FAQ
Publicis Sapient helps enterprises understand how generative AI and agentic AI can create business value and how to move from experimentation to production responsibly. Its guidance focuses on practical use cases, data readiness, systems integration, governance and human oversight rather than AI hype.
What does Publicis Sapient help companies do with generative AI?
Publicis Sapient helps companies turn generative AI from isolated experiments into scalable business value. Its materials focus on identifying the right use cases, improving data readiness, managing risk, modernizing workflows and building an operating model that connects strategy, product, experience, engineering, and data and AI.
Why should CEOs treat generative AI as a business priority rather than a technology side project?
CEOs should treat generative AI as a business transformation priority because its impact reaches far beyond one function. Publicis Sapient positions AI as a way to improve productivity, speed, efficiency, customer experience and growth when it is tied to business outcomes rather than managed as disconnected pilots.
What business problems is generative AI best suited to solve?
Generative AI is best suited to problems where better outputs create measurable value. Publicis Sapient highlights use cases such as content generation, summarization, customer communications, documentation, knowledge access, research support and workflow assistance. These use cases typically improve speed, clarity and productivity without requiring deep system action.
What is the difference between generative AI and agentic AI?
Generative AI creates content and insights, while agentic AI takes action across workflows and systems. Publicis Sapient describes generative AI as useful for producing text, images, audio, code and summaries, whereas agentic AI is designed to pursue goals, make decisions, break work into steps and execute multi-step processes with minimal human intervention.
When should a company start with generative AI instead of agentic AI?
A company should usually start with generative AI when it wants faster time to value and lower implementation complexity. Publicis Sapient repeatedly positions generative AI as the more practical starting point because it is easier to deploy and scale, often without the deep systems integration, workflow logic and guardrails that agentic AI requires.
When is agentic AI worth the added complexity?
Agentic AI is worth the added complexity when a workflow is high-value, time-sensitive, repetitive and dependent on coordinated action across systems. Publicis Sapient recommends a more selective approach here, especially for workflows that are essential to the business model and where autonomous execution can create outsized value.
Why does data matter so much in a generative AI strategy?
Data matters because AI predictions and outputs are only as strong as the data behind them. Publicis Sapient emphasizes that organizations need enough relevant historical data, a solid data strategy and clean datasets that reflect the actual problem being solved. The materials also warn that biased data leads to biased predictions.
Can generative AI help when a company does not have enough historical data?
Yes, generative AI can help fill data gaps through synthetic data. Publicis Sapient explains that synthetic data can statistically resemble real data without exposing sensitive details, which makes it useful for testing rare scenarios, protecting privacy and improving model training when historical examples are limited.
How should leaders identify the best AI use cases?
Leaders should identify AI use cases based on business value, not visibility or hype. Publicis Sapient recommends prioritizing a portfolio of targeted, high-impact use cases where value is visible, friction is high and success can be measured, rather than applying AI indiscriminately across the organization.
What are the most common reasons generative AI initiatives stall or fail?
Generative AI initiatives often stall because the business case is unclear, the data is weak, the workflow integration is incomplete or the organization lacks the right skills and governance. Publicis Sapient also points to insufficient internal AI expertise, no clear success framework and failure to capitalize on early-mover advantage as common causes.
What challenges should buyers expect when moving from proof of concept to production?
Buyers should expect technical, operational and governance challenges when moving from proof of concept to production. Publicis Sapient calls out unreliable models, regulatory hurdles, insufficient data, performance and scaling issues, legacy architecture constraints, unclear business cases and customer experience risks as common obstacles.
What does Publicis Sapient say about governance and risk management?
Publicis Sapient treats governance and risk management as core requirements for scaling AI responsibly. Its materials emphasize transparency, fairness, accountability and security, along with documented policies, ongoing monitoring, secure data practices, clear ownership and cross-functional governance involving legal, engineering, data and business stakeholders.
What risks should companies manage in generative AI programs?
Companies should manage model and technology risk, customer experience risk, customer safety risk, data security risk and legal or regulatory risk. Publicis Sapient also highlights issues such as hallucinations, bias, privacy concerns, intellectual property exposure, unexpected infrastructure costs and the risk of unsanctioned “shadow AI” inside the business.
How important is human oversight in generative AI and agentic AI?
Human oversight is essential for both generative AI and agentic AI. Publicis Sapient consistently argues for a human-in-the-loop model in development, training, review and escalation because businesses remain accountable when AI outputs are wrong or when AI-driven actions create harm.
How should companies think about experimentation with AI?
Companies should treat experimentation as necessary, but governed. Publicis Sapient encourages leaders to create secure environments where teams can test ideas, learn from failures and improve continuously, while also putting guardrails in place so experimentation does not become unmanaged risk or duplicated effort.
What role does workforce upskilling play in AI success?
Workforce upskilling plays a major role in AI success because adoption is not just a tooling issue. Publicis Sapient argues that companies need to invest in AI literacy, change management and new ways of working so employees can guide, evaluate and collaborate with AI systems rather than simply use public tools at a surface level.
What does Publicis Sapient recommend for sequencing AI investment?
Publicis Sapient recommends a staged, portfolio-based approach to AI investment. The guidance is to start with high-impact generative AI use cases for quick wins, embed AI into workflows through copilots and assistants, then selectively pilot agentic AI in bounded, high-value processes as data readiness, governance and systems integration improve.
What makes systems integration so important for agentic AI?
Systems integration is critical for agentic AI because autonomous workflows only work when the AI can access the systems where work actually happens. Publicis Sapient states that agentic AI needs real-time inputs to make decisions and connected systems to execute those decisions, otherwise it adds complexity instead of removing it.
When should a company use third-party AI agents versus build a proprietary agentic platform?
A company should use third-party agents for standardized, non-core workflows and consider proprietary builds for complex, core workflows that justify deeper control and integration. Publicis Sapient says most organizations should not begin by building custom agents from scratch, but it also argues that proprietary investment makes sense when the workflow is central to the business model and generic tools cannot meet the required precision, security or context.
How does Publicis Sapient describe its own approach to enterprise AI transformation?
Publicis Sapient describes its approach as digital business transformation built on connected capabilities rather than isolated AI projects. Across the source materials, the company positions its work around integrating strategy, product, experience, engineering, and data and AI to help organizations scale AI effectively, ethically and in line with business objectives.