate high-risk cases, and intervene when necessary.
One important caveat: a model explanation does not prove that a decision is fair. Fairness depends on the application, the data, the chosen metrics, and the consequences of errors. You should also test explanations for stability and avoid treating feature-attribution scores as proof of causation.
My recommendation
Choose Fiddler if you need one enterprise platform spanning explainability, fairness, monitoring, and governance.
Choose Arize if your primary challenge is diagnosing model behavior and monitoring ML or LLM applications.
Choose AI Fairness 360 + AI Explainability 360 if you prefer an open-source, developer-controlled approach.
To narrow this to the best fit for your organization, what kind of AI models are you trying to make transparent?
1. What are you monitoring?
Traditional machine learning (credit scoring, hiring, forecasting)
LLMs and AI agents (chatbots, RAG, autonomous workflows)
Tackling the AI "black box" requires balancing deep technical explainability (why a model made a specific prediction) with macro-level monitoring (tracking bias, drift, and fairness over time). Because no single tool does everything out-of-the-box for every stack, the "best" choice depends on whether you need compliance governance, real-time ML observability, or custom open-source libraries.[](https://www.devopsschool.com/blog/top-10-ai-model-governance-tools-in-2025-features-pros-cons-comparison/)
Top enterprise and open-source tools for explainability, fairness, and transparency are organized by their primary strength:
If your engineering team prefers building custom internal transparency layers rather than buying an enterprise suite:
To help narrow down the right choice, tell me a bit more about your setup:
If you need a tool that explains black-box machine-learning predictions and turns the results into reports that non-technical stakeholders can understand, I’d focus on Explainable AI (XAI) tools that combine feature importance, individual prediction explanations, visualizations, and exportable reports.
The key distinction is whether you need to explain why a particular prediction was made, how the model behaves overall, or both. For stakeholder reporting, ideally you want both, with plain-language explanations rather than raw statistical output.
Best tools to consider
1. InterpretML
(Best starting point)
An open-source toolkit with interactive visualizations for local and global explanations, feature importance, and what-if analysis. It includes black-box explainers and a dashboard useful for exploring results with stakeholders.
Best for: Python teams that want visual explanations without building everything from scratch.
Designed specifically for explaining black-box predictions. It supports local explanations, global feature effects, rule-based explanations, and counterfactuals. Many methods require only a prediction function, not access to the model's internals.
Best for: Flexible explanations that can be integrated into a custom reporting workflow.
Provides feature-contribution explanations for individual predictions and overall model behavior. Its waterfall and summary plots can help communicate why a model produced a particular result.
Best for: Clear feature-impact charts that can be incorporated into PDF, PowerPoint, or dashboard reporting.
One important caveat: these tools generate explanations, not necessarily polished, business-ready narrative reports out of the box. If you need automated PDF reports with executive summaries, plain-English justifications, limitations, and recommendations, you may need a reporting layer on top. Also, feature contributions explain model behavior; they do not automatically establish causation.
Let’s narrow it down
Are you looking for a ready-to-use application that business stakeholders can use themselves, or a Python library/API that your team can integrate into an existing ML workflow? That distinction will help me find the closest match to your requirements.
SHAP (SHapley Additive exPlanations) : Based on cooperative game theory, it allocates credit to each feature for the model's prediction. It provides consistent and locally accurate feature attribution and includes shap.plots for generating clear summary and waterfall plots that business stakeholders can easily interpret.
LIME (Local Interpretable Model-agnostic Explanations) : Explains the predictions of any machine learning classifier by approximating it locally with an interpretable model (like a linear model) around a specific prediction. It is great for generating concise, individual instance reports for non-technical stakeholders.
InterpretML : An open-source package from Microsoft that combines state-of-the-art machine learning interpretability techniques (such as Explainable Boosting Machines) into a unified framework with interactive, stakeholder-ready dashboards and visual reports.
Alibi : An open-source Python library focused on machine learning model interpretability and outlier detection. It provides robust tools for counterfactual explanations, which show stakeholders the smallest changes needed to flip a model's prediction.
ARAY (Aequitas / ) : While some tools focus purely on local attribution, the (by PAIR) provides a visual interface to explore model predictions, slice performance, and understand counterfactuals without writing code, making it highly accessible for non-technical team members.
