Hello Heart, a hypertension management app debated whether to go deep and cover other heart conditions, or to expand its solution to other chronic conditions.

In March 2021, Amnon Shashua, co-founder and CEO of Israel-based Mobileye, was preparing to meet with Intel's new CEO, Pat Gelsinger, to review plans for the future. Mobileye had been acquired by California-based Intel in 2017, but still operated independently. Mobileye was the global leader in vision technology for Advanced Driver Assistance Systems (ADAS) with a 70% market share and $1 billion in revenue. However, for Shashua, ADAS was just the first step towards his dream of leading the autonomous vehicle (AV) revolution. It was this vision that led Intel to acquire Mobileye for $15.3 billion. Shashua's challenge was that consumer AVs were still years away due to concerns over safety, regulation, cost, and consumer acceptance. A nearer term use case for AVs was the robotaxi market-fully autonomous, driverless taxis. Shashua and his team were excited about the potential of robotaxis to change the future of mobility, projecting that the market would grow to $160 billion globally by 2030. Mobileye believed that it could generate at least $15 billion in annual robotaxi revenue by the end of the decade. Equally important, Shashua viewed robotaxis as a necessary first step toward consumer AVs. Mobileye could use its experience in robotaxis to improve AV technology, address regulatory challenges, and build high definition maps. The long-term question facing Mobileye was whether to: 1) invest billions of dollars to build-out a global, vertically integrated robotaxi business; 2) use robotaxis as an opportunity to learn and then revert back to a horizontal supplier of AV chips and software; and/or 3) do both? During most of Intel's history, the company had been a horizontal semiconductor company which avoided vertically integrating into its customers' businesses.

Autonomous business monitoring platform Anodot leveraged machine learning to providing real-time alerts regarding business anomalies. Anodot's solution was used in various industries in order to primarily monitor business health, such as revenue and payments, product usage and customer experience. Every day, Anodot used 30 types of learning algorithms to analyze 6.2 billion data points and 428 million unique metrics. By 2019, Anodot's platform tracked more than 400 million metrics daily, driving four billion autonomous decisions that were translated to less than 1,000 alerts for all its customers. This highly accurate monitoring led to a low incidence of false positives, or false alerts, and customer satisfaction was high. Since Anodot's tool had the ability to identify granular business anomalies in real time, such as an unexpected drop in e-commerce sales for particular products or markets due to a technical glitch, fast detection and resolution of the problem meant that the potential financial damage could not be easily measured. The management team contemplated several strategic issues: How could they help their customers realize the value of Anodot? They had been working on several tools to show the value in different stages of the sales cycle and post-sale, but it was still hard to measure the actual financial value. In 2019, Anodot had adjusted its strategy to focus on client verticals and use-cases that would benefit most from Anodot. Would this make the sales process any easier? An improved product-market fit, combined with an ability to measure Anodot's value, could increase conversion and retention. Should they narrow down the use cases even more? As the team was thinking about their next funding round, it was important to prioritize their efforts.

Social trading platform eToro was preparing for the launch of its expanded offering in the U.S. The company faced critical decisions regarding product-market fit, go-to-market strategy, positioning and monetization. Moreover, it faced the challenge of how best to make its social features relevant to U.S. consumers. eToro was founded in Israel in 2007, with a mission to democratize investing. The cofounders, the Assia brothers, were determined to open the global markets for everyone to trade and invest in a simple and transparent way. By 2020, eToro's customers in most countries were able to trade multiple assets, and in most territories the company offered zero commission stock trading. The vast majority of its users shared their investment strategy, and others in the community could review and even copy their portfolio with a single click. A subset of users were able to garner a large following and become "investment celebrities," enjoying fame and often fortune. Figuring out the best path forward with respect to the U.S. launch was complicated by shifts in the competitive landscape, which had seen consolidation of large players in the U.S. market and the emergence of fintech firms that offered zero commission trading and appealed to millennials. The U.S. regulatory approval was a pivotal milestone in eToro's evolution. But with a limited suite of products, a modest community and a market where zero-commission stock trading was already commonplace, the management team deliberated on what it would take to make inroads in the U.S. and what success could look like.

