2025 IBM North America AI for Business Award
Salient's CLARITY methodology covers the full process lifecycle: mapping, analysis, business architecture, governance, simulation, training, prioritization, and the business case that gets automation and AI investment approved. Every engagement ends with a clear recommendation for the right next build.
The Challenge
Failed automation programs, underperforming AI deployments, and stalled operational improvement initiatives share three failure modes. All three are process problems, not technology problems.
Where's the Value in AI? BCG, 2024
Companies invest in AI and automation but can't prove tangible business value. Projects never scale past proof-of-concept.
Deloitte, 2023
of work happens off-system, invisible to standard reporting tools and undocumented by any team.
AA, 2025
of the workday lost to manual tasks that could be automated or improved. Without prioritization, the wrong ones get funded.
The CLARITY Methodology
Why Salient Process
Most consulting firms deploy AI on top of broken processes and call it done. We start differently: baseline the work, measure what it actually costs, design the end-to-end fix, then embed governed AI where it belongs inside your real workflows. Every engagement closes with an executive-validated outcome your leadership can fund again.
13+ Years
IBM Gold Partner
600+
Successful Projects
$200M+
Clients Results
We don't just ship code; we move KPIs and secure executive validation for every AI milestone
Design AI safely into real workflows with built-in controls, auditability, and human oversight
Leverage 15 years of Gold Partner experience to maximize your investment in watsonx and CP4BA
We ensure AI automation doesn't create downstream bottlenecks by optimizing your entire end-to-end process
What Gets Mapped by Industry
The hidden waste lives in different places across industries. These are the workflows where SPADE and process mining consistently surface the highest-value automation, AI, and redesign opportunities.
AI reconciles purchase order, goods receipt, and vendor invoice. Clean matches close without a human touch. Exceptions route with context. AP cycle time drops from days to hours.
Typical outcome: 80% straight-through processing
AI extracts, validates, and routes data from unstructured loan documents. Manual keying eliminated. Origination capacity scales without headcount. Every decision governed and auditable.
Example: 55% faster, 60% lower cost per loan
Automated watchlist checks, risk scoring, and case creation on customer onboarding. Full audit trail for regulatory defensibility. Compliance teams work exceptions only.
Typical outcome: 70% reduction in manual screening time
Data aggregation, transformation, and report generation automated across systems. Reports timestamped, versioned, and submission-ready. Manual assembly risk eliminated.
Typical outcome: 90% reduction in manual report assembly
AI reads First Notice of Loss submissions from any channel, extracts incident type, severity indicators, fraud signals, and coverage match, then routes the claim to the right adjuster tier or automated track before any human opens the file.
15–30% cycle-time reduction
Structured and unstructured submissions are read by AI that extracts risk-relevant data points, flags missing information, and cross-references external data sources. Underwriters receive a structured risk summary instead of raw documents.
Outcome: Submission handling time reduced by 60%, 40–60% faster submission intake.
Technologies: watsonx.ai
Models score every policy at renewal time for flight risk based on payment history, engagement signals, competitive pricing exposure, and life event indicators. High-risk policies route to proactive retention campaigns.
Outcome: 20-30% improvement in retention for at-risk policyholders when proactively engaged
Technologies: watsonx.ai
AI processes satellite imagery, inspection photos, weather history, and building permit records to generate property risk profiles for underwriting and claims validation. Human review focuses on edge cases.
Outcome: Underwriting accuracy improves. Inspection costs reduced by 30% through AI pre-screening
Technologies: watsonx.ai, watsonx.governance
AI reads clinical notes and payer coverage guidelines simultaneously, extracting clinical evidence and mapping it against payer-specific medical necessity criteria. Submissions arrive complete the first time rather than being returned for missing documentation.
Outcome: 60% reduction in auth cycle time
Technologies: watsonx.ai, watsonx Orchestrate
AI classifies denied claims by root cause: coding error, missing auth, eligibility mismatch, timely filing. For each denial category, correction logic is applied and supporting documentation is generated automatically. Resubmission rates improve because the fix targets the actual cause.
Outcome: 30% improvement in first-pass resolution on resubmission. Revenue recovery accelerates.
