2025 IBM North America AI for Business Award
Salient understands your process first. We map it, analyze it, simulate it, and then select the right combination of IBM Cloud Pak for Business Automation and watsonx technologies to eliminate waste, reduce cycle time, cut error rates, and build a scalable automation program your operations team can run without us.
The Challenge
Point solutions and task automation produce incremental gains. The transformational savings come from eliminating manual handoffs, integrating disconnected systems, and embedding intelligence at every decision point in a complex workflow.
McKinsey, 2024
"We have automated individual tasks in five different tools. But work still falls through the gaps between systems, handoffs are still manual, and nobody has visibility into the end-to-end process. We are more automated and still slow."
IBM Cost of Data Breach Report, 2023
"Our error rates are too high, our cycle times are unpredictable, and we have no visibility into where work is or why it is stuck. We know the problem is the process. We do not know which technology to apply where."
Forrester, 2025
"We have proven value in a pilot. Now we need to scale it to a program, across multiple complex processes, with governance the board and risk committee will approve. Our current partner cannot take us there."
Why Salient Process
Most automation failures are technology selection errors. The right tool applied to the wrong step, or to a process that was never properly mapped, produces marginal gains at best and technical debt at worst. Salient's approach starts with understanding the actual execution before any architecture decision is made.
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 13 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
How We Work
A continuous improvement methodology that replaces subjective assessments with data-driven process truth.
Define the North Star, KPIs, and what “done” means for leadership.
Make work visible, identify bottlenecks, exceptions, and where value is trapped.
Decide where deterministic automation ends and where governed AI belongs.
Build and validate quickly with stakeholders, instrument outcomes from the start.
Report baseline vs realized results, then expand with a practical backlog and operating cadence.
Start your journey from process complexity to measurable AI and automation outcomes today.
AI Patterns by Industry
These are the highest-ROI AI patterns we deploy by industry, each tied to measurable outcomes your operations team and CFO both recognize.
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
Technology
No predetermined architecture. We select from the full IBM Cloud Pak and watsonx portfolio and layer Salient's own accelerators on top. Each component earns its place by solving a specific requirement in your actual process.
End-to-end workflow orchestration backbone. Manages approvals, assignments, escalations, integrations, and audit trail across every participant in a complex process.
Used where UI-based task automation or legacy system interaction is required. RPA supports targeted digital labor within the broader automation architecture, helping reduce repetitive manual work without becoming the lead solution.
Externalizes business rules and decision logic from hardcoded applications. Business users can update rules without IT involvement. Consistency, auditability, and speed in every regulated decision.
Enterprise content management integrated into process workflows. Documents, records, and content move with the process rather than sitting in disconnected repositories.
End-to-end workflow orchestration backbone. Manages approvals, assignments, escalations, integrations, and audit trail across every participant in a complex process.
Layered into the orchestration where UI-based integration or legacy system interaction is required. Not the lead solution — a targeted layer within the broader automation architecture.
Externalizes business rules and decision logic from hardcoded applications. Business users can update rules without IT involvement. Consistency, auditability, and speed in every regulated decision.
Enterprise content management integrated into process workflows. Documents, records, and content move with the process rather than sitting in disconnected repositories.
End-to-end workflow orchestration backbone. Manages approvals, assignments, escalations, integrations, and audit trail across every participant in a complex process.
Layered into the orchestration where UI-based integration or legacy system interaction is required. Not the lead solution — a targeted layer within the broader automation architecture.
Externalizes business rules and decision logic from hardcoded applications. Business users can update rules without IT involvement. Consistency, auditability, and speed in every regulated decision.
Enterprise content management integrated into process workflows. Documents, records, and content move with the process rather than sitting in disconnected repositories.
Salient Accelerators
Embeds governed AI agents directly into IBM BAW production workflows. Agents operate with native audit trail, approval routing, and escalation logic already in place. Compresses agentic AI integration from months to weeks.
Accelerates BAW application development. Reduces the time required to scaffold, build, and deploy standard process application patterns in IBM BAW. Developed from thousands of hours of BAW implementation experience.
Automated migration tooling for IBM BPM to BAW cloud transitions. Reduces migration time from months to days, eliminates manual rework, and handles complex multi-track solution trees. Tested across large multinational enterprise migrations.
Seamless bi-directional integration between IBM BAW and Salesforce CRM. Connects front-office sales and customer workflows to back-office process orchestration without custom API development for each field or object.
How to Engage
Process Intelligence is the right starting point. The Opportunity Lab produces the prioritized, ROI-ranked roadmap that feeds directly into the PoV Lab or QuickStart scope.
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.
Technology selection follows the process analysis, not a predetermined stack. BAW is almost always the orchestration backbone for complex, multi-step workflows. Every other component — ODM for decisions, Datacap for capture, watsonx.ai for document intelligence or AI agents, watsonx Orchestrate for digital labor, watsonx Assistant for self-service, RPA for legacy integration gaps — is added only when the process requirement specifically calls for it. We will explain every selection decision before the architecture is approved.
It means we do not start an architecture conversation before we understand how the process actually executes. If you have not previously mapped the process, we run a SPADE discovery session and Business Compass simulation before any technology design is proposed. If you come with existing process documentation, we validate it against how the work actually runs before accepting it as the basis for architecture decisions. The simulation tells us where automation produces the most leverage before we write the first line of configuration.
Every engagement is architected as the first module in a program, not as a standalone project. The governance model, operational runbooks, KPI instrumentation framework, and technology architecture are all designed to support the next automation without re-architecting. The QuickStart closes with a pre-scoped expansion backlog ranking the next candidates by readiness and ROI. Organizations that start with one QuickStart and follow the program model are running multi-process automation Centers of Excellence within 12 to 18 months.
RPA is in the stack but it is never the lead solution. We use IBM RPA specifically for legacy system interaction, UI-based integration where APIs do not exist, and targeted repetitive steps within a broader orchestrated workflow. An RPA bot doing its job in isolation is a fragile, point solution. An RPA bot embedded in a BAW workflow with ODM decision logic and watsonx intelligence on either side of it is a component in a production system. That is the difference between task automation and end-to-end hyperautomation.
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