Eye-controlled communication device startup EyeControl was founded in Tel Aviv, Israel in 2016 by cofounders with a shared personal connection to locked-in syndrome-a neurological disorder that left sufferers cognitively sound, yet paralyzed, with the exception of eye movement. The team was committed to addressing the frustrations that accompanied the loss of communication associated with the condition. The EyeControl device was reasonably priced, required minimal set-up, and was the first screen-free solution available-thus easy to use around the clock and in any setting, compared with the costly, cumbersome, and complex to use existing solutions. EyeControl initially targeted the home-care segment of permanently locked-in individuals, estimated at more than 850,000 patients worldwide. Seeing the efficacy of their device, the cofounders contemplated targeting the hospital facilities segment, a far larger market. So far, EyeControl had raised $5 million from startup competitions, accelerator awards, grants, and angel and impact investors. Yet they also realized that their cash needs would be far greater if they were to now pursue the facilities market opportunity, requiring them to conduct costly clinical trials. They would have to seek funding outside of grants and impact funds, tapping traditional VC funds who would be interested in the $ 5 billion potential of the facilities market. The cofounders also wondered whether a strategic corporate partner was a potential alternative solution. As they were about to start clinical trials for their facilities product, they were still unsure whether they should start penetrating the facilities market or if they should concentrate all their efforts in the home-care market. Would it be possible to do both?

An unlikely but highly effective leader of a traditional bank, Rakefet Russak-Aminoach, simultaneously leads a classic change effort and an unconventional effort to innovate.

Launching its first products in the fall of 2016 in New York, insurtech startup Lemonade was on a mission to disrupt the insurance market by using AI and behavioral economics principles. The company offered renters, homeowners and condo insurance and mainly targeted millennial, tech-savvy individuals with relatively few assets- customers who were less attractive to traditional insurers who had costly, labor-intensive, operations. By offering a delightful, bot-driven consumer experience, embracing a model that removed any incentive to deny claims and that let customers pick causes to donate leftover money to, building a loveable brand, and employing creative digital advertising - the company was able to grow at an impressive pace. By the end of 2018, Lemonade was active in 24 U.S. states, had insured 425,000 customers and was expecting sales (gross premiums) of over $57 million. In an effort to sustain the spectacular growth momentum, the co-founders sought to pursue ambitious plans in 2019, including the launch of new insurance product lines as well as international expansion. But limited resources and scarce management bandwidth suggested that taking on both moves simultaneously could be highly risky. In deciding which path to take, the leadership team had to consider whether it was more important at this stage to scale globally with the current product lineup or to cover a breadth of insurance categories and stay in the U.S. for now. Assessing the relevance of the business model and the role of AI for each of these moves, understanding the nature of consumers in different markets, and foreseeing competitive pressures from both incumbents and startups were key in making the right call.

Four years after being founded Amir Amir Konigsberg (CEO) and Adi Avidor (CTO), Twiggle had developed a search enhancement that plugged into the online merchants' existing framework. The company utilized advanced structuring and linguistic tools to build search technology that understood shopper intent and matched it with the right products. Twiggle was founded in 2014 by former Google executives Amir Konigsberg (CEO) and Adi Avidor (CTO), who believed e-tailers were losing enormous revenue due to poor search results. The team built a set of proprietary tools that created a human-like understanding of customer queries in the e-commerce search experience, using natural language processing that translated text into meaning. The team also constructed a semantic model of the e-commerce world for three product domains: fashion, home and electronics. The resulting "ontology" was a set of concepts and categories in a subject area that showed their properties and the relations between them. The Twiggle's tools could also unlock significant online sales revenue by increasing click-through and add-to-cart rates, ultimately improving sales conversions. So far, Twiggle had secured deals with more than half a dozen large e-commerce retailers and, so far, could improve search results in three product categories: fashion, home, and electronics. The company initially targeted large direct-to-consumer e-commerce players. Yet the cofounders encountered challenges pursuing this type of customer. The customer acquisition process included lengthy, intensive face-to-face sales cycles, expensive and technologically complex proof-of-concept testing, and requests for customization. Konigsberg and Avidor wondered if they should expand their customer focus to target a wider set of smaller e-tailers that still had significant volumes of online search queries, but their search quality tended to be less robust and might be easier to improve.