In this era of digitalization and rapidly changing technologies, artificial intelligence (AI) is entering a dynamic phase of growth as adoption in enterprise sectors continues unabated. However, questions have emerged about whether AI technologies can continue to drive the revolution by delivering the value promised. Especially, AI algorithms/software companies (AI companies) face more and more challenges as the market moves from hype to reality. What can they do to ensure smooth sailing into the trillion-dollar AI market?
AI is a trillion-dollar market
Despite AI’s nascent stage of development, spending on AI-based solutions is expected to see a compound annual growth rate of 65%, exceeding $1 trillion by 2027. The AI boom is supported by technology improvements, governmental policies and active investments around the globe. Sustainable AI development is driven by three key factors:
Magnitude and depth of data — Data is a new asset for companies to leverage. Sophistication of data generation/collection (from records, vision, audio, etc.) and especially growth in the “internet of things” are expected to render massive amounts of meaningful data (~40 zettabytes of data in 2020). Deriving value through decision insights and workflow automation has become imperative for most companies. Data have become increasingly accessible to train and validate the AI algorithms, which is highly valuable to draw conclusions and predict future outcomes in AI models.
数据的大小和深度 - 数据是公司利用的新资产。数据生成/收集（来自记录，视觉，音频等）的复杂化以及特别是“物联网”的增长预计将产生大量有意义的数据（2020年约40个zettabytes的数据）。通过决策洞察和工作流程自动化获取价值已成为大多数公司的必要条件。数据越来越易于培训和验证AI算法，这对于得出结论和预测AI模型的未来结果非常有价值。
Computing power — Incorporating the most advanced technology and systems (GPU/FPGA/ASIC chip, cloud, distributed and parallel), AI has evolved from the academic to the application stage and broken the limit of traditional computing technologies.
计算能力 - 结合最先进的技术和系统（GPU / FPGA / ASIC芯片，云，分布式和并行），AI从学术阶段发展到应用阶段，突破了传统计算技术的极限。
Real value to enterprises — AI empowers real disruptive changes in businesses and allows significant value creation in areas such as chatbots for customer relationship management, fraud detection, personalization of services, etc. For example, Netflix’s AI-enabled content recommendation process has generated $1 billion in additional revenue per year.
对企业的真正价值 - AI为企业提供真正的颠覆性变革，并允许在聊天机器人等领域创造重要的价值，用于客户关系管理，欺诈检测，服务个性化等。例如，Netflix的支持AI的内容推荐流程已产生10亿美元每年额外的收入。
Prioritization is required
Each vertical has a different adoption trajectory and dynamics, driven by significances of “use cases”; therefore, prioritization is required. AI is rapidly transforming many industries. Major AI use cases have gradually emerged to address critical pain points in various industries. It is important to connect the dots between use case, analytics and data to sufficiently answer the question of real value. Depending on the quality of the AI analytics and the hurdle to demonstrate evidence and consistency in results, use cases can be viable/monetizable in the near vs. far term.
Hence, not all industry verticals are the same. Verticals can be assessed through intrinsic demand (use case value, AI adoption readiness, etc.) and ease of entry (competition, analytics transparency requirements, etc.) when evaluating AI adoptions.
Banking, financial services and insurance (BFSI) — Major AI use cases include automated trading, fraud/risk detection and customer relationship management tools. The technology adoption readiness is high for BFSI, as the sector increasingly relies on data and data analytics, which provide the foundation for the AI revolution. The core competencies of BFSI businesses — reliability, speed, safety, accuracy, etc. — can be further improved through AI technologies. The regulatory barrier is low as the industry is open to modern technological upgrades.
银行，金融服务和保险（BFSI） - 主要人工智能用例包括自动交易，欺诈/风险检测和客户关系管理工具。 BFSI的技术采用准备程度很高，因为该部门越来越依赖于数据和数据分析，这为AI革命奠定了基础。 BFSI业务的核心竞争力 - 可靠性，速度，安全性，准确性等 - 可以通过AI技术进一步提高。由于该行业对现代技术升级持开放态度，监管门槛较低。
Healthcare and life science — Major AI use cases include diagnostics assistance, drug discovery and patient management. The technology adoption readiness is at median level, but AI can significantly improve productivity and ameliorate the scarcity of medical resources. The competition and entry barrier in the industry are moderate, with many promising startups.
