A Fábrica de Serviços do Futuro

Imagine um mundo em que você nunca precisa tentar freneticamente chegar ao atendimento ao cliente quando o serviço da Internet cair e depois lutar para solucionar problemas pelo telefone. Em vez disso, o provedor chama você para tomar medidas preventivas - ou corrige o problema antes mesmo de saber que tem um. E quando você está comprando um provedor de serviços, a empresa de telecomunicações usa dados disponíveis para recomendar um pacote adaptado às necessidades e orçamento da sua família; portanto, você não precisa percorrer combinações complexas de preços, serviços, uso de dados e opções de dispositivos. Os setores tão diversos quanto finanças, transporte, energia e telecomunicações estão iniciando uma transformação que não é diferente da adoção de tecnologias da indústria 4.0 pela fabricação. Today’s service factory—with its mass production of commodity products, giant call centers, and rigidly standardized processes and scripts for dealing with everything from customer inquiries and technical support to after-sales service—will be replaced by dispersed, minimalist workforces of highly skilled specialists collaborating with digital technologies and tools, such as artificial intellgence, advanced robotics, blockchain, and predictive data análise. Esses

Over the coming decade, this vision will become a reality. Sectors as diverse as finance, transportation, energy, and telecommunications are beginning a transformation not unlike manufacturing’s adoption of Industry 4.0 technologies. Today’s service factory—with its mass production of commodity products, giant call centers, and rigidly standardized processes and scripts for dealing with everything from customer inquiries and technical support to after-sales service—will be replaced by dispersed, minimalist workforces of highly skilled specialists collaborating with digital technologies and tools, such as artificial intellgence, advanced robotics, blockchain, and predictive data analytics. These A BIONIC COMPANY Será capaz de antecipar, montar e fornecer ofertas hiperpessoalizadas e ultra -industrializadas em tempo real, com base em "bits de serviço" modulares que podem ser misturados e combinados como tijolos de Lego. Provedores de serviços titulares como Lloyds Bank, Telstra e Starbucks também começaram a transição. Esses primeiros adotantes estão fornecendo experiências altamente personalizadas, com o traseiro de microsserviço adaptativo e econômico e arquiteturas de TI com base nas interfaces de programação de aplicativos (APIs). Mas em quase todos os setores, a concorrência está se intensificando dos participantes perturbadores e digitalmente nativos. Portanto, a hora de começar é agora.

Digitally native service companies such as Amazon, Alibaba, Tencent, Expedia, and Uber already are well along on this journey; incumbent service providers like Lloyds Bank, Telstra, and Starbucks have also begun the transition. These early adopters are providing highly personalized experiences on the back of adaptive and cost-efficient microservice and IT architectures based on application programming interfaces (APIs).

The full transition to the service factory of the future entails a massive transformation that could take five to ten years. But in nearly every sector, competition is intensifying from disruptive, digitally native entrants. So the time to start is now.

A fábrica de serviços tradicional está saindo

To get a sense of how far service industries still must travel, consider the revolutionary changes that are transforming manufacturing. Most leading automakers, for example, long ago adopted modular assembly methods that enable them to customize at mass efficiency using different combinations of standardized components. Tesla has taken the concept further with connected cars that allow owners to modify their autopilot systems, battery life, and other features by downloading over-the-air software updates. Industry 4.0 manufacturing systems—autonomous robots, 3-D printing, and digital simulation, among other tools—are enabling producers of everything from athletic shoes to aircraft components to shift from mass production to mass customization and from giant factories concentrated in a few low-cost countries to smaller, more flexible facilities dispersed in locations closer to end customers.

Por outro lado, a maioria das organizações de serviço permanece ancorada nos modelos industriais mais antigos. A partir da década de 1980, as companhias aéreas, seguradoras, prestadores de cuidados de saúde, empresas de cartão de crédito e outras empresas começaram a terceirizar o trabalho de back-office intensivo para os prestadores de serviços compartilhados-primeiro perto de casa e depois cada vez mais offshore-para reduzir custos e melhorar a eficiência. Na maioria dos setores, essa extensão de bancada foi o mais longe do que a transformação. A maioria das grandes empresas de serviços ainda usa layouts tradicionais de fábrica que dependem de um grande número de pessoas que seguem fluxos de trabalho que são conectados aos sistemas de TI herdados. Os clientes de telecomunicações tiveram que suportar longas esperas por reparos e clientes bancários longos esperam que os empréstimos sejam aprovados e desembolsados. Ao arquivar reivindicações de seguro, os clientes geralmente precisam preencher formulários longos à mão que solicitam dados pessoais que suas seguradoras já possuem, enviam documentação extensa e aguardam meses de reembolso. Soluções personalizadas para clientes preferidos ou aqueles dispostos a pagar um prêmio. Mas essa abordagem não é adequada para atender às expectativas dos clientes nos mercados de serviços altamente contestados de hoje. Os provedores de ponta estão se movendo para a hiperpessoalização e a ultra-industrialização. Eles estão aspirando a fornecer serviços que podem ser adaptados aos gostos, interesses e necessidades imediatas em eficiência de massa. A situação atual pode até desencadear maior aceitação por clientes e funcionários de uma introdução acelerada de tecnologias digitais.

