Artificial Intelligence (AI) has experienced a massive increase in attention and interest in the last six years as a plethora of new commercial applications for it have appeared.
This explosion in interest is manifestly linked to the increase in, and accessibility of, vast computing power. Some of the world’s largest tech firms are not only building core areas of their business around artificial intelligence, they are also making the resources for it available to their customers.
This allows those customers in turn to build AI functionality for their customers and is reflected in the growing number of companies offering AI solutions to business problems.
Whether these solutions technically qualify as "AI" is sometimes up for debate and will depend on your definition.
Amazon describes AI as the “..field of computer science dedicated to solving cognitive problems commonly associated with human intelligence, such as learning, problem solving, and pattern recognition.”
Within the field of AI, you will also find terms such as “machine learning” and “deep learning”, which refer to specific types of AI functionality.
To help you understand where AI fits into contract management, first let’s pull out some of the relevant characteristics of contract management as a discipline and then see where AI and machine learning could usefully be applied.
Contract Management can involve:
- Large datasets. Some businesses will be managing contracts numbering in the millions at any one time. However, even smaller businesses can rapidly generate significant volumes of contracts, with each contract having multiple types of metadata.
- Repetition. In an ideal world contract management should involve the consistent application of agreed processes to all incoming and existing contracts.
- A combination of administrative and specialist work, often being carried out by the same under-resourced group of employees (eg in-house legal teams).
How can AI improve contract management?
1. Large Datasets
This area is really where AI can make the biggest impact on the discipline of contract management. Using AI and machine learning to make sense of big data is significantly more efficient than humans attempting the same tasks.
In this context, machine learning is adept at:
- Pattern recognition. Deploying AI in an “unsupervised learning” capacity can allow it to pull out previously unseen patterns and relationships between contract data.
- Anomaly identification. Related to the above, what data and relationships from your contract universe are anomalous?
- Optimisation. For example, what clauses within your business’s contracts are most commonly renegotiated or are common to your highest value contracts? How can these be changed to serve the intended purpose while cutting down on the time to signature?
Humans may have an instinctive feel for where patterns and correlations may be found but computers can interrogate datasets without preconceived ideas and pull out the objective relationships.
This can be highly useful for uncovering unexpected correlations and issues worth exploring.
In the case of contract management, this could manifest itself in finding duplicate spend, uncovering fraudulent activity, seeing which departments achieve closest to their contracts’ initial values or which specific staff take the longest to sign off contracts.
Once these patterns and data-points are established, they can be monitored over time and steps taken to address and improve them.
2. Repetitive tasks
Contract management is inherently a repetitive process, but doesn’t necessarily have to be a manual one.
Once you have an agreed contract management process in place, the use of rule-based automation can help to speed up that process and make sure it’s applied consistently.
While not strictly “AI” at this point, there is scope to enhance the automated processes with AI applications to provide such things as data enrichment.
Instead of having to populate counter-party details manually for example, simple high-level company information can be used to pull in richer data about that company from a range of online information sources.
What once would have required an individual to manually retrieve information can now be executed automatically in a matter of seconds.
Similarly, the need to extract contract information to create contract metadata can again be improved with the application of AI.
Gatekeeper's AI Extract in action
By applying AI data extraction tools to incoming contract documents, the process of creating full contract records can be automated and improved over time.
Often, these data extraction tools have the capacity to “learn” when they’re right and wrong by inviting human feedback. This then helps to optimise the process in the future and makes it much faster to extract data such as key dates, obligations and sign-off parties.
Once this is automated, the systems can be left to run 24/7, which is ideal for global businesses where contracts are being generated across different time-zones at all hours of the day and night.
3. Administrative Work & Specialist Resource
Contract management is a process that is often carried out almost entirely by specialist resource. For example, it’s often managed centrally, on behalf of a business, by the Legal Team.
The result of this can often be that highly trained legal professionals spend a disproportionate amount of their time conducting non-specialist work, like basic admin or data entry.
If a business’s contract management process is only rudimentary, or doesn’t have the right technology in place, then it can result in a large amount of manual work for internal teams.
Where automation and AI can make the difference is helping to better manage the administration side of the work, freeing up the specialists to spend more time applying their training and expertise.
In the case of in-house legal, this could involve:
- The automatic extraction of key contract information to create detailed contract records
- The highlighting of contract clauses which are either in breach of company policy, or which contain wording that’s consistently been red-lined in previous contract negotiations
- Gap Analysis to identify missing clauses that should be included
- Interpreting a variety of factors about the contents of a contract and the counter-party to automatically assess contract risk
- Generating high-volume, low value work such as NDAs
In each case, the information is then presented for expert assessment and the process becomes far more efficient, with the specialists focusing their effort on the parts that will drive the most benefit.
Many in-house legal teams are overburdened with administrative work and looking to automate as many tasks as they can, while remaining compliant. Touchless Contracts allows teams to generate compliant, legally-binding contracts from pre-approved contract boilerplates.
This drives efficiencies for the Legal team, streamlines their workload and still remain compliant.
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As the prevalence of AI has grown, so too has the level of scrutiny and media coverage it has received. In some cases this has resulted in some rather sensationalist claims and emotive responses.
The most common negative response is focused on AI replacing humans completely for certain types of work.
One of the first questions people ask themselves in relation to AI is “how will it practically affect my job?”.
As shown here, AI has many potential applications in relation to contract management and, realistically, a number of areas where it will be superior to a person attempting to carry out the same tasks.
But, as we’ve shown here it should enable those managing contracts to make better decisions, more quickly and help businesses generate more value from their contracts.