The SPEED Capabilities

Develops and tests hypotheses on priority value pools that are in line with an organization’s purpose

This is the capability that connects an organization’s purpose directly to the solutions it can create in customers’ lives. What is the product? Where is there a market for it? What is the business model to deliver it? Strategy consulting sets the goals and then partners with the other capabilities to reach them. Through pilots and testing, strategy stays involved in the process, determining the feasibility of building, scaling and taking new products or services to market.

As an AI-enabled transformation partner, we believe that strategy is the first critical step when integrating generative AI for digital transformations. Strategy examines new value opportunities, from identifying net new businesses to transforming existing businesses to driving efficiencies in current businesses. Our strategy capability helps identify the value drivers of generative  AI and models the revenue uplift opportunities, as well as the opportunity to create greater efficiencies.

Strategy is also where the right partners are identified, as partners are crucial to creating the most value for both the customer and the company. In these times, it’s nearly impossible for a business to hold all the capabilities it needs, but an ecosystem of the right partners gives access to customers, more insights, visibility, talent and capabilities.

Evolves new products and services at pace and scale

Product is the glue that brings all the capabilities together in service of the customer. Products are defined and designed collaboratively with the interests of the brand, interests of the consumer and continuous innovation baked in. Risks are tackled at the forefront, as the process is centered around solving problems—not just implementing features. For this reason, we utilize generative AI not just in the products we build but as a powerful tool in the product development workflow and within our product management process.

Product management flips project management on its head. Rather than using the three restraint factors of time, cost and scope, product management uses speed, quality and value. Product iterates, tests, learns and operates in continuous development. Every business is different, and the key is to find the delivery methodology that works for each business. The methodology has to drive value consistently, with quality, over and over again.

• Agile method: Consists of cross-functional teams that work in a way that’s inclusive of change and uncertainty in the development process
• Lean method: Optimizes and reduces waste so that teams, resources, energy and efforts can be more focused on creating the right outcomes. It’s about getting to the place where your organization’s capabilities are working concurrently and collaboratively
• DevOps method: Directly addresses organizational silos and blockers so companies can produce software faster with higher quality. It creates stronger collaboration between software development and IT ops

Creates value for customers

Across industries, the bar is raised every time a customer’s expectations are exceeded. Experience not only changes what a business produces but how fast it can produce and evolve it. Experience tries to create value throughout the entire customer journey by crossing organizational silos and bridging the gap between strategy and the rest of the SPEED capabilities.

Experience can’t just be focused on the front end, though, as everything that customers see and interact with is controlled or informed by internal functions. Customer experience design and functionality must be connected with the way those internal functions work. Today, data, data systems, AI and machine learning give experience designers the ability to move at a faster pace while using computation in their designs. We utilize Publicis Sapient -built AI toolsets and models with trainings and tools from partners like Adobe, Google, Microsoft and others to help accomplish this, along with a suite of methods and frameworks that allows us to quickly make AI tangible and useful for our clients.

Delivers on an organization’s promise, at pace and at scale

Engineering’s role is to figure out what's possible through technology and execute it. Then, it tests and refines based on customer needs. In the past, technology was primarily functional and used to drive efficiency. Now, generative AI and tech are used to solve problems and create value by connecting the back end with the front end.

A modern engineering capability is built upon three factors: architecture, ways of working and talent. In each, it requires a shift in focus to product and value. How does an organization get there? Through the three methodologies mentioned above: agile, lean and DevOps.

Engineers today have to be plugged into factors outside their field and more interactive with other capabilities than in the past. They need to be able to handle ambiguity in how various tech is applied to make a product or service work. They need to know the customer and have visibility into feedback loops. They need to work in multidisciplinary, autonomous teams, and they need to work towards shorter cycles and continuous delivery.

Validates hypotheses and finds insights for constant iteration

Knowing that all of the SPEED capabilities are enabled and supported by AI technology, data and AI systems are more than just assets. To reach a competitive advantage, these are a must-have, a non-negotiable. Data and AI feed every other SPEED capability area and enable high levels of computation. The holy grail is to create data and intuitive AI that can be used to optimize multiple business functions and offerings, not only to drive a better customer experience but also to innovate new products and services and to run operations more effectively.

There are four lenses through which companies should view data and AI:

• Utility: Getting useful data means obtaining the right data sets on the right audiences. With the right data, a business will have enough inputs to make predictive components of generative AI and machine learning valuable
  
  
• Data science and AI: The data science team partners with other capabilities to take the understanding of the customer, in any given use case, from conscious incompetence to conscious competence. Then, AI will take this to unconscious competence, creating a generative loop where each capability reinforces the other
  
  
• Designing for data: Great experiences both use and gather data, incorporating clean, accurate and discoverable data into the design process to create a mechanism that continues to feed the existing algorithms.
  
  
• Ethics: It’s crucial to understand the ethical implications of AI and data collection and use it only for a purpose the customer has allowed. Companies must be thoughtful in their approach and strive to maintain a people-centric strategy. It needs to be in line with the values of the company to preserve trust not only with customers but also with employees. The end goal is to create a clear exchange of value for the data they give access to