While the traditional service factory works well in terms of reducing costs and enabling service companies to scale up, it has always had limitations. Telecom customers have had to endure long waits for repairs and bank customers long waits for loans to be approved and disbursed. When filing insurance claims, customers often must fill out lengthy forms by hand that ask for personal data their insurers already have, submit extensive documentation, and wait months for reimbursement.

Constrained by their factory model and by poor IT architecture, many providers have simply accepted these tradeoffs between cost efficiency and quality and offered two tiers of service: mediocre quality and standardized offerings for the general public and highly attentive service and customized solutions for preferred customers or those willing to pay a premium. But this approach is not adequate to meet customer expectations in today’s highly contested services markets. Leading-edge providers are moving to both hyperpersonalization and ultraindustrialization. They are aspiring to deliver services that can be adapted to the evolving tastes, interests, and immediate needs of individual consumers at mass efficiency.

As the global economy recovers from the COVID-19 crisis, the challenging business environment will further increase the pressure to cut costs, deliver services in ways that build resilience against future shocks, and meet customer expectations for more personalized and enjoyable service. The current situation may even trigger greater acceptance by customers and employees of an accelerated introduction of digital technologies.

Anatomia da fábrica de serviços do futuro

A fábrica de serviços do futuro quebrará o compromisso entre personalização e industrialização, aproveitando os bits de serviço padrão: pequenos elementos de serviço, como um chatbot ou um carrinho de compras on -line. Os bits de serviço consistirão cada vez mais em "microsserviços"-ofertas ou processos digitalizados de serviços-que são acessados ​​através de APIs e criados internamente ou adquiridos a partir de parceiros do ecossistema. Os bits também podem ser atividades automatizadas ou de serviço manual com base em sistemas de TI legados. Ao combinar flexivelmente bits de serviço, a fábrica de serviços do futuro poderá criar ofertas e pacotes hiperpessoais adaptados às necessidades, preferências e hábitos de um indivíduo com base em uma ampla gama de Dados do cliente .

Migração para a fábrica de serviços do futuro requer mudança transformadora em cinco dimensões críticas: experiência do cliente, prestação de serviços, tecnologia digital, pessoas e organização e ecossistemas digitais. (Consulte o Anexo 1.)

Customer Experience

Most service providers currently operate in a reactive way. They wait for customers to articulate their needs and then respond by offering a range of preset offerings with different levels of service. The service factory of the future will enable providers to be predictive, preventive, and proactive. It will anticipate customers’ needs and approach them with solutions and hyperpersonalized experiences. More important, it will develop capabilities to prevent service lapses from occurring in the first place. Telcos, for instance, will be able to use predictive network maintenance to identify network issues before they occur. Leading insurance companies have already launched digital services that provide early warnings aimed at preventing property loss and casualty.

The service factory of the future will also enable companies to leverage new AI-powered tools that can recognize customer emotions and personality traits to provide context-based offerings. General Motors has demonstrated its interest in deploying facial recognition to pick up emotional signals, for example, by installing interactive billboards in public spaces in which an “actor” presenting a car responds differently to viewers depending on their age, sex, facial expressions, movements, and reactions. A company could customize the experience by combining such recognition tools with its own data from previous customer touchpoints and with information gleaned from the internet. Does a particular customer prefer to engage with a sales agent who straightforwardly presents the facts, for example, or with one who conveys empathy? With someone who is younger or someone older?

Entrega de serviço

Automated “smart” workflows that retrieve the right information and select the right combinations of microservices for the customer will replace rigid workflows offering standard products. These smart workflows will continuously improve and become more efficient through machine learning that allows for individualized and real-time rearrangement of microservices.

