Contracts form the foundation of business partnerships and revenue for companies. But managing contract workflows – from creation, approving contracts, tracking updates, maintaining compliance as obligations change – can be complex manual processes. This leads to inefficiencies which can have legal and financial implications.
This technology guide provides an extensive analysis across –
- Emergence of AI in Contract Lifecycle Management
- Capabilities, solutions and key considerations
- Integrating CLM with other Enterprise Systems
- Analytics innovations
- Deployment models
Let‘s get started!
Rise of Contract Intelligence Capabilities
Legacy contract lifecycle management (CLM) solutions provided capabilities like centralized document storage, workflow configuration, reporting.
But contracts encode complex legal, financial and technical relationships across parties, products, timelines and obligations. Manning these workflows still needed extensive human review.
This is now changing with AI. Contract AI leverages:
- Computer Vision – Classify images, diagrams, signatures in contracts using image recognition
- Natural language processing – Understand sentences, clauses extracted using OCR to auto-tag concepts
- Predictive analytics – Surface patterns, risks and renewals forecasts using metadata analytics
Let‘s see how AI is impacting major CLM capabilities:
Intelligent Metadata Extraction
Manual metadata tagging depends on rigid templates and is error-prone needing extensive reviews.
AI CLM solutions use NLP + Rules engines to auto-classify clauses and sentences in contracts. This allows auto-harvesting structured attributes like:
- Party names, addresses
- Contract dates, terms, limits
- Products, services contracted
- Legal provisions – governing laws, IP ownership, termination clauses
This level of details powers rich analytics downstream.
For example, Evisort leverages ML models trained on 100M+ contracts documents to achieve 95%+ accuracy in metadata tagging.
Actionable Insights for Obligations Management
While CLM solutions track milestones, renewals and amendments, getting holistic views needed manual setup.
Now AI CLM platforms generate proactive insights like:
- Search contracts easily in natural language
- Upcoming deliverables and expiration alerts
- Renewals probability scores and volume forecasts
- List of non-standard contracts increasing business risks
This prevents revenue leakage and keeps continued compliance in check.
For instance, SirionLabs compares captured contract metadata against 500+ industry benchmarks to provide out-of-the-box analytics on contract health, risks and performance.
Smart Contract Recommendations
Even with readymade templates, drafting contracts needing extensive reviews.
AI CLM tools now provide intelligent recommendations with:
- Relevant clause suggestions based on contract context
- Synonyms for stronger negotiation positioning
- Personalization guidance based on customer history or cohorts
This boosts productivity while reducing review cycles for legal, sales teams when creating contracts.
These innovations powered by AI and automation are transforming legacy CLM systems into smarter Contract Intelligence platforms.
Now let‘s cover key CLM capabilities, software comparison and considerations when evaluating solutions.
Contract Lifecycle Management Software Capabilities
While AI is revolutionizing areas within CLM, core platform capabilities still center around:
Contract Templates + Authoring
To speed up document creation using pre-approved structures and editing tools.
Collaboration + Negotiations
To align internal teams and discuss changes easily with external parties.
Obligation Tracking
Monitoring upcoming deliverables, milestones initiation and managing changes.
eSignatures + Approvals
Electronically sign final contracts as per pre-defined sequence, rules.
Analytics Reporting
Insights into process efficiency, cycle times for operational and strategic decisions.
Leading end-to-end CLM platforms like:
- Agreement Express
- Conga Contracts
- Concord
- Icertis Contract Management
- Ironclad
Provide 80% out-of-the-box capabilities on these areas complemented by AI innovations covered before.
Let‘s analyse them on key considerations.
CLM Software Comparison Considerations
As contract workflows span across teams, different platforms cater to specific needs. So start by identifying current limitations or bottlenecks to address.
Then across shortlisted options, compare on:
Scope + Use Cases
What contract types are supported – sales, procurement, employment etc. As needs evolve, analysts prefer platforms which offer versatility vs niche solutions.
Template + Doc Customization
Ease to tailor out-of-box templates, editing tools leveraging configuration vs dependencies on the software vendor.
