Top 5 CLM Trends for 2022


An AI-enabled contract lifecycle management (CLM) system standardizes data, automates compliance, and provides visibility into the entire contract management process. Leveraging contract management technology allows businesses to overcome challenges and become more resilient. It is imperative for businesses to have effective and efficient contract management to mitigate risk in today’s business dynamic.

Below are 5 of the top CLM trends for 2022 that are enabling a faster and more successful contracting system.

1. The role of AI in contract analytics

Businesses must expect to use AI capabilities to their advantage in 2022 and beyond. AI and machine automation are efficient and quickly taking over contract lifecycle management.

Managing contractual obligations efficiently is one of the most important yet challenging aspects for businesses. AI contract analytics allows businesses to streamline their compliance functions and reduce costs associated with these obligations.

As contract negotiations accelerate, the use of AI contract analytics helps mitigate risk. The machine-automated system speeds up the process and quickly identifies opportunities and obligations connected with business relationships.

The use of contract AI can auto-assign a “risk score” in comparison to the standard language. AI helps uncover “descriptive,” “predictive,” and “prescriptive” fields and metrics, as noted in the image below for Business Intelligence Analytics dashboards to publish in an intuitive manner.

With the use of AI contract analytics, contracts are managed to the highest level while simultaneously decreasing time, cost, and effort.

2. Accelerated disaster preparedness

Businesses rarely ever conduct IT security risk assessments due to cost. Most wait until an incident occurs to then sue over a breach. This decision proves much more costly.

Experience shows that current master agreements, such as MSAs, do not cover this risk where contracts are stored, where environments are set up, and in case of double redundancy of data.

Data privacy agreements (DPAs) include some minor language to cover this risk, as in this case: “Vendors shall have in place and regularly test and update routines and processes for data recovery and disaster recovery to ensure that recovery times for the services and access to company data is minimized.” However, no further details stipulate what mechanisms need to be in place.

To avoid risk, it’s important to add more robust legal language to current master agreement templates to protect clients. Following this, it is recommended to use a centralized repository with OCR capabilities to identify, monitor and audit clients for IT security risk assessments. This allows clients to have the ability to audit any vendor’s procedures proactively to mitigate the risk of a disaster and/or data breach.

3. Accelerated digital transformation

Digital transformation has never been more necessary. The rise of AI and consequent digital solutions are exponentially growing. To maintain a competitive edge, digital business transformations must be accelerated.

It is recommended to use Agile-based deployments that focus on faster releases of capabilities (e.g., new functionality every 3 weeks). It is pertinent to move away from elongated “big bang” deployments and release plans that exceed 12 weeks in duration.

Leveraging AI to review legacy contracts is a smarter, faster way to flush out “process insights” for identifying real requirements and functional improvements/areas that need to be addressed first.

Questions to be considered are: which contract types should be focused on? Which clauses require approvals/notifications for redlines? To mitigate risk, it is important to reduce “bad” friction but keep “good” friction to help speed up the process while ensuring adequate controls.

4. The urgency for data privacy

Data privacy is a top priority in the current marketplace. Similar to accelerated disaster preparedness, a growing amount of new accounts require review and the execution of a DPA.

It is recommended to leverage AI for DPAs mainly to see if there are consistent areas of privacy that are being covered. Each DPA should stipulate the type of data that needs protection. In short, it should stipulate who has access and measures to secure the data whether in the US or the UK.

Mainspring has identified KPIs and developed a risk score, as noted in the first section of this article, to measure these attributes so clients can assess several agreements that protect personal information, HIPPA, and responsibilities of data importer versus data exporter.

5. End-to-end automation

End-to-end automation is driven by an accelerated digital transformation and the goal for business resiliency.

This type of automation allows for end-to-end process flow tests to ensure the system works as intended. It is tested from the user’s perspective, thus fulfilling core business objectives. This automated process confirms the health of the entire application, guarantees that functionality is optimized for customer experiences, and secures the business process flow.

Several business areas such as management of contract obligations and auto-escalation of non-compliance, are pertinent to a healthy operation. Combining AI capabilities with robotic process automation (RPA) makes for a faster, smarter, easier approach for processing standard contracts and/or redlines with minimal intervention from the legal department. This opens up their attention to non-standard transactions and redlines.

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