A new chapter in AI adoption
It’s 8:00 AM. A sales manager opens their laptop to prepare for a negotiation. Instead of scrolling through spreadsheets and email, an AI negotiation assistant has already summarized competitor moves, highlighted risks, and drafted counter-arguments.
The change has already begun with agents stepping into real roles across organizations.
Most organizations don’t lack ideas but they struggle to convert pilots into results. At DAIN Studios, we call this the AI Value Gap – the disconnect between AI experiments and real business value. Agentic AI closes that gap by enabling systems that sense context, decide, and execute end-to-end workflows. Below are five practical use cases you can pilot in 90 days, plus what really has to be in place to make them work in practice.
1. Audit Automation
The problem
Drafting audit reports absorbs days of repetitive effort and introduces inconsistency.
The agent solution
An audit agent ingests structured audit data, drafts report sections, highlights anomalies, and routes for human sign-off. The agent uses retrieval-augmented generation (RAG) to surface relevant findings from prior audits, ensuring consistency across reports. It can also classify findings by risk level, suggest remediation actions based on historical precedent, and flag missing control evidence. In production, this requires a vectorised audit knowledge base, fine-tuned prompts for each report section, and a secure runtime environment that supports audit trail logging and version control.
Behind the scenes
For an audit agent to succeed, several foundations are needed:
- Secure access to structured audit data from ERP or accounting systems.
- Integration with reporting tools (e.g. SharePoint, Confluence) so auditors can edit and approve drafts without switching platforms.
- Governance mechanisms within a secure workspace (e.g. DAIN Brain), including role-based access controls and token-level usage monitoring.
These visible layers rest on many invisible ones—permissions, infrastructure, and versioning setups that determine reliability at scale.
DAIN in action
At Linde, AuditGPT reduced report drafting from 24 hours to 2 hours, saving thousands of hours annually and improving compliance confidence. Read the full case story here.
2. Sales Negotiation Simulator
The problem
Sales teams practice with static scripts that don’t reflect real-world dynamics.
The agent solution
A negotiation agent role-plays as the customer, adapts objections in real time, and provides post-session coaching. The agent simulates realistic objection handling using a multi-agent architecture: a customer persona agent generates context-specific objections, a scenario agent models financial impact, and a coaching agent provides feedback aligned to sales KPIs. These agents can be orchestrated to simulate negotiation rounds, adapt tone and strategy dynamically, and log performance metrics for training analytics.
Behind the scenes
What makes this possible goes beyond dialogue generation:
- Libraries of curated playbooks and objection patterns, tagged by industry, persona, and objection type.
- Embedding into CRM or other sales platforms, so managers can trigger sessions by pipeline stage or deal complexity.
- Feedback loops captured via post-session ratings and transcript analysis, which refine prompts and tune scenarios over time.
Behind the curtain, orchestration logic and environment integration keep simulations realistic, adaptive, and measurable.
DAIN in action
For a global manufacturing enterprise, our Sales Negotiation Assistant cut prep from days to minutes and enabled instant access to facts, scenarios, and counter-arguments. Read the full case here.
3. Market Intelligence Agent
The problem
Analysts drown in updates across news, filings, and competitor channels.
The agent solution
A market intelligence agent continuously monitors defined sources, filters noise, and pushes concise alerts with context. It uses a combination of keyword-based filters, semantic search, and entity recognition to extract relevant signals from structured and unstructured sources. Alerts are scored by relevance and novelty, and routed to teams based on topic taxonomy (e.g. regulatory, competitor, macroeconomic). Summarisation is handled by LLMs fine-tuned for business reporting, with source attribution and confidence scoring.
Behind the scenes
To make alerts trustworthy and timely, organizations need:
- Connectors to reliable sources (news APIs, RSS feeds, internal data lakes).
- Whitelisting and audit logs of content to ensure governance.
- Feedback channels like thumbs-up/down or inline comments, which feed retraining for better classifiers and summaries.
Beneath this are the scheduling jobs, indexing strategies, and data pipelines that keep intelligence fresh, relevant, and compliant.