Obligation Management
How robust is milestone tracking, notifications, amendments support and analytics on contracted commitments.
Collaboration Infrastructure
Annotation abilities for discussion threads, document version compares and radiuses for external parties.
Integration + Security
Available APIs, integrations with other enterprise systems. Protocols for encryption, access controls.
Reporting + Analytics
Beyond process tracking, analytical views for forecasting, risk management and optimizations.
Alongside end-to-end suites, there are also speciality CLM solutions like:
Aurigo for Public Sector Contracts with pre-configured templates for government agencies.
Fuse for Legal Services optimizing the quote-to-cash process between law firms and clients.
Make choices based on current limitations vs long term roadmap.
Now let‘s cover recommendations on integrating CLM with other enterprise systems.
Integrating Contract Management Systems
While standalone tools have benefits, analytics and collaboration advantages grow exponentially when contract data integrates across systems like:
- CRM, ERP holding customer, products data
- Communications systems like email, chat platforms
- Document management providing storage foundations
- eSignature platforms standardizing executions
Here are some best practices on enabling integrations:
Leverage APIs + Webhooks
Platforms like Conga Contracts, Paperflip provide REST APIs and webhook triggers allowing modern integrations. These call contractor data or notify events to other apps.
Build Lightweight Glue Code
Small middleware scripts executing formatting, aggregations before feeding data across systems prevents vendor dependencies.
Evaluate Message Queues
Asynchronous queues like Kafka decouple connect systems, smooth workflows for high volume data sharing like contract drafts, approvals.
Audit Trails + Versioning
Integrations should preserve activity records and data changes across systems for compliance needs.
Getting integrations right is crucial to balance productivity and security when connecting contract data across tools.
Now let‘s explore the emerging "headless" architecture trending in enterprise software.
Headless Contract Lifecycle Management
Historically CLM suites have bundled:
- Contract database + ingestion workflows
- Obligation engines + analytics
- Fixed user interfaces + portals
But managing customizations, upgrades gets complex given interdependencies.
Headless architecture decouples the:
- Backend CLM engine
- Frontend portals + experiences
Teams can tap integration layers like APIs to connect new experiences, insights modules from other vendors or build their own.
Benefits of headless CLM include:
- Tap best-of-breed experiences like chatbots, document summarize tools
- Own UX roadmap without vendor dependency
- Mix open source and proprietary capabilities
As cloud solutions mature, headless models balancing flexibility and integration will drive the next wave of contract lifecycle management capabilities.
Now let‘s wrap up with deployment considerations.
Cloud vs On-premise CLM Platform Considerations
With capabilities and integrations covered, an equally crucial decision is deployment model – cloud or on-premise:
Beyond business preferences, consider long term implications on:
Customization Flexibility: Cloud limits changes to available configuration levers vs open continuum on-premise.
Data Control + Security: Cloud allows little user data governance vs on-premise hosting in private infrastructure.
But cloud environments provide higher uptime guarantees and avail latest features faster.
Growth + Scalability: Cloud elasticity allows effortless scaling vs hardware planning, upgrades needed on-premise.
So balance productivity gains against compliance and change control priorities.
Many choose hybrid models placing non-sensitive data in secure cloud nodes while keeping proprietary data and apps on-premise.
With the accelerated pace of technological disruptions, partner with vendors providing appropriate deployment patterns aligned to internal digitization roadmaps.
Final Thoughts
This extensive guide has spanned AI advancements, solution capabilities, technical integrations, architecture patterns and deployment models accelerating contract lifecycle management transformations.
Centralize on addressing current limitations, bottlenecks and target incremental enhancements leveraging analytics. Balance business priorities, level of customizations needed and partner with responsive vendors.
Contracts being vital assets encoding complex relationships, lifecycle automation is foundational yet complex necessitating step-wise implementations.
Hopefully these strategies and considerations provide a 3600 view helping shape effective contract intelligence foundations powering the needs of modern digitally connected enterprises.
