While most of the world agrees that carbon dioxide emissions from human activity are creating a climate crisis, there’s little consensus regarding how to address it.
One of the solutions that’s both the most obvious and, seemingly, the most difficult for the international community to agree on is establishing a market that would put a price on carbon emissions. Making the cost of emissions palpable for industries would encourage companies to curb their polluting activities or pay to offset them.
The holy grail of a global carbon market — or a collection of regional ones — has been on the agenda for climate activists and regulators since the Kyoto Protocols were ratified in 1997, but enacting the policy has proven elusive.
Now, as the results of climate inaction become more apparent, there appears to be some movement on the regulatory front and concurrent activity from early-stage technology investors to make carbon offsets more of a reality.
It’s still early days, but startups like Project Wren, Pachama and Cloverly prove that investors and utilities are willing to take a flyer on companies that are trying to enable carbon offsets for consumers and corporations alike.
These small bets for investors are complemented by the potential for outsized returns given the size and scope that’s possible should these markets actually develop.
After years of languishing in relative obscurity, global carbon markets rebounded with vigor in 2017 and into 2018, according to data from the World Bank.
Countries raised about $44 billion in revenues from carbon pricing in 2018, an increase of $11 billion, with more than half coming from carbon taxes. In 2017, the $33 billion raised by governments from carbon pricing was an increase of 50% over 2016 numbers.
However large that number may seem, it’s dwarfed by the figure required to make any real changes in industry emissions, according to the World Bank. The current pricing schemes that exist cover a small percentage of global emissions at a cost that’s consistent with achieving the goals of the Paris Agreement, the latest international treaty around climate change and greenhouse gas emissions. Prices need to rise to between $40 per ton of carbon dioxide and $80 per ton by 2020 and between $50 per ton and $100 per ton by 2030.
Delta Air Lines, in its first-ever keynote at CES, today announced a new AI-driven system that will help it make smarter decisions when the weather turns tough and its finely tuned operations get out of whack. In a first for the passenger airline industry, the company built a full-scale digital simulation of its operations that its new system can then use to suggest the best way to handle a given situation with the fewest possible disruptions for passengers.
It’s no secret that the logistics of running an airline are incredibly complex, even on the best of days. On days with bad weather, that means airline staff has to figure out how to swap airplanes between routes to keep schedules on track, ensure that flight crews are available and within their FAA duty time regulations and that passengers can make their connections.
“Our customers expect us to get them to their destinations safely and on time, in good weather and bad,” said Erik Snell, Delta’s Senior Vice President of its Operations & Customer Center. “That’s why we’re adding a machine learning platform to our array of behind-the-scenes tools so that the more than 80,000 people of Delta can even more quickly and effectively solve problems even in the most challenging situations.”
With ConnectionSaver, United recently launched a tool that helps ground staff make the right decisions to hold planes at the gate for a few minutes to all passengers to make tight connections when things go awry. Delta’s new tools take this a step further, though, by modeling all of the company’s operations.
The new platform will go online in the spring of this year, the company says and, like most of today’s AI systems, will get smarter over time as it is fed more real-world data. Thanks to the included simulation of Delta’s operations, it’ll also include a post-mortem tool to help staff look at what decisions could have resulted in better outcomes.
Juro, a UK startup that’s using machine learning tech and user-centric design to do for contracts what Typeform does for online forms, has caught the eye of Union Square Ventures. The New York-based fund leads a $5 million Series A investment that’s being announced this morning.
Also participating in the Series A are existing investors Point Nine Capital, Taavet Hinrikus (co-founder of TransferWise) and Paul Forster (co-founder of Indeed). The round takes Juro’s total raised to-date to $8M, including a $2M seed which we covered back in 2018.
London is turning into a bit of a hub for legal tech, per Juro CEO and co-founder Richard Mabey — who cites “strong legal services industry” and “strong engineering talent” as explainers for that.
It was also, he reckons, “a bit of a draw” for Union Square Ventures — making what Juro couches as a “rare” US-to-Europe investment in legal tech in the city via the startup.
“Having brand name customers in the US certainly helped. But ultimately, they look for product-led companies with strong cross-functional teams wherever they find them,” he adds.
Juro’s business is focused on taking the tedium out of negotiating and drawing up contracts by making contract-building more interactive and trackable. It also handles e-signing, and follows on with contract management services, using machine learning tech to power features such as automatic contract tagging and for flagging up unusual language.