Read more: DAIN Studios AI Agent Suite
4. Proposal & RFP Assistant
The problem
Responding to tenders is time-critical, repetitive, and error-prone.
The agent solution
An RFP agent parses requirements, assembles approved content, and produces a review-ready draft with compliance checks. It applies document classification and entity recognition to identify mandatory requirements, deadlines, and evaluation criteria. Drafts are assembled from a modular content library tagged by industry, solution type, and compliance status. A validation agent checks for outdated claims, missing certifications, and tone alignment before routing for human review.
Behind the scenes
Turning RFPs into reliable drafts requires:
- A content library with metadata for versioning, approval, and usage history (often stored in SharePoint or a CMS).
- Compliance filters ranging from regex checks to tone analysis and policy integration.
- Integrated review workflows in Teams or other Office software, allowing companies to approve or annotate drafts in familiar environments.
These backend layers make sure the agent isn’t just fast, but also accurate and compliant.
Read more: DAIN Studios AI Agent Suite
5. Knowledge Orchestrator
The problem
Knowledge is scattered across SharePoint, Teams, Slack, and CRMs. Employees lose time switching between systems.
The agent solution
A knowledge orchestrator connects these sources, reconciles conflicting information, and delivers clear answers in one place. It uses a query decomposition engine to break down requests into sub-tasks, each routed to specialised agents (e.g. document search, CRM query, transcript summarisation). Responses are reconciled via source prioritisation rules and presented with provenance links. Over time, the agent learns user preferences and tailors responses to role or department.
Behind the scenes
To make this seamless, several building blocks are required:
- Flexible integrations with secure authentication and configurable permissions.
- Hybrid search indexing (keyword + semantic) and caching for frequently accessed data.
- Data lineage tracking and fallback mechanisms, ensuring provenance and resilience when sources fail.
These layers transform fragmented data into a reliable, governed knowledge fabric.
Read more: DAIN Studios AI Agent Suite
Why pilots stall—and how to avoid it
Pilots fail when they remain demos. What separates “interesting” from “in production” is execution:
- Data access: Agents must plug into governed, live systems.
- Workflow integration: Delivered in the tools employees already use.
- Governance & trust: Logging, traceability, EU AI Act and GDPR alignment from day one. Agents must operate in governed runtimes that support auditability, model transparency, and fallback logic. This includes logging every prompt and response, tracking model versions, and ensuring outputs are traceable to sources.
- Adoption: Change management is as important as algorithms.
Read more: A.G.E.N.T. Framework: From Pilot to Enterprise Adoption
From pilot to scale: DAIN’s six-step path
A 90-day pilot is the start—not the finish. Scaling agentic AI requires a deliberate roadmap:
- Value Discovery & Alignment – Pick 2–3 high-value workflows; define success metrics.
- Core Team & Governance Foundation – Assign business, IT, and compliance ownership; set guardrails.
- Platform & Tools (MVP) – Deploy agents in a secure workspace (e.g. DAIN Brain) with support for multi-agent orchestration, prompt versioning, and enterprise identity integration. MVPs should include telemetry dashboards for usage, accuracy, and feedback.
- Pilot Execution & Learning – Test with real users, measure, and iterate (A.G.E.N.T. framework).
- Long-Term Roadmap – Translate lessons into operating models and skill-building.
- Scaling & Productization – Define agent roles, interfaces, and SLAs. Catalogue agents with metadata, expose them via APIs or chat interfaces, and extend governance as new workflows are added. Scaling requires onboarding playbooks, training modules, and governance to sustain enterprise adoption.
Read more: Agentic AI: From Pilots to Scalable Impact
What you’ll gain
Agentic AI doesn’t replace people; it removes friction from work. These five pilots are pragmatic, governed, and achievable in 90 days—if you build with real data, real workflows, and the right foundations behind the scenes.
Ready to explore your first pilot?
Start with a focused A.G.E.N.T. cycle and a modular agent from the DAIN Studios AI Agent Suite – and build toward a scalable, governed portfolio to gain real business value from agentic AI.