As Commonwealth Bank (CommBank) CEO Matt Comyn delivered the full financial year results in August 2021 over videoconference, it took less than two minutes for him to make his first mention of the organization's Customer Engagement Engine (CEE), the AI-driven customer experience platform. With full cross-channel integration, CEE operated using 450 machine learning models that learned from a total of 157 billion data points. Against the backdrop of a once-in-a century global pandemic, CEE had helped the Group deliver a strong financial performance while also supporting customers with assistance packages designed in response to the coronavirus outbreak. Six years earlier, in 2015, financial services were embarking on a transformation driven by the increased availability and standardization of data and artificial intelligence (AI). Speed, access and price, once key differentiators for attracting and retaining customers, had been commoditized by AI, and new differentiators such as customization and enhanced interactions were expected. Seeking to create value for customers through an efficient, data-driven practice, CommBank leveraged existing channels of operations. Angus Sullivan, Group Executive of Retail Banking, remarked, "How do we, over thousands of interactions, try and generate the same outcomes as from a really in-depth, one-to-one conversation?" The leadership team began to make key investments in data and infrastructure. While some headway had been made, newly appointed Chief Data and Analytics Officer, Andrew McMullan, was brought in to catalyze the process and progress of the leadership's vision for a new customer experience.
The case opens in August 2021, as Habib and Shahysta Hassim, husband and wife co-founders of the data labeling company SmartOne, contemplate the strategy of the high growth company. Between 2016 and 2021, SmartOne had kept doubling its size every two years and now, with its workforce of 1,000, it was annotating data for global tech clients. The case provides a background on SmartOne's journey from call center operations to data labeling and elaborates on the company's operating and business model, providing details on processes such as: recruiting, training, managing the workforce, project management, and quality control. The case also provides a background on data labeling, data pipeline and the AI factory (a term explained in the case which represents the AI industry value chain) for larger context and gives an overview of the competitive environment. In August 2021, the co-founders needed a strategy to shape the company's future. Where in the AI factory could SmartOne position itself to remain relevant and take a piece of the evolving pie? Should the company grow upstream, to become a full data pipeline provider, or downstream into developing algorithms?
Florian Hillen, co-founder and CEO of VideaHealth, a startup that used artificial intelligence (AI) to detect dental conditions on x-rays, spent the early years of his company laying the groundwork for an AI factory. A process for quickly building and iterating on new AI products, Hillen believed that the AI factory would prove a competitive advantage when he took his company to market. The AI factory consisted of four stages: the data pipeline, which required labeling dental pathologies on x-rays; algorithm development, which required writing computer programs that learned to recognize the presence of dental conditions and identify them in un-labeled x-rays; an experimentation platform, which tested the accuracy of Videa's diagnoses and determined the appropriate sensitivity of the AI models; and software infrastructure, which deployed the company's AI programs and managed the process for cleaning, storing, and transmitting data. Hillen knew that his company's technology would appeal to individual dentists or dental service organizations (DSOs), who could use AI to make diagnoses more accurate and improve patient trust. Yet Videa would also benefit insurance companies, who could use the technology during the claims review process. Poised to take the company to market, Hillen must decide which customer segment to pursue.
Threadless, an online apparel company and artist community which Jake Nickell founded in 2000, continued to maintain its status as a top company in the online apparel industry during its second decade. From 2010 to 2020, Threadless continued to operated its crowd-sourcing platform, while it transitioned away from traditional screen printing to a digital print-on-demand model. Concurrently, the company jettisoned its warehouse and built a worldwide network of manufacturers that could print and ship Threadless orders on demand. Threadless also launched a new platform called Artist Shops that allowed graphic artists to sell apparel in uniquely branded online stores, with the option of having Threadless manage their pricing and promotional events. The software Threadless developed to facilitate its manufacturing network and Artist Shops platform also led Threadless to increasingly view itself as a technology company performing intermediary services, rather than merely an online apparel company. The onset of the COVID-19 pandemic in 2020 accelerated the company's transition, triggering the sale of Threadless's office and a move to working from home. Nickell wondered what the next steps for the company should be.
Over the last decade, experimentation has become integral to the research and development processes of technology companies-including Yelp-for understanding customer preferences and mitigating innovation risks. The case describes Yelp's journey with experimentation, from running a few experiments across various teams to building a centralized experimentation platform that standardized and improved the experimentation process. Concurrently, the case describes a pivotal experiment to evaluate the impact of geographically constrained adverts on user experience and revenue. The results of the experiment are not included in the case but are part of supplementary data (courseware no. 621-703); instructors can either provide the data to the students or give the results in a table as part of the assignment questions. Based on the results, the protagonist must determine an appropriate course of action, carefully managing the various trade-offs.
In the midst of 2020, as the coronavirus pandemic was unfolding, OpenIDEO - an online open innovation platform focused on design-driven solutions to social issues - rapidly launched a new challenge to improve access to health information, empower communities to stay safe during the COVID-19 crisis, and inspire global leaders to communicate effectively. OpenIDEO was particularly suited to challenges which required cross-system or sector-wide collaboration due to its focus on social impact and ecosystem design, but its leadership pondered how they could continue to improve virtual collaboration and to share their insights from nearly a decade of running online challenges. Conceived as an exercise of disruptive digital innovation, OpenIDEO successfully created a strong open innovation community, but how could they sustain - or even improve - their support to community members and increase the social impact of their online challenges in the coming years?