Esses recursos ainda não existem. Mas as ferramentas digitais estão chegando. A Alemanha Celonis, por exemplo, oferece uma solução de "mineração de processos" que cria transparência completa do processo, identifica gargalos e destaca o potencial de otimização em processos como atendimento ao cliente, ordem de dinheiro e planejamento de produção. A empresa inteligente de nuvem de negócios da empresa analisa e visualiza os dados do evento capturados em sistemas de TI subjacentes, como o SAP, para superar as ineficiências do processo. Enquanto isso, os algoritmos estão melhorando em tomar decisões que sempre exigiram julgamento humano. O credor turnkey, com sede em Austin, no Texas, por exemplo, oferece software corporativo cujos algoritmos de aprendizado de máquina avaliam os perfis de risco dos mutuários, acessando e analisando mais dados-como histórico de pagamentos-que os agentes de empréstimos geralmente usam para tomar decisões. Os algoritmos do credor turnkey também aceleram o processo de processamento e aprovação de empréstimos. Uma fábrica de serviços do futuro recuperará todas as informações necessárias e de fontes externas e processará as reivindicações rapidamente. Por exemplo, pode inspecionar danos causados ​​por inundação ou furacões a uma casa das imagens de drones e iniciar o processo de reivindicação proativamente. Além disso, ao acessar dados nas casas, veículos e status médico dos clientes, a seguradora poderá reconhecer e antecipar suas necessidades de seguro e oferecer pacotes completos e personalizados de serviços - e alterar essas ofertas em resposta a eventos da vida, como o nascimento de uma criança ou uma mudança para uma cidade diferente.

To get a sense of how service delivery can change, consider a typical insurance company. A service factory of the future will retrieve all the necessary information from its own and from outside sources and process claims quickly. For instance, it might inspect flood or hurricane damage to a home from drone images and begin the claim process proactively. What’s more, by accessing data on customers’ homes, vehicles, and medical status, the insurer will be able to recognize and anticipate their insurance needs and offer full, custom packages of services—and alter those offerings in response to life events, such as the birth of a child or a move to a different city.

TECNOLOGIA DIGITAL

In the legacy IT environment of most large enterprises, data storage and consumption are still largely fragmented, siloed, and redundant across functions such as sales and marketing, customer operations, and billing. Moreover, front-end software that contains data inputs from users and covers much of the business logic is often integrated with monolithic, and partly outdated or obsolete, back-end information technology. (See Exhibit 2.) That makes it difficult to retrieve and use relevant data along the customer journey. Moreover, applications are often based on thousands of lines of linear code that are hard or even impossible to modify, owing to their hard-coded linkages to other programs. As a result, workflows—such as the steps that call center agents or automated chatbots take to address technical issues or handle insurance claims—are rigidly defined and require “human glue” in the form of manual interventions.

Digitally advanced companies, such as fintechs, online travel, digital advertising, hospitality sites, and video-streaming services, have turned to an architecture in which data is decoupled from legacy system workflows. Data from across the enterprise is pooled in a data lake. An application layer draws data from this shared pool to create microservices that are capable of running independently. An e-commerce platform, for example, can build its workflow from different types of customer data records and from such microservices as account creation, online searches, shopping carts, and payment transactions. APIs connect the digitized service offerings or microservices to front-end applications that customers view through their web browsers. A travel site such as Expedia has thousands of APIs that enable customers to check flight availability, compare prices, and view hotel options.

Incumbent service providers are likewise beginning to migrate to new architectures. A leading European financial group, for example, has launched an API-enabled open banking facility that provides a single view of all its banking and insurance customers’ data. The group also aims to have a single view of its customers across all its banks in Europe, which will allow for faster payments between accounts and lower costs.

People and Organization

The service factory of the future will be organized to better reflect the ways in which technology has changed how work gets done. Enterprises need to move from pyramid structures, with huge operations staffs and business units reporting upward, to a “rocket shaped” structure with a digital corporate center at the top controlling data, setting governance policies, and providing insights that help steer the business. This structure will have fewer middle layers—and potentially none—because there will be no need for multiple levels of reporting and management. At the bottom, automated tools embedded within business unit teams will take over many transactional tasks, executing them faster and more accurately and at lower cost. At Alibaba, for example, most decisions are made by a “platform” of semiautonomous front-end teams.

A fábrica de serviços do futuro também exigirá um tipo diferente de força de trabalho. O enorme número de generalistas moderadamente qualificados alojados nas instalações centrais que a fábrica de serviços de hoje exige para a produção em massa e as interações básicas do cliente darão lugar à automação e dados, tecnologias digitais e especialistas em atendimento ao cliente em locais dispersos. Para adquirir o talento digital certo, as empresas de serviços tradicionais terão que imitar o Vale do Silício, oferecendo incentivos como a carreira acelerada e rotinas de trabalho flexíveis. Muitos funcionários existentes exigirão treinamento em novas habilidades e maneiras de trabalhar. Tais mudanças organizacionais ajudarão a manter a empresa resiliente por meio do ambiente de negócios resistente e de mudança rápida do mundo pós-Covid. Os provedores precisarão passar de cadeias de valor verticalmente integradas para aquelas que aproveitam um ecossistema amplo e diversificado de parceiros e fornecedores.

