We recently came across a Forbes piece that distilled the findings of a Forrester report on artificial intelligence into the 10 “hottest” AI technologies.
As suckers for clickbait, as well as folks interested in the implications of AI for the procurement sector, we checked it out.
ICYMI, here they are, according to Forbes contributor Gil Press, taken from Forrester’s TechRadar™: Artificial Intelligence Technologies, Q1 2017:
The 10 Hottest AI Technologies
- Natural Language Generation
- Speech Recognition
- Virtual Agents
- Machine Learning Platforms
- AI-Optimized Hardware
- Decision Management
- Deep Learning Platforms
- Robotic Process Automation
- Text Analytics and Natural Language Processing (NLP)
Which Ones Matter Most to Procurement Today (and in the Future)?
In procurement, “AI could soon help tackle some of the big challenges facing the industry,” writes Andrew Nichols, analytics lead and head of procurement at Tungsten Network, a global e-invoicing and analytics firm, in a guest post on Spend Matters UK.
Those challenges include “identifying new markets, managing supply chain risks, tracking exchange rate volatility and finding the best value without compromising on quality.”
“Making the most of big data is another area on which procurement leaders are focusing,” Nichols writes. “An example of how this can be achieved is spend analytics software, which has been helping procurement departments identify where cost savings can be made.”
Here are a couple artificial intelligence technologies that procurement practitioners should keep an especially close eye on, with regard to contract lifecycle management specifically:
#10 Natural Language Processing (NLP)
When it comes to contract management, you may have wondered, “How the heck do I actually model and extract all of this knowledge out of our existing contract documents in the first place?”
This is a great question, and as Spend Matters Chief Research Officer Pierre Mitchell has written, “the answer is not easy, because you need to solve the multilingual syntax and semantics problem before you get to higher-level knowledge ontologies. This is where NLP comes in. It’s needed for data mining of external ‘big data’ sources and for addressing the legacy contract encoding problem.”
That eventually leads to…
#4 Machine Learning
In the same piece, Mitchell writes, “the term machine learning refers to computers that ‘learn’ from the data they process rather than relying on humans for rules-based procedural programming to act upon that data. It not only discovers patterns in data but also specifically helps correlate various data inputs and key data outputs, which helps enable predictive analytics.
To get context on both NLP and machine learning in contracts, start at the beginning — it all starts with a base of knowledge.
Forrester AI Report: Key Takeaway
One of the three key takeaways of Forrester’s report is that, as they authors put it, “AI technologies demand new skills, not a new team.”
They write that:
“You can build these intelligent systems with existing development and data science teams, albeit with deeper partnerships among them and novel new roles.”
To that point, stay tuned for Pierre Mitchell’s upcoming series on the Collective Intelligence of Supply, which speaks to how practitioners can best equip themselves to utilize a matrix of interconnected and at times overlapping practices and technologies — not just hop on an AI bandwagon.
According to Mitchell, “there is a lot of content out there on digital ‘disruption,’ including for procurement, but I personally find most of it laden with jargon, and not very instructive on what to do about it all. This is a shame because the fundamental transformation and evolution of value chains offers so many opportunities that supply professionals can seize upon if they understand the changes and how to harness them.”
A recent article in the Atlantic put forth a provocative thesis, one that our editorial team has discussed internally before in light of marketing language sometimes clouding our judgment as followers of trends in the B2B media.
The headline: “Artificial Intelligence” Has Become Meaningless
The sub-head: It’s often just a fancy name for a computer program.
Are we really at the place where the definition of “AI” is akin to defining something as “disruptive” or “innovative” these days?
Give it a full read (we will too) and tell us what you think!