All of that sums to being a “contract collaboration platform”, as Juro’s marketing puts it. Think of it like Google Docs but with baked in legal smarts. There’s also support for visual garnish like animated GIFs to spice up offer letters and engage new hires.
“We have a data model underlying our editor that transforms every contract into actionable data,” says Mabey. “Juro contracts look like contracts, smell like contracts but ultimately they are written in code. And that code structures the data within them. This makes a contract manager’s life 10x easier than using an unstructured format like Word/pdf.”
“Still our main competitor is MS Word,” he adds. “Our challenge is to bring lawyers (and other users of contracts) out of Word, which is a significant task. Fortunately, Word was never designed for legal workflows, so we can add lots of value through our custom-built editor.”
Part of Juro’s Series A funds will be put towards beefing up its machine learning/data science capabilities, per Mabey — who says the overall plan at this point is to “double down on product”, including by tripling the size of the product team.
“That means hiring more designers, data scientists and engineers — building our engineering team in the Baltics,” he tells us. “There’s so much more we are excited to do, especially on the ML/data side and the funding unlocks our ability to do this. We will also be building our commercial team (marketing, sales, cs) in London to serve the EU market and expand further into the US, where we already have some customers on the ground.”
The 2016-founded startup still isn’t breaking out customer numbers but says it’s processed more than 50,000 contracts for its clients so far, noting too that those contracts have been agreed in 50+ countries. (“Everywhere from Estonia to Japan to Kazakhstan,” as Mabey puts it.)
In terms of who Juro users are, it’s still mostly “mid-market tech companies” — with Mabey citing the likes of marketplaces (Deliveroo), SaaS (Envoy) and fintechs (Luno), saying it’s especially companies processing “high volumes of contracts”.
Another vertical it’s recently expanded into is media, he notes.
“E-signature giants have grown massively in the last few years, and some are gradually encroaching into the contract lifecycle — but again, they deal with files (pdfs mostly) rather than dynamic, browser-based documentation,” he argues, adding: “In terms of new legal tech entrants — I’m excited by Kira Systems especially, who are working on unpicking pdf contracts post-signature.”
As part of the Series A, Union Square Ventures parter, John Buttrick, is joining Juro’s board.
Commenting in a supporting statement, Buttrick said: “We look for founders with products equipped to change an industry. While contract management might not be new, Juro’s transformative vision for it certainly is. There’s no greater proof of the product’s ease of use than the fact that we negotiated and closed the funding round in it. We’re delighted to support Juro’s team in making their vision a reality.”
The hype around artificial intelligence (AI) and machine learning has led to lots of jargon, so that this very powerful technique has become more difficult to understand. The tips below have all helped me, so I hope this article will help product managers to cut through the noise and better understand how AI can fit into their daily work.
Machine learning being employed to recognize vehicles (Image: Shutterstock)
A Broad Definition Enables Better Problem-solving
Let’s forget the buzzwords for a moment: what is AI anyway?
In my experience from working on data-driven products, I’ve found that a broad definition of AI helps me to focus on defining the problem I’m trying to solve, rather than fixate on specific techniques to use in the solution.
As product managers, we must thoroughly understand our problem space so that we can properly define requirements and allow our team to solve the right problems. This often leads to the topic of “how” coming up prematurely; before we know it, we have accidentally biased ourselves towards specific solutions before we’ve properly defined this problem.
To help avoid this, I prefer to define AI as “automated decision-making”.
Most products we work on require decision-making based on data, though the method for making decisions can vary. For example, decisions can be made by machines or humans, and the data can be static or dynamic. A focus on decision-making abstracts away the intricacies of specific methodologies or the noise of industry jargon. This broader definition enables product managers to be more attentive to the problem space – it removes distractions that can lead us to think about solutions too early in our process.
AI is a Tactic That Helps to Solve Problems
There are three key concepts that serve as the foundation of everything we do as product managers:
Vision: The end goal we aim to achieve
Strategy: Doing the right things to realize our vision
Tactic: Doing the things right to properly execute our strategy
Depending on the product lifecycle, product managers need to operate on all three of these levels at any given time. We must align our teams to the vision we are trying to achieve, while ensuring everyone understands the strategy and how our daily, operational tactics fit into the overall plan. It’s important to remember that AI is a tactic that can be used to solve specific problems rather than a strategy or vision. Deploying AI without an end goal usually brings no value to end users.