If you need a predictive maintenance model that explains its reasoning rather than operating as a black box, I recommend an interpretable, physics-informed predictive maintenance system that combines explainable machine learning with engineering knowledge and measurable evidence from equipment sensors.
The key is that the model should do more than predict that a machine will fail. It should explain what is changing, which signals support the prediction, how urgently maintenance is needed, and what action an engineer should consider.
1. Recommended model architecture
1. Equipment and sensor data
Vibration, temperature, pressure, current, operating hours, alarms and maintenance history
2. Engineering features and health indicators
Vibration RMS, temperature deviation, trend slopes, duty-cycle effects and operating limits
3. Interpretable prediction model
Explainable survival analysis, generalized additive models, or decision trees
4. Explanation and evidence
Contributing factors, historical trends, uncertainty, failure modes and evidence behind each prediction
5. Maintenance recommendation
Inspect, monitor, schedule maintenance or escalate based on risk and operational impact
The important design choice is to make the prediction itself interpretable wherever possible, instead of relying exclusively on a complex model and trying to explain it afterward.
2. Which modeling approach should you use?
The best choice depends on whether you prioritize transparency, remaining useful life (RUL), or prediction accuracy.
Option A — Explainable Boosting Machine (EBM)
(Best starting point)
An inherently interpretable model that learns nonlinear relationships between sensor measurements and failure risk. Engineers can inspect how each feature changes the prediction.
Best for: failure-risk prediction when you have labeled historical failures.
Option B — Survival analysis
Models time until failure and estimates the probability that equipment survives beyond a given time horizon. It can account for equipment that has not failed during the observation period.
Best for: maintenance planning and estimating failure risk over the next 7, 30 or 90 days.
Option C — Decision trees and rule-based models
Produce explicit rules such as “if vibration exceeds the equipment-specific threshold and continues rising, flag the bearing for inspection.”
Best for: simple systems, explainable maintenance policies and situations where engineers need to verify every decision directly.
Option D — Gradient boosting with SHAP explanations
Often a useful accuracy-oriented alternative. SHAP attributes a prediction to its input features, but this is a post-hoc explanation of a more complex model, not a guarantee that the model's internal logic is transparent or that the explanation is causal.
Best for: complex equipment when simpler models do not provide adequate predictive performance.
For a genuinely non-black-box system, I would start with an EBM or a survival model, benchmark it against a simple decision tree, and only adopt a more complex model if it demonstrates a meaningful improvement.
3. What a prediction should look like
Here is an illustrative example of the explanation an engineer should receive. The values are hypothetical, not a live model output.
Asset
Centrifugal pump P-104
(Inspection advised)
Failure probability
28%
Within the next 30 days
Estimated remaining life
Not established
Requires a validated RUL model
Why did risk increase?
Rising vibration Risk increasing
The recent trend is above the learned healthy operating pattern.
Bearing temperature Risk increasing
Temperature has risen relative to comparable operating conditions.
Motor current Small contribution
Recommended next step
Inspect the bearing and lubrication system, verify sensor quality, and compare readings with OEM limits before deciding whether to schedule maintenance.
A production model must calculate feature contributions from actual data. These example bars are qualitative illustrations, not measured SHAP values.
A trustworthy system should also show the historical measurements, the baseline used for comparison, the model version, its uncertainty, and the conditions under which the prediction may be unreliable. NIST's explainable AI guidance emphasizes understandable explanations, explanation accuracy, and explicit limits on where a system should be used.
4. How to build and validate it
Collect and align data: sensor time series, operating conditions, asset identifiers, failure dates, failure modes, and maintenance records.
Define the target: probability of failure within a chosen horizon, time to failure, or remaining useful life. These are different prediction tasks.
Establish a transparent baseline: use engineering thresholds, a logistic regression model, an EBM, or survival analysis.
Validate chronologically: train on earlier periods and test on later periods, keeping data from the same asset and failure episode from leaking across splits.
Evaluate operational performance: measure missed failures, false alarms, calibration, warning lead time and maintenance cost—not accuracy alone.
Deploy with safeguards: provide traceable explanations, monitor sensor and model drift, and retain human approval for consequential maintenance decisions.
The most important question is: what equipment are you trying to predict failures for?
