Smart Contracts Vs Legal Contracts: It's Complicated

While a future without lawyers is unlikely to materialize (much to the dismay of many), new technology in the form of smart contracts is altering the way legal documents are written. There has been an increased adoption for major industrial applications, smart contracts backed by distributed ledger technology.

Smart contracts provide unparalleled capability and automation of contract terms, whether for regulatory compliance, contractual enforceability, cross-border financial transactions, material provenance, document management, or other uses. Smart contracts and their code,
on the other hand, operate on an ‘if-then-else' logic that does not inherently
work in lockstep with the natural language of legal contracts. Due to their
distance from the business and operational processes, legal and contracting organizations may create contractual terms and conditions that execution teams are unable to follow or manage.

To verify, authenticate, collect, and enforce agreed-upon conditions between many parties, smart contracts interact with two additional technologies: the Industrial Internet of Things (IIoT) and Distributed Ledger Technology (DLT). A smart contract gathers IIoT data for performance measures, including data from sensors, meters, and other business
processes, from real-world, legally controlled occurrences. By publishing
findings and supporting evidence to the blocks, this data informs the automatic conditions of a contract.

A smart contract is a software application that automates contract terms execution. It only applies to the execution of a contract's executable provisions. Smart contracts do not replace natural language contracts; instead, they work as a program that links to one through an addendum that creates an unbreakable connection between the program and the
natural language contract.

The procedure seems to be excellent in principle. However, there are a few challenges to overcome in practice. Here are a few early takeaways from negotiating the still-emerging process of harmonizing legal language with the terms and data required for smart contract codeability.

Data that isn't exact doesn't compute.

Contracts are often left deliberately vague to allow for the interpretation. Contracts that are optimally ambiguous allow for debate and, as a result, allow contract parties to use their side of data gathering to establish a case and, ultimately, financial consequence. This may result in claims, disputes, increased legal costs, project, and operational delays, invoicing and payment delays, and a delayed contract closeout when work is finished, all of which have an effect on the larger supply chain and the delivery of the contract's value.

Field data collected via an IIoT platform from a variety of sources and uploaded to the blocks of a distributed ledger may make this long-standing practice and its associated legal dispute obsolete in the industrial sector. In order to automate contracts, a complete and clear understanding of the business and operational processes of all parties involved is required when establishing and agreeing on conditions.

To put it another way, contracts will no longer be able to accommodate unforeseen circumstances. Location and time, for example, must be specified. When one firm gathers data in local time at the end
of business operations, but this isn't stated to the counterparty, and the
counterparty measures data in Coordinated Universal Time (UTC) at the start of a business day, misunderstanding arises. Participants must agree on "particular data," which in this instance comprises the precise time
zone to be utilized, as well as the precise time and what it implies in terms
of contractual terms and fulfillment. Legal departments writing contracts must think about such issues ahead of time.

Creating Parameters for Logic

Real-time data recording capabilities are touted by blockchain-backed contracts, but it's more accurate to state near real-time. One trucking company's data stream, for example, clocks readings every 15 minutes for reporting, while another runs reports at the end of each day. For their contract, what data source would these businesses rely on? What
are the tolerances, exactly?

What is the match tolerance for the amount of water at a saltwater disposal site from an oil and gas production well, for example, when utilizing a reading of volume in the truck against the sensor output at the site? In addition, what kind of rounding will the smart contract handle? Rounding up, down, seldom, bankers' rounding? These are the kinds of
issues that need to be addressed and worked out before smart contract
codification can begin. In fact, smart contracts become data specifications.

Inconsistent readings cannot be automated, and specificities influence logic parameters surrounding data.

Two different data sources are unable to come to an agreement on a contractual condition that dictates payments. As previously stated, choices like location, time, and rounding have an effect on how contracts are translated into code. Parameters such as sources, tolerances,
frequency, and time periods of data collection techniques must all be included in legal contracts.

Language that is contradictory

Contracts, in reality, become evergreen documents that are updated, added, and cut/pasted over time as the initial contract is applied to transaction after transaction. Due to residual wording that continues over—typically by procurement departments—through the life of a
contract, the terms and conditions are always divergent or conflicting. It's
understandable that modifying an existing contract that's "near
enough" is simpler than creating a totally new one. However, issues occur
when an earlier contract utilized as a starting point has provisions that are
unnecessary or inapplicable and have been neglected to be deleted. A smart contract's code cannot be forced to execute conflicting clauses.

This is a frequent problem with billing language. If one company's contract requires payment by 12:00 UTC on the fifth of each month, while the counterparty invoices twice a month, on the first and 15th of each month, it's understandable that there would be misunderstanding (and dismay) as a result of this inconsistency. Smart contracts are, in a sense, "dumb." They do precisely what they're taught to do and are incapable of making decisions. Rules of engagement, especially those pertaining to fee calculations and billing procedures, must be able to be decoded from
unambiguous, non-conflicted contract provisions.

Predicting Data Errors and Gaps

IIoT data serves as a source of information for smart contract execution. Despite the fact that IIoT data has an order of magnitude more dependability and integrity than human-entered data, there will always be technological faults and malfunctions that result in data gaps or
mistakes.

The good news is that these occurrences can be fairly predicted, and protocol for them can be written in plain language or smart contracts. A smart contract may be designed to traverse data tolerances and triggers that automatically identify when a glitch or failure has happened with agreed-upon conditions for these occurrences. It may then carry out the
proper preset action that both parties agreed upon in advance, resulting in no delays or downtime in the relationship.

The Transition from Risk to Outcome-Based Thinking

Contracts in today's society are trapped in a paper-based framework. Despite the fact that many contacts between and inside businesses have been digitized, relationship management continues to be focused on risk. A “paper” contract—whether a physical copy or an electronic file—defines commitment; it is the vehicle that codifies an agreement, a mechanism designed to carry work ahead as a stopgap against which to assess and
execute services. However, depending on whose side of the contract you are on, it is intrinsically riddled with mistrust and prejudice. This style of thinking keeps a paper contract economy alive, where risk is negotiated and shared fairly between participants.

Smart contracts will impose a new approach, outcome-based thinking, in the future. It is feasible to design contracts that function optimally for autonomous systems by collecting digital data that captures performance metrics, removing paper processes, human emotions, and inherent biases from the equation. Risk management has moved to a new stage.Instead of compensating for human mistake, emphasis will be placed on developing digital rules that can be performed autonomously. Lawyers of the future will require a new set of abilities, one that focuses on how to build efficient contracts that take use of digital surroundings to make contract measurement and implementation easier.

Moving to an outcome-based strategy encourages logistics to improve their performance. The core of an agile legal strategy is identifying sources of unpredictability and mistake and then methodically removing them. This creates an input to result connection. Don't be shocked if law firms in the future hire data scientists to help them utilize blockchain technology and take advantage of the advantages of smart contracts for everyone.