To give a concrete example, here is how the application of AI might fit into a team at Netflix, one of the first companies to effectively productize AI at scale:
Netflix Vision: Become the best global entertainment distribution service
Netflix Strategy: Drive member retention via engaging, personalized UX
As you can see, there are a number of tactics within the personalization strategy that could be used to achieve the goal of improving member retention. The degree to which data and AI are used varies from tactic to tactic, while the vision and strategy statements abstain from dictating which technologies or algorithms must be used.
AI can Empower Humans, Rather Than Replace Them
The current discussion on topics like automation has raised some interesting ethical questions about the future of work, and subsequently, the narrative around how AI can empower humans has become a bit lost. A common example that illustrates this point is self-driving cars. Within the industry, autonomous capabilities for vehicles are classified into five different categories, with much of the conversation fixated around what would happen in a world where cars were fully self-driving (level 5 autonomy).
What would happen in a world where cars were fully self-driving? (Image: Shutterstock)
It is important for product managers to recognize that AI capabilities are typically developed in stages over time, rather than turned on instantaneously. Machines are good at different types of tasks from humans, so certain decisions are easier to automate than others. High-performing AI capabilities require a sizable training dataset to get started, and training datasets need to be well-structured high volume, and machine-readable. Ideally, the dataset should also have well-defined notions of success and failure, where past outcomes are predictive of future results. Here is a framework that I often use when considering how to apply automated decision-making:
Along the y axis, routine scenarios happen in high frequency and have low variability in how they unfold, while nuanced scenarios seldom occur and could contain hard-to-replicate subtleties. On the x axis, informational decisions provide additional context to the end user, while action-oriented decisions perform an action on behalf of the end user. Routine scenarios tend to generate more reliable training datasets and are therefore easier for machines to learn; informational decisions tend to be lower risk than action-oriented decisions. Combining these two dimensions yields four categories of automated decisions:
Routine information: Easy to predict and low risk if wrong
Example: Car estimating remaining driving distance based on remaining fuel and driving behavior
Nuanced information: Hard to predict and low risk if wrong
Example: Car warns when the driver is falling asleep based on image recognition and driving behavior
Routine action: Easy to predict and high risk if wrong
Example: Car autonomously driving on highways under normal conditions
Nuanced action: Hard to predict and high risk if wrong
Example: Car autonomously driving through busy construction zones
AI can Drive Impact in Three Different Ways
From working on data-driven products over the last decade, I’ve identified three primary buckets of use cases for how product managers can drive impact with data:
AI can optimize and/or automate operational processes. Products with proper behavioral tracking can generate a dataset that empowers teams to make more informed decisions about how to run the business. For example, customer touchpoints and communications can be optimized based on data in order to increase conversion or reduce churn. Support requests can be triaged or routed more effectively based on the predicted topic or outcome. In this sense, AI serves as an advanced business intelligence tool that drives productivity and effectiveness for teams.
AI can dramatically improve the user experience of products. Here are some examples of how some companies have been able to use AI to create delightful experiences for their customers:
Brand
Product
AI-enhanced UX Example
Mobility
Trip Duration Estimation
Rider-Driver Matchmaking
Homes
Home Value Estimate
Home Recommendations
Entertainment
Movie Recommendations
Hero Shot Optimization
In each of these examples, the fundamental product delivered to end users remains the same (for example, mobility via Uber), but the experience around the product is better with the application of data (being matched to a nearby Uber driver). Using this pattern, product teams can often create unique user experiences that become long-term competitive advantages for their company.
AI can fundamentally change products themselves. Perhaps the most famous example of this is the story behind Netflix’s “House of Cards” series, where the usage of data redefined how entertainment is created. This series not only won many awards, but it is also loved by Netflix subscribers. It also marked the beginning of a new era of significant growth for the company. It shows that AI has the potential to create new categories of products and new trends for an entire industry.
Conclusion
To sum up, here are four ground rules for how product managers might think about integrating AI into their daily work:
Defining AI broadly frees us to focus on problem-solving rather than the end solution.
AI is a tactic that helps solve problems, not a strategy or an end-goal.
AI can empower humans, rather than replace them.
AI can drive impact in three different ways: optimize operations, improve product experiences, and create new product categories.