Technologies: watsonx.ai, watsonx Orchestrate
AI matches referred patients to in-network specialists using availability, distance, clinical specialty match, and insurance eligibility simultaneously. The model recommends the optimal match in seconds rather than requiring coordinators to work through provider directories manually.
Outcome: 50% reduction in referral cycle time
Technologies: watsonx.ai, watsonx Orchestrate
Natural language processing reads completed clinical notes after each encounter, extracts diagnosis and procedure information, maps to the correct coding and billing fields, and routes documentation to the right downstream system. Providers document in their natural workflow; AI handles the classification.
Outcome: 40% reduction in documentation time per provider
Technologies: watsonx.ai, watsonx Orchestrate
Sensors on production assets feed a predictive model that detects degradation patterns before failure. When a threshold is crossed, AI automatically creates a prioritized work order in the CMMS, pulls the relevant maintenance procedure, checks parts availability, and notifies the technician.
Outcome: Unplanned downtime reduced by up to 20%
Technologies: watsonx Orchestrate, watsonx.ai
Computer vision on the production line flags non-conforming parts. AI classifies the defect type, traces the batch to its origin, generates a non-conformance report, and initiates a supplier corrective action request with evidence attached. No manual inspection routing.
Outcome: Defect escape rate drops
Technologies: watsonx Orchestrate, watsonx.ai
AI monitors purchase orders, shipment ETAs, and inventory levels in real time. When a disruption is detected (late delivery, stock-out risk, supplier issue), an orchestrated workflow identifies alternative sourcing options, calculates impact to production, and routes a decision package to the supply chain manager.
Outcome: Response time to supply exceptions drops from days to hours
Technologies: watsonx Orchestrate, watsonx.ai
Engineering submits design specs. AI extracts key parameters, generates draft SOPs, work instructions, quality control plans, and regulatory documentation aligned to the product category. Technical writers review and finalize instead of authoring from scratch.
Outcome: NPI documentation cycle compressed by 50%. Consistency improves across product lines
Technologies: watsonx Orchestrate, watsonx.ai
AI models combine POS data, weather, local events, promotional calendars, and supplier lead times to generate store-level and SKU-level demand forecasts daily. Replenishment orders are generated from the forecast rather than from reorder points set months ago.
Outcome: 30% fewer stockouts. 15% reduction in inventory carrying costs
Technologies: watsonx.ai, watsonx Orchestrate
AI extracts structured data from orders arriving in any format (EDI, email, portal, fax), validates pricing against active contracts and promotional agreements, flags discrepancies before fulfillment, and scores each order for credit risk. On the back end, cash application models match incoming payments to open invoices automatically, even when remittance information is incomplete or inconsistent. Deductions and short payments are classified by reason code and routed with context.
Outcome: 80% of orders and payments processed without manual intervention
Technologies: watsonx.ai, watsonx Orchestrate
AI verifies purchase eligibility, classifies the return reason, and determines the most economical resolution path: full refund, exchange, store credit, or no-return refund where shipping costs exceed item value. Models trained on return history learn which resolution paths drive repurchase and loyalty versus which create friction. Fraud scoring runs in parallel to flag serial returners or policy abuse before the resolution is issued.
Outcome: Return resolution time drops from days to minutes
Technologies: watsonx.ai, Watson Assistant, watsonx Orchestrate
AI forecasts customer traffic by location and time of day, translates it into staffing requirements by role, and generates compliant schedules from employee availability data. Managers receive a schedule recommendation rather than building one from scratch against a spreadsheet.
Outcome: 60% reduction in scheduling time
Technologies: watsonx.ai, watsonx Orchestrate
AI indexes the organization's internal knowledge base: policies, SOPs, product documentation, contracts, past case notes. Employees ask questions in natural language and receive answers with cited sources, replacing manual document searches.
Outcome: Employee time searching for information reduced by 50%
Technologies: watsonx.ai, Watson Assistant
AI reads incoming documents from any channel, classifies type, extracts key fields, validates completeness, and routes to the appropriate system or workflow. Replaces manual data entry across AP, claims, HR, compliance, and operations.
Outcome: Document processing cost reduced 60-80%
Technologies: watsonx.ai, watsonx Orchestrate
AI reads lengthy case histories, email chains, meeting transcripts, and reports and generates structured summaries in seconds.