True North was a private equity fund that specialized in the growth and buyout of mid-market, India-centric companies. The leadership team initially believed that technology was not core to traditional businesses and steered clear of new age technology-oriented businesses. Then, in 2007, True North invested in Meru Cabs, a ride-sharing service with a growing fleet of cabs that had to change its business model with the launch of tech-based platforms like Uber. This experience led True North to significantly alter its stance on the importance of technology-powered business models. The team learned that all businesses needed to effectively become technology businesses to remain relevant. True North initiated the "Analytics, Algorithms and Artificial Intelligence" (3A) project. A system, Kelp, was designed and developed internally to facilitate the transformation of True North into an AI-first firm based on the introduction of data-driven decision making and productivity-enhancing digital tools. In 2020, as different elements of Kelp were launched, the leadership team met to discuss the future of this technology.
In summer 2020, Stephane Bancel, CEO of biotech firm Moderna, faces several challenges as his company races to develop a vaccine for COVID-19. The case explores how a company builds a digital organization, and leverages artificial intelligence and other digital resources to speed its operations, manage its processes and ensure quality across research, testing and manufacturing. Built from the ground up as such a digital organization, Moderna was able to respond to the challenge of developing a vaccine as soon as the gene sequence for the virus was posted to the Web on January 11, 2020. As the vaccine enters Phase III clinical trials, Bancel considers several issues: How should Bancel and his team balance the demands of developing a vaccine for a virus creating a global pandemic alongside the other important vaccines and therapies in Moderna's pipeline? How should Moderna communicate its goals and vision to investors in this unprecedented time? Should Moderna be concerned it will be pegged as "a COVID-19 company?"
Over the course of the 2010s, the rapid advancement of mobile technologies and the rise of online freelancing platforms seemed to portend a radical transformation of labor markets into on-demand, flexible talent pools. Even though several Fortune 500 companies-including Microsoft, Samsung, and General Electric-embraced digital labor solutions, enterprise adoption lagged far behind smaller businesses and startups. Despite the promising potential benefits, concerns persisted about navigating labor regulations, ensuring appropriate vetting, and guaranteeing the quality of work. Sarah Tang, the newly appointed Vice President of Enterprise at Freelancer, Ltd., took on the challenge of crafting the growth strategy, operations, and sales of Freelancer's services to Fortune 500 companies. What it would take to convince more enterprises of the potential of on-demand freelance labor that could help them hire skilled freelancers in volume or in multiple countries simultaneously? What did the future hold for open work practices between enterprises and digital labor markets?
Today markets are being reshaped by a new kind of firm--one in which artificial intelligence runs the show. This cohort includes giants like Google, Facebook, and Alibaba, and growing businesses such as Wayfair and Ocado. Every time we use their services, the same thing happens: Rather than relying on processes run by employees, the value we get is delivered by algorithms. Software is at the core of the enterprise, and humans are moved off to the side. This model frees firms from traditional operating constraints and enables them to compete in unprecedented ways. AI-driven processes can be scaled up incredibly quickly, allow for greater scope because they can be connected to many kinds of businesses, and offer very powerful opportunities for learning and improvement. And while the value of scale eventually begins to level off in traditional models, in AI-based ones, it never stops climbing. All of that allows AI-driven firms to quickly overtake traditional ones. As AI models blur the lines between industries, strategies are relying less on specialized expertise and differentiation based on cost, quality, and branding, and more on business network position, unique data, and the deployment of sophisticated analytics.
In 2015, Dietmar Hopp, owner of Germany's Bundesliga football team TSG Hoffenheim and co-founder of the global enterprise software company SAP, was considering how to ensure long-term sustainability and competitiveness for TSG Hoffenheim. While historically a small team from bottom rungs of the league, TSG Hoffenheim, with revenues of €60 million to €70 million, reached the top division of the Bundesliga in the 2008-2009 season thanks to a deliberate strategy focused on enhanced scouting, strong youth programs, and innovative technology and analytics that improved player development. In 2014 Hopp, who had personally invested €300 million in the club, built a "footbonaut," an automated training environment that collected data on players' skills and strengths. The tool, one of three in the world, helped scouts and coaches better assess and develop each player. Yet some managers felt the technology was a distraction, an investment too expensive for a team that was not yet cash-flow positive. The team finished the 2014-2015 season in eighth place, below the top division, and Hopp wondered whether the focus on technology and analytics was the right strategy to grow the club. He wondered if the "moneyball" approach-when a smaller team competed with wealthier teams by using statistical analysis to buy undervalued assets and sell overvalued assets-could work in football and if investments in technology could lead the team to financial independence.
This note provides essential information on funding sources and valuation topics facing deep technology startups-ventures that are capital-intensive, with high technical and market uncertainty, and require long gestation periods. Both dilutive and non-dilutive sources of investment are described, along with empirical examples and figures.