Digital Ecosystems

On its own, no company will be able to create all the microservices needed to deliver cost-efficient, best-in-class hyperpersonalized offerings. Providers will need to move from vertically integrated value chains to ones that leverage a broad and diverse ecosystem of partners and vendors.

Alexa Digital Assistant da Amazon oferece uma ilustração precoce de onde os ecossistemas de serviço estão chefiados. A maioria das dezenas de milhares de "habilidades" da Alexa é fornecida por um vasto ecossistema de aplicativos e provedores de serviços conectados à sua plataforma. Da mesma forma, a experiência modular e de viagem modular da Expedia é ativada por um ecossistema de mais de 10.000 parceiros, enquanto o ecossistema do WeChat da China permitiu que o aplicativo evoluísse de mensagens sociais para uma plataforma de microsserviços para comprar e vender produtos, fazer pagamentos, pedir alimentos e mantimentos e verificar as notícias. Para aprimorar a experiência e o engajamento dos clientes, uma líder de telecomunicações formou várias parcerias para lançar uma plataforma on -line que permite que clientes e funcionários discutam produtos e serviços, resolvam problemas rapidamente e compartilhem experiências. O Uber cresceu orquestrando a experiência digital de muitos parceiros por meio de APIs, por exemplo. Agora ele oferece sua própria API usada pelas companhias aéreas, mapas do Google, serviços de reserva de restaurantes e outros parceiros.

Digital ecosystems also enable service companies to reach new markets. Uber grew by orchestrating the digital expertise of many partners through APIs, for example. Now it offers its own API that is used by airlines, Google Maps, restaurant booking services, and other partners.

O modelo operacional do futuro

The transition to the service factory of the future will shift key pillars of the operating model. We predict that companies will need two distinct operating building blocks. One will be a “prediction engine” that collects and analyzes data to anticipate the needs of individual customers. (See Exhibit 3.) The prediction engine will also identify the actions required to meet those needs and recommend ways to bundle microservices into hyperpersonalized offerings. In addition, the prediction engine will gather and analyze data from legacy applications to sense, forecast, and display the status of operations in order to preempt outages and other service delivery interruptions.

The other operating building block will be an “assembly engine.” It will identify the service bits that must be assembled from an ecosystem repository of services. It will also draw from services in the company’s legacy applications. If the needed service bits are already available in the form of standardized, API-enabled microservices, highly automated service delivery will be straightforward, with little or no manual intervention required.

In most cases, however, an automated “smart” workflow will be needed to retrieve the right information and select the right combinations. We believe that many of today’s bionic services will increasingly be automated with the help of smart workflows combining process mining, machine learning, and other enabling technologies.

It will be critical that both the prediction and the assembly engines have continuous, real-time feedback loops to test and refine service solutions.

What Service Companies Can Do Now

The transformation to the service factory of the future—one that fits with a company’s strategic vision, market segment, and geographical footprint—will take years to realize. But many companies have already begun the transition. Business leaders can start by taking several actions now:

• Criar recursos de previsão de demanda em torno de casos de uso primeiro, como ofertas personalizadas de meselling e retenção, com base em conjuntos de dados estendidos e IA. APIs. entrega. Escala maneiras ágeis de trabalho, habilidades digitais e novos comportamentos de liderança que promovem a responsabilidade de ponta a ponta e uma abordagem de teste e aprendizado de alta frequência. Os vencedores da década à frente serão as empresas que embarcam na jornada agora.
• Rationalize your services portfolio and start creating a repository of modular service bits for the assembly and delivery of future individualized offerings—including those of ecosystem partners through APIs.
• Radically simplify and standardize end-to-end processes, such as lead to cash, in order to work efficiently across customer segments and the service portfolio.
• Build zero-touch workflows that pair individual service configurations on customer front ends with the corresponding delivery paths on the back end.
• Prepare your workforce for the bionic future of service delivery. Scale up agile ways of working, digital skills, and new leadership behaviors that promote end-to-end accountability and a high-frequency test-and-learn approach.

A new era in services is dawning. The winners in the decade ahead will be those companies that embark on the journey now.

Acknowledgments

The authors are grateful for the contributions of their colleagues Martin Stefan, Kunal Goel, and Tobias Lampe.