医疗保健和生命科学 - 主要的AI用例包括诊断帮助，药物发现和患者管理。技术采用准备就绪处于中等水平，但AI可以显着提高生产力并改善医疗资源的稀缺性。该行业的竞争和进入壁垒适中，有许多有前途的创业公司。
Advertising, media and entertainment — Major AI use cases include consumer behavior analytics, automated recommendation systems, etc. Technology adoption readiness and demand are high as this industry is highly digitalized with an advanced IT infrastructure. AI algorithms have been applied in targeted marketing to assist with customer segmentation and product promotion. The entry barrier is low, but there are concerns about data breaches in developed regions such as the EU and the U.S.
广告，媒体和娱乐 - 人工智能的主要用例包括消费者行为分析，自动推荐系统等。由于该行业通过先进的IT基础设施实现高度数字化，因此技术采用的准备和需求很高。 AI算法已应用于目标营销，以协助客户细分和产品推广。进入壁垒较低，但欧盟和美国等发达地区存在数据泄露问题
Retail (offline) — Major AI use cases include customer analytics, shopping experience enhancement and supply chain management. However, the sector faces a low technology adoption level because customer and business data cannot be transferred externally. Due to the inherent characteristics of offline retail, the urgency of AI implementation is not as high as in other industries. The entry barrier in retail is low, which is similar to that of the advertising, media and entertainment industries.
零售（离线） - 主要人工智能用例包括客户分析，购物体验增强和供应链管理。但是，由于客户和业务数据无法从外部传输，因此该部门的技术采用率较低。由于离线零售的固有特征，人工智能实施的紧迫性并不像其他行业那么高。零售业的进入壁垒较低，与广告，媒体和娱乐行业类似。
Education — Major AI use cases include adaptive learning tools and course personalization. Education is a traditional industry where data are less digitalized and structured. Many practitioners are not tech-savvy. It will take time to improve on AI technology adoption. On the other hand, AI will enable online study platforms to provide personalized courses with added value to learners. Even though the current intrinsic demand is low, it is easy to enter the education market because of few competitors and minimal regulatory obstacles.
教育 - 主要人工智能用例包括自适应学习工具和课程个性化。教育是一种传统行业，数据的数字化和结构化程度较低。许多从业者不懂技术。改进人工智能技术需要时间。另一方面，AI将使在线学习平台能够为学习者提供具有附加价值的个性化课程。尽管当前的内在需求很低，但由于竞争对手很少且监管障碍很少，因此很容易进入教育市场。
Industrial/manufacturing — Major AI use cases include quality control, yield optimization, predictive and prescriptive maintenance, and supply chain management. The large amount of internet of things data offers a strong basis for AI development in this industry. The drivers are efficiency optimization, process integration and insights analysis through advanced AI algorithms. The spectrum of potential use cases has yet to be explored thoroughly.
工业/制造业 - 人工智能的主要用例包括质量控制，产量优化，预测性和规范性维护以及供应链管理。大量的物联网数据为该行业的人工智能开发提供了坚实的基础。通过先进的AI算法实现效率优化，流程集成和洞察力分析。潜在用例范围尚未彻底探索。
High tech — Major AI use cases include all kinds of AI-empowered software systems and devices. The intrinsic demand is high because IT companies are prone to accept new technology and improve their core technological competency. On the other hand, leading high-tech giants have established their own ecosystems with internalized AI know-how and value chain capabilities.
高科技 - 主要的AI用例包括各种AI授权的软件系统和设备。内在需求很高，因为IT公司倾向于接受新技术并提高其核心技术能力。另一方面，领先的高科技巨头已经建立了自己的生态系统，具有内部化的AI专有技术和价值链功能。
Automotive and transportation — Major AI use cases include autonomous driving and route management. New AI applications are led by auto OEMs and IT giants at the pilot stage. Some of the potential applications can drive revolutionary changes and long-term demand. The barrier to entry is high as new players face intensive competition from existing players.
