AI News, Artificial Intelligence Brings Static Contracts to Life


Nearly every enterprise is experimenting with artificial intelligence and deep learning.

It seems like every week there’s a new survey out detailing the ever-increasing amount of focus that IT shops of all sizes put on the technology.

If it’s true that data is the new currency, then it’s artificial intelligence that mines that data for value.

It’s not just the usual suspects of defense and intelligence who demand these capabilities—AI/ML is fast becoming a fact-of-life across the spectrum of government agencies.

I advise my clients on how storage systems and data architecture must evolve to meet the needs of emerging and disruptive technologies.

Depending on the problem you’re trying to solve, that may be one GPU in a data scientist's workstation, or it may be a cluster of hundreds of GPUs.

That could mean deploying storage systems that have scalable multi-dimensional performance that can keep the GPUs fed, or simply ensuring that your data lakes are designed to reduce redundancies and serve the needs of all that data’s consumers.

The single best way to prepare for the impacts of AI in the data center is to become educated on what it is and how it’s used.

Gone are the sessions and talks about gaming, with the focus instead on the business of deploying artificial intelligence with a purpose.

According to NVIDIA, he’ll talk extensively about how organizations of all types can best utilize the power of artificial intelligence to boost their competitiveness.

Strategy, like all research and analyst firms, provides or has provided research, analysis, advising, and/or consulting to many high-tech companies in the industry NVIDIA, which may be cited in this article.

Microsoft pips Amazon for $10bn AI 'Jedi' contract

The Pentagon has awarded a $10bn (£8bn) cloud-computing contract to Microsoft, following a heavily scrutinised bidding process in which Amazon had been seen as the favourite.

Microsoft executive Toni Townes-Whitley said the company was 'proud' to have had its cloud technologies picked by the Department of Defense to 'satisfy the urgent and critical needs of today's warfighters'.

The move was likely to boost Microsoft's share price and bring 'significant positive financial implications' for the company in the coming years, he said.

But after President Trump's comments about 'receiving tremendous complaints' about Amazon's frontrunner status in July, and then the delay to announcing the award the following month, it had always looked liked Microsoft could pull off an upset.

The timing is curious, however, coming just days after Defence Secretary Mark Esper unexpectedly removed himself from the review process after months of involvement, on the grounds that one of his sons worked for IBM - one of the other original applicants.

This has run into delays of its own, but is still set to result in the DoD using cloud-based Office 365 for its email, calendar, video-calling and other productivity software needs.

Some of its own workers have already objected to it developing a version of its Hololens augmented reality headset for the US military, and the idea of it now providing machine learning tools and other systems to help 'enhance force lethality' could prove to be a PR nightmare.

Why per-seat pricing needs to die in the age of AI

In the past, founders could get away with giving pricing short shrift under the mantra, “the best product will ultimately win.”

pricing fundamentally impacts usage, which directly informs product quality.  Therefore, pricing models that limit usage, like the predominant per-seat per month structure, limit quality.

And since AI is currently the best-funded technology in the software industry — by far — pricing could soon be changing at a number of vendors.

Features are there whether users take advantage of them or not — your CRM doesn’t sprout new bells and whistles when more employees log in;

Veeva, which provides a CRM optimized for the life sciences vertical, commands a subscription price many multiples higher, in large part because it has been tailored to the pharma user’s end needs.

Relying on per-seat pricing gives buyers an easy point of comparison ($/seat is universalizable) and immediately puts the AI vendor on the defensive.

Moving away from per-seat pricing allows the AI vendor to avoid apples-to-apples comparisons and sell their product on its own unique merits.

There will be some buyer education required to move to a new model, but the winners in the AI era will use these discussions to better understand and serve their customers.

As their customers grow, the logic goes, so should the vendors’ contract (presumably because the vendor had some part in driving this growth).  Tethering yourself to per-seat pricing will make contract expansion much harder.

INSIGHT: AI-Powered Contracts Can Help Legal Departments Avoid Pitfalls

Legal departments face operational challenges and expectations and are also tasked with delivering great service in a timely manner, all while reducing legal risk to the business.

Recent advances in artificial intelligence showcase powerful applications of deep learning and similar technologies in a host of sectors, yet do not show any real promise toward “General AI” or technology that thinks and learns as humans do.

While contracting is a common activity, it is one that few companies manage efficiently or effectively, which often triggers a chain of deleterious events beginning with slower sales cycles, moving into “contract leakage” and at times culminating in litigation.

The main challenge firms face in contracting arises from the sheer number of contracts they must track, which in turn often lack uniformity and are difficult to organize, manage, and update Contract lifecycle management (CLM) solutions grew out of the realization that contracts have long, often-changing lives.

Static contract management systems forget the ebbs and flows—increases in obligations, opportunities to increasing pricing, important deadlines—and that leads to costly contract leakage, or the failure of a party to obtain the full benefit of its contractual bargain.

But the promise of CLM historically failed to address two critical gaps in contracting: (i) when an organization decides to use a contract proposed by the other party—a “third-party paper” contract, and (ii) legacy contracts, which are the “tale of the tape” so to speak for the organization’s historical operations.

Rather than spending weeks or months analyzing existing contracts to glean insights that may be skewed at best (and inaccurate at worst), contract management AI can quickly extract relevant data, letting legal teams spend more of their valuable time on analysis, and less on investigation.

For example, Workspace Property Trust, a commercial real estate company that specializes in the ownership, management, leasing, and development of office space, recently harnessed AI to obtain comparable lease abstracts for an asset acquisition (a classic third-party paper problem) at considerable time and cost savings.

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