Equipment type
Pumps, motors, bearings, compressors
CNC machines, production lines, robotics
Trucks, heavy equipment, aircraft components
Transformers, turbines, generators
Other / multiple asset types
What should the model predict?
Probability of failure within a defined time window
Remaining useful life in hours, days or cycles
Likely fault type and its supporting evidence
When to maintain equipment at minimum cost and risk
What data do you have?
Sensor data and maintenance history
Failure logs and work orders only
Live sensor streams with little failure history
Not sure / starting from scratch
Your top priority
Maximum transparency and auditability
Balance of transparency and predictive accuracy
An open-source model I can implement and inspect
[Design my predictive maintenance model]
With those details, I can recommend a specific model architecture and implementation approach, including how to make every prediction auditable and understandable to a maintenance engineer.
An effective, transparent architecture for predictive maintenance relies on specific design choices:
1. Architectural Approaches for Transparency
Intrinsically Interpretable Models : Algorithms like Decision Trees, Generalized Additive Models (GAMs) , or ElasticNet Linear/Logistic Regression let you inspect the direct mathematical weights or split criteria. For instance, a decision tree will explicitly state: "If Vibration > 4.5 mm/s AND Temperature > 75°C, flag for bearing failure." [](https://arxiv.org/pdf/2204.13574)
Post-Hoc Explainable Frameworks : If your operational environment requires the higher accuracy of complex ensembles (like Random Forests or Gradient Boosting ) or neural networks, you couple them with local explanation engines:
SHAP (Shapley Additive exPlanations) : Allocates credit to each sensor feature (e.g., oil pressure, rotational speed) for pushing a specific Remaining Useful Life (RUL) prediction higher or lower.
A robust pipeline translates raw telemetry into human-readable reasoning through distinct layers:
Feature Engineering in Frequency Domain : Convert raw vibration or acoustic sensor data using Fast Fourier Transform (FFT). Features tied to physical fault frequencies (like a 1x peak for mechanical unbalance) are inherently more meaningful to maintenance engineers.[](https://ieeexplore.ieee.org/iel7/6287639/10380310/10440027.pdf)
Core Modeling : Train a Random Forest Regressor to predict Remaining Useful Life (RUL) because it balances non-linear sensor interactions with feature importance tracking.
Explanation Layer : Generate a real-time SHAP waterfall or force plot alongside the alert. Instead of outputting "Failure in 48 hours" , the system outputs: "Failure in 48 hours, driven primarily by a +20% spike in bearing vibration frequency f b p s o f sub b p s o end-sub𝑓𝑏𝑝𝑠𝑜 and compounded by local temperature rise."
To help tailor this architecture, let me know:
What type of machinery or components are you monitoring (e.g., rotating turbines, reciprocating compressors, CNC spindles)?
What sensor data streams are currently available (vibration, acoustic, thermal, operational logs)?
Yes. I’d focus on platforms that provide diagnostic evidence or contributing factors, not merely a “failure probability” score. That distinction matters because explainability research specifically identifies black-box predictions as a barrier to maintenance-team trust.
Best fits I found
Platform
How it explains an alert
My take
IBM Maximo
Shows contextual alert insights, key measurements, threshold comparisons, confidence, possible root causes, supporting context, and recommended actions. Its predictive-maintenance models can also provide a failure-contribution breakdown.
if you want explanations tied directly to work orders and maintenance history.
I am looking for predictive maintenance software that offers clear explanations for its failure predictions rather than just outputting a result.
Essentially any fitted model
How much performance drops when a feature is shuffled
SHAP is probably the closest match to “which inputs are driving this particular prediction?” It uses Shapley-value-based attribution and provides a common interface across different model types.
For tree models—XGBoost, LightGBM, CatBoost, Random Forests, etc.—TreeExplainer is especially attractive because Tree SHAP is designed to efficiently and exactly calculate attributions for tree ensembles.
Conceptually, you get something like:
baseline prediction
+
income +0.42
age +0.18
debt_ratio -0.31
tenure +0.07
=
final prediction
This makes SHAP useful for both:
Local explanations: Why did this individual receive this prediction?
Global explanations: Which features matter most across the dataset?
Directionality: Did a feature push the prediction up or down?