汽车和运输 - 主要人工智能用例包括自动驾驶和路线管理。新的AI应用程序由试点阶段的汽车OEM和IT巨头领导。一些潜在的应用程序可以推动革命性的变化和长期需求。由于新球员面临来自现有球员的激烈竞争，进入门槛很高。
Smart city — Major AI use cases include national security and traffic surveillance. This market is mainly driven by initiatives to improve governing efficiency and mitigate security risks. Non-government-related players would find it difficult to enter this market due to the highly regulated access to surveillance data and the favoritism inherited in government funding projects, especially in China.
智慧城市 - 主要人工智能用例包括国家安全和交通监控。该市场主要受提高管理效率和降低安全风险的举措的推动。由于监管数据的高度监管以及政府资助项目中继承的偏袒，特别是在中国，与非政府相关的参与者会发现很难进入这个市场。
With analysis of the intrinsic demand and ease of entry of each industry, L.E.K. Consulting identified four promising verticals for AI companies to focus on in the near term: BFSI, ads/media, healthcare and industrial internet of things (see Figure 1).
AI companies should focus on the pain points of implementation in industry verticals
AI algorithms and software companies can be divided into broad-based framework suppliers and vertical application-focused suppliers.
Technology giants like Google, Amazon and Baidu have built extensive ecosystems with a wide range of products and services, covering infrastructure, framework and implementations. Some framework-oriented suppliers typically expand their coverage to multiple vertical sectors. Others mainly focus on specific industry verticals or technology, such as Yitu Technology, Face++ (based on image reading) and iFlyTech (based on voice recognition).
谷歌，亚马逊和百度等科技巨头已经建立了广泛的生态系统，提供广泛的产品和服务，涵盖基础设施，框架和实施。一些面向框架的供应商通常将其覆盖范围扩展到多个垂直行业。其他主要关注特定的行业垂直或技术，如Yitu Technology，Face ++（基于图像阅读）和iFlyTech（基于语音识别）。
To deliver real value to customers in different industry verticals, AI companies first need to understand customers’ pain points.
1. Limited AI framework capabilities
Increasing data magnitude requires exceptional computing power and training efficiency. The frameworks existing in the market may not sufficiently handle all kinds of machine learning models.
2. Implementation talent shortages
Machine learning vertical applications require domain knowledge and customized workflows as standardized solutions do not fit all needs of end users. AI implementations often face obstacles due to lack of expert skill set and deep industry experience.
AI companies need to have a flexible and scalable AI framework supporting different models/algorithms.
For example, the processing capability of a distributed machine learning framework could have 10 times higher processing speed than that of a single framework. With the growing number of data and model parameters, processing speed becomes an increasingly important feature of any AI framework. Distributed machine learning is built on data parallelism and model parallelism, which can scale to achieve significant speed.
A modular framework is also very important. Reusable modules can allow quick implementation and reduce delivery time, which is essential when engaging with third-party developers and system integrators to co-build the vertical platforms.
AI companies need to embed industry know-how into a vertically integrated platform as much as possible to capture value.
A vertically integrated platform can be quickly deployed and capture a high share of the value created for the customers along the value chain. Even though the AI framework is typically the differentiation factor for AI companies, customer value creation is mostly realized in actual applications.
AI companies need to build up core commercial competencies
In addition to core AI technical know-how, the success of AI companies also depends on commercial capabilities and effective go-to-market strategies.
First of all, AI companies, especially startups, should prioritize verticals in order to build deep domain knowledge and develop high-profile use cases. AI companies may face competition, especially from traditional market leaders such as GE and Siemens in the industrial internet of things. There are concerns about ramp-up speed of commercial capabilities to catch up with larger and more “commercialized” competitors. It is critical for AI companies to quickly build high-profile use cases to generate attraction in priority verticals. It is also important to build a sustainable go-to-market model to be able to quickly expand customer reach and ensure market access.
AI companies need to analyze the value creation and to identify the right target customer group. They should keep flexibility in their pricing model to cater to different needs. For specific industry verticals, AI companies can identify the right combination of a go-to-market model and a set of vertical-specialized partners to ensure market access. For example, AI companies can work with hospital information system integrators to penetrate the healthcare market.
AI companies also need to work with potential clients to put AI investment at the top of the leadership agenda.
AI companies should be able to articulate the value of AI solutions. By emphasizing impact on clients’ business operations, AI companies can elevate the priority of AI investments.