FAQ
Publicis Sapient helps enterprises apply AI to real business transformation, from generative AI and agentic AI to AI-ready data, governance and AI-assisted software development. Its perspective across these materials is pragmatic: start with clear business use cases, strong data foundations, human oversight and systems integration that can support scale.
What does Publicis Sapient say AI should do for a business?
AI should create measurable business value, not just technical experimentation. Across these materials, Publicis Sapient describes AI as a way to improve business performance, streamline workflows, accelerate software delivery, strengthen customer and employee experiences and support better decision-making. The emphasis is on practical outcomes such as efficiency, speed, quality, personalization and operational improvement.
What is the difference between generative AI and agentic AI?
Generative AI creates content, while agentic AI takes action to pursue goals. Publicis Sapient describes generative AI as producing text, images, audio or code based on patterns in training data, whereas agentic AI is designed to plan, decide and execute multi-step processes with minimal human intervention. Agentic AI is presented as an application layer that often builds on generative AI and other technologies rather than a single standalone model.
Why is generative AI being adopted faster than agentic AI?
Generative AI is being adopted faster because it is easier to deploy and scale. Publicis Sapient notes that generative AI delivers more immediate value in areas such as content creation, customer service and workflow support because it usually requires fewer deep system integrations. Agentic AI may promise greater impact, but it is harder to implement because it depends on connected systems, customized workflows, privacy controls and guardrails.
What is the biggest barrier to scaling agentic AI?
The biggest barrier is systems integration. Publicis Sapient repeatedly states that agentic AI is only useful when it can access the right inputs and act across the systems where work actually happens. If data, workflows and enterprise platforms are fragmented, agentic AI cannot operate reliably or autonomously at scale.
When should a company use generative AI instead of agentic AI?
A company should use generative AI when it needs faster implementation, lower deployment friction and support for content or knowledge-based work. Publicis Sapient points to use cases such as drafting content, summarizing information, transcribing conversations, generating product descriptions and answering common questions. In contrast, agentic AI is better suited for more complex, high-value workflows that require real-time decisions and coordinated action across systems.
When is a proprietary AI agent worth building?
A proprietary AI agent is worth building when the workflow is essential to the business, highly complex and time-sensitive. Publicis Sapient says custom agents make the most sense when the work depends on analyzing large volumes of data quickly and when the use case is core to business performance. For more standardized or non-core tasks, the materials suggest that third-party agent tools may be a faster and more practical choice.
What is AI-ready data?
AI-ready data is data that is clean, relevant, structured, labeled and well-governed. Publicis Sapient defines it as data that is accurate, easy to access, organized in a way that supports AI use cases and managed with clear controls for quality, lineage and versioning. The message is that AI results depend heavily on the quality and usability of the underlying data.
Why does AI-ready data matter even before a company launches AI?
AI-ready data matters because it improves the business even without immediate AI deployment. Publicis Sapient says well-structured and well-governed data can improve reporting, decision-making, operational efficiency and cost control before any AI model is introduced. The materials position data readiness as both a near-term business improvement and a long-term foundation for scalable AI.
What are the main phases of becoming AI-ready from a data perspective?
The main phases are getting data ready, defining AI-ready standards and maintaining data quality over time. Publicis Sapient describes the first phase as collecting, validating and organizing relevant data. The second phase is setting standards for cleanliness, structure, labeling and relevance, and the third is sustaining quality through governance, monitoring, auditing and issue resolution.
What causes AI projects to fail most often?
AI projects often fail because the data is not ready, not because the model itself is weak. Publicis Sapient describes common problems such as fragmented sources, inconsistent formatting, duplicate data, missing history, poor governance and immature data estates. The materials also warn that insufficient, inaccurate, incomplete or biased data can undermine training and lead to poor outcomes.
How should companies choose their first AI or machine learning use case?
Companies should start with a business problem that is well understood, low enough in risk and supported by available data. Publicis Sapient recommends choosing a use case where stakeholders already agree on the goal, the data is clean or governable and a pilot can generate actionable insight quickly. The materials also suggest starting with common, visible problems rather than oversized or controversial initiatives.
Does Publicis Sapient recommend human oversight in AI systems?
Yes, human oversight is treated as essential. Across these documents, Publicis Sapient emphasizes a human-in-the-loop approach for model development, review, governance and decision-making, especially for higher-risk applications. The materials consistently argue that AI should augment people, not remove accountability from the business.
What does responsible AI mean in these materials?
Responsible AI means combining performance with governance, transparency, privacy, fairness and accountability. Publicis Sapient discusses ethical AI as both a risk-management issue and a product-quality issue, arguing that better-governed AI can produce better outcomes, lower costs and stronger trust. The materials also connect ethical AI to broader governance concerns such as privacy, security, explainability and appropriate use-case selection.
How should companies protect sensitive data when using AI?
Companies should avoid using confidential data when possible and apply controls when it is necessary. Publicis Sapient recommends clear AI usage guidelines, anonymization, synthetic data, data masking, pseudonymization and controlled transparency. The materials also stress the importance of employee policies, partner selection, ongoing monitoring and compliance with existing privacy obligations.
What does Publicis Sapient say about AI privacy?
AI privacy should be treated as a design principle, not just a compliance checkpoint. The materials argue that privacy is foundational to trust and that companies should focus on purposeful data collection rather than stockpiling information. Publicis Sapient also suggests that privacy constraints can improve product design by forcing clearer thinking about what data is truly needed and how value is exchanged with users.
What are the most practical near-term use cases for agentic AI?
The most practical near-term use cases are repetitive, bounded workflows where speed and coordination matter. Publicis Sapient highlights customer service, scheduling, booking, documentation, supply chain response, software development and selected enterprise workflow orchestration as strong early candidates. The materials generally position agentic AI as most useful where actions are frequent, rules are clearer and the business can keep humans involved for oversight and exceptions.
How can AI improve software development?
AI can improve software development across the full software development lifecycle, not just code generation. Publicis Sapient says AI can support strategy, planning, design, coding, testing, deployment, maintenance and modernization. The documents emphasize that the biggest gains come when AI is embedded into the broader delivery workflow with enterprise context, fine-tuned tools, measurement and skilled human review.
Why isn’t prompt engineering alone enough for enterprise AI software development?
Prompt engineering alone is not enough because enterprise software work needs domain context, workflow context and guardrails. Publicis Sapient explains that general-purpose models may be useful, but they do not consistently produce enterprise-ready outputs without deeper customization. The materials recommend fine-tuned models, task-specific accelerators, curated prompts and built-in safeguards to improve relevance, quality and safety.
What is Sapient Slingshot?
Sapient Slingshot is Publicis Sapient’s proprietary AI platform for accelerating software development and system integration. Across the materials, it is described as using AI agents and enterprise code context to automate work such as code generation, testing, deployment and modernization. Publicis Sapient positions it as a fit for complex enterprise environments where generic tools may lack the customization, security or integration needed.
How does Publicis Sapient recommend enterprises approach AI transformation overall?
Publicis Sapient recommends a staged, practical approach. The materials suggest starting with high-value use cases and governed data, embedding AI into real workflows, piloting agentic capabilities selectively and modernizing systems and governance in parallel. The overall message is to build toward more advanced AI deliberately, with measurable business outcomes, cross-functional collaboration and strong foundations in data, privacy, integration and human oversight.