Outcome: Knowledge worker productivity increases 30-40%
Technologies: watsonx.ai
An orchestrator AI receives an intent from an employee or customer, decomposes it into tasks, selects the right downstream agents and tools, and coordinates execution to completion. Multi-system tasks that previously required multiple teams complete in a single conversational interaction.
Outcome: Task completion time drops 50-70%
Technologies: watsonx Orchestrate, watsonx.ai
Transition from subjective guessing to data-driven growth.
Tools and Platform
BM watsonx is built for enterprise AI deployment in regulated environments. It is not just an AI platform. It is an AI governance platform.
Process mapping repository, ROI simulation, and scenario modeling platform. As-is and to-be simulations with conservative, base, and upside projections. Business case generation with documented assumptions traceable to process data.
Salient's proprietary process discovery AI solution. Captures actual execution across systems, email, spreadsheets, and manual steps. Produces a complete as-is map including all off-system work that standard tooling cannot replicate or document on its own.
Cloud-based process mapping repository. BPMN-native, collaborative, accessible from any location. 200+ process templates. Direct integration with Blueworks Insights for analytics and reporting.
Event log analysis that reconstructs how processes actually executed from ERP, CRM, and other system data. Surfaces deviation patterns, throughput bottlenecks, and compliance deviations invisible to workshop-based discovery.
How to Engage
75% Faster claims processing 25x Increase in revenue targets Claims Processing Automation Salient Process implemented IBM BAW and watsonx to replace CCG's legacy CRM, automating claims routing and real-time policyholder tracking across all stakeholders. Claims cycle dropped from 120 to 30 days. Daily estimates per user jumped from 15 to 288. National expansion achieved in 1 year vs. a 5-year plan.
21 Week Deployment$100K+ Projected annual savings Licensing Compliance with watsonx Assistant + ODM BBB agents were spending hours manually searching state licensing requirements to process business accreditations. Salient Process deployed IBM watsonx Assistant and ODM so agents could ask licensing questions in plain language and get instant answers. Hundreds of hours saved daily across the organization.
80% Improvement in cycle times 1,000+ Authorizations processed per week Payment Authorization Automation with BAW + ODM The client's manual payment update process took weeks or months per request, with no tracking and high error risk. Salient Process and IBM automated intake, validation, and routing via BAW and ODM, giving agents a single portal instead of multiple disconnected systems. Processing time cut by 5 weeks on average.
The PM&A Sprint is about building process intelligence capability: mapping key processes, establishing methodology standards, training your team, and setting up the Blueworks Live environment. Outputs include automation-ready process maps, a trained team, and an organized repository. The Opportunity Lab is about investment prioritization: producing a scored, ROI-quantified ranking of automation and AI candidates with a business case formatted for CFO and board approval. Many teams run the Sprint first and the Opportunity Lab second.
Documentation is a starting point, not a substitute for discovery. We captures the gap between the documented process and actual execution: the workarounds, exception paths, shadow processes, and off-system manual steps that are rarely recorded anywhere. In most engagements, the most valuable findings come from the undocumented work, not the documented process. We will build on what you have and fill in what is missing.
No. Process intelligence surfaces four types of improvement opportunities: workflow automation with BAW, AI applications with watsonx, RPA for repetitive manual tasks, and process redesign where the right answer is not technology but a better operating model. We recommend the right type of improvement for each opportunity, not the one that produces the largest build engagement for Salient.
Typically 4 to 6 hours per process for key stakeholders across workshops and review sessions. We structure sessions to be efficient: a focused kickoff, targeted working sessions with process owners, and a final readout that doubles as your funding presentation or training capstone. The engagement is designed to minimize disruption while capturing the depth needed for a credible ROI model.
Most clients move directly into either a Proof-of-Value Lab for AI opportunities or a QuickStart production build for workflow automation or RPA. The Opportunity Lab deliverables pre-scope the next engagement directly, so there is no handoff gap or re-discovery phase. The executive funding pack is also the scoping document for the first build, which is why CFO approval after an Opportunity Lab is typically faster than after a standalone business case exercise.
No more pilot stall, embed adaptive agents in your IBM BAW processes today.
