Adoption of AI technologies such as machine learning have gathered pace in business, helping enterprises identify patterns that can significantly assist with decision making in an ever-changing world.
Those patterns themselves, however, are subject to rapid change due to the constant shifts in the larger business landscape. Case in point: the eruption of COVID-19 this spring and the upending of normal business activity that ensued.
Change doesn’t have to be something as dramatic as a worldwide pandemic to affect patterns of activity. On a more routine basis, laws and regulations are constantly evolving and can lead to change in business operation. Consequently, static business models that were trained on “the old ways” become inadequate and unrepresentative of current scenarios very quickly.
For this precise reason, there is business value in adopting AI technologies such as ontologies and graphs.
Dynamic Data Connections
Ontologies and graph technology enable a dynamic connection between situations, data points, and entities. The graph will naturally evolve based on changing data: some data connections may go down, and some may go up in response to changing circumstances.
This might sound slightly abstract, so a concrete example is helpful here to illustrate what we’re talking about.
In the “normal” pre-pandemic times, when the economy was on steady ground, certain types of clauses – perhaps around force majeure, or around the ability to break a lease – might not have appeared much in contracts or be leveraged. However, as the pandemic changed the landscape and the economy tilted towards recession rather than expansion, these clauses might start appearing with much greater frequency. Graphs will grow with this change and reflect it in what they show to applications.
In this way, graphs deliver a powerful advantage over organisations that need to have humans step in to retrain the models every time situations change or evolve. That type of manual effort is a far less sustainable road for an organisation to tread.
A Window Into “What’s Going On”
There are other conspicuous advantages that ontologies and graphs provide that bolster its business case. For starters, because organisations can see changes – areas where the graph is increasing or decreasing – happening almost in real-time, they can more easily identify trends.
This allows organisations to take an investigative approach to surfacing and proactively resolving business issues before they become problems. This stands in contrast to the historical approach, which typically used data to delve into pre-defined/identified problem areas.
With ontologies and graphs, people within the organisation don’t need to have a theory about where they think there might be problems in the organisation and then seek out data to prove or disprove that theory. Instead, they can look at the data from different angles – and “they” includes everyone from the lawyers and business analysts, to the strategy team and the sales leadership – and hone in on anything that strikes them as worthy of investigation.
Overall, this makes it easier to “discover” what’s going on in an organisation. It’s not hard to see this being put to use, for example, by the lawyers within an organisations to proactively stay on top of regulatory compliance, or other risk factors that could potentially end up in litigation, through a better understanding of what’s happening in the organisation. Who’s being compliant and who isn’t? Where is data being stored or handled in a problematic way in our systems?
In an increasingly regulated world – and one where the regulations are constantly evolving and shifting – graphs provide an efficient way for organisations to look at systems data and wrap their arms around whether their people are following compliance policies or legal advice, helping to minimise overall risk to the organisation.
A Technology Whose Time Has Come
For all the excitement about ontologies and graphs, it’s important to remember that this isn’t new or unproven technology. Tech giants like Amazon, Facebook, and Netflix have deployed it at scale and used it to power their recommendation systems for over a decade.
Organisations of all stripes could take a page out of the books of these tech companies by moving away from keyword-led “search and browse” efforts to find and surface information, towards recommended results that draw upon ontologies and graphs to provide valuable, contextual information.
The ability to automatically surface relevant knowledge is especially important as workforces become more dispersed – not just because more people are working from home or working remotely in response to COVID-19, but because business is a more international and distributed endeavor today than it ever has been.
Across offices, across countries, and across jurisdictions, these organisations need the ability to uncover connections around their people and their data. Especially in the legal and professional services, this is vital to the work that they do – and in tackling this challenge, ontologies and graphs make a strong business case that they are a technology whose time has come.
About the author:
Alex Smith, Global Product Management Lead for iManage RAVN, has over 20 years of experience in product management and service design, including new and emerging technologies such as artificial intelligence, semantic search and linked data, as well as content management. Prior to iManage RAVN, Alex has held positions at Reed Smith LLP and LexisNexis UK.