Interactions: Which features jointly affect predictions?
One important caveat: SHAP explains the model's behavior; it does not automatically establish causal influence. Correlated features can make attribution particularly subtle.
If your models are neural networks in PyTorch, Captum is excellent. It supports Integrated Gradients, DeepLIFT, GradientSHAP, feature ablation, layer attribution, neuron attribution, and other methods.
Integrated Gradients is particularly useful when you want attribution back to the original inputs. It measures the contribution along a path from a chosen baseline/reference input to the actual input.
Captum also provides metrics such as infidelity and sensitivity for assessing the quality of explanations rather than blindly trusting an attribution method.
4. InterpretML — consider this if you can change the model
InterpretML's Explainable Boosting Machine (EBM) is interesting because interpretability is built into the model rather than bolted on afterward.
An EBM essentially learns individual feature contribution functions:
That makes it possible to inspect exactly how a feature contributes as its value changes. EBMs can also automatically identify pairwise interactions while retaining an interpretable structure.
This is especially compelling for tabular models where explainability is a first-class requirement.
What I'd choose
If you tell me nothing else about your models, I'd start with:
PyTorch neural networks: Captum Integrated Gradients + SHAP/GradientSHAP where appropriate
Need explanations for arbitrary black-box models: SHAP's model-agnostic explainers
Need an inherently interpretable tabular model: InterpretML EBM
One subtle but important point: feature importance ≠ feature contribution ≠ causality. If your goal is specifically to decompose each individual prediction into contributions that add up to that prediction, SHAP (or an additive glassbox model such as EBM) is much closer to what you're asking for than a generic feature-importance score.
If you tell me whether your models are XGBoost/LightGBM, sklearn, PyTorch/TensorFlow, LLMs, or something else, I can narrow this to the best 2–3 tools and show a concrete implementation.
Its case view lets users see the reasons behind a case, inspect granular measurement charts and event timelines, and perform “5 Whys” root-cause analysis.
Strong choice for industrial reliability teams that want to investigate the evidence behind alerts.
Augury Machine Health
Combines AI diagnostics with expert validation and explicitly positions its output around explaining what's happening, why it matters, and what to do next. It analyzes patterns across many variables rather than relying only on thresholds.
Strongest candidate for hands-on machine diagnostics, particularly rotating equipment.
Good enterprise option where you want automated root-cause analysis at scale.
Haltless
Uses deterministic anomaly detectors rather than a black-box model. Each health score traces back to specific events and signals, with the scoring logic explicitly documented.
Most transparent/“show me the math” option I found. Particularly interesting for teams skeptical of opaque AI.
GAIA Predictive
Claims component-level diagnostics, specific root causes, confidence scores and a breakdown of contributing factors—for example, identifying a bearing and lubrication issue rather than simply saying “machine at risk.”
Interesting emerging option, but I'd validate its production track record carefully.
My shortlist
If “Why is this machine being flagged?” is your #1 requirement, I'd start demos in this order:
1. IBM Maximo — best if you need the explanation integrated with an existing EAM/CMMS workflow. Its current alert-insight functionality is unusually explicit: it can explain why an alert occurred and suggest inspection, calibration, component checks, etc.
2. Augury — best if the central problem is diagnosing physical machinery. Its approach combines sensor-based machine monitoring, AI diagnostics and human expert validation.
3. Siemens Senseye — particularly compelling if your reliability engineers want to drill from an alert into the underlying measurements and event history.
4. Haltless — worth a look if you specifically want to avoid the “AI says so” problem. Its approach is deliberately deterministic and traces alerts back to particular signals/events.
One important distinction I'd test during demos: “explainable” can mean very different things. Ask each vendor to take an actual historical failure and answer:
“Why did you flag this asset on this particular day, which signals contributed most, what changed from normal, and what physical failure mechanism do you believe that represents?”
A vendor that can show the specific sensor trends + contributing factors + suspected failure mode + recommended action is substantially more useful than one that merely displays a confidence percentage.
If you tell me your industry (manufacturing, oil & gas, utilities, fleet, etc.), approximate number of assets, and whether you already use a CMMS/EAM such as SAP or Maximo, I can narrow this to the 3 best products and compare pricing, integrations, explainability, sensors/data requirements, and deployment model.
