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Transform Your Business with AI Agents: Evolve from Basic Automation to Strategic Enterprise Intelligence
Struggling to Understand What AI Agents Really Do and How They Create Value?
You’re not alone. Most organizations are still trying to separate the reality from the hype. Join Mike Gualtieri, Forrester VP & Principal Analyst, and Jeff Evernham, Sinequa Chief Product Officer, for a practical, no-nonsense session: “FROM AI INTERNS TO AI EXPERTS: 5 STRATEGIES FOR REAL BUSINESS TRANSFORMATION”
In just 45 minutes, you’ll learn how leading companies are:
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Evolving simple AI tools into intelligent business partners capable of nuanced decision-making
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Embedding institutional knowledge and domain expertise directly into AI agents
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Turning underused data into actionable intelligence that drives measurable impact
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Deploying AI agents that understand your business context, workflows, and constraints
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Gaining a competitive edge with AI systems that augment your teams rather than replace them
If you’re aiming to operationalize AI and deliver real results, not just experiments, this session is for you.
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Session Content: 5 Strategies for Real Business Transformation
The session is organized around five practical strategies drawn from Forrester’s enterprise AI research and Sinequa’s production deployment experience. Together they form a roadmap from where most organizations are today — with AI tools that handle narrow, well-defined tasks — to where the organizations generating the most AI value are operating: with AI systems that understand business context, embed institutional knowledge, and act as genuine partners in complex decision-making.
Strategy 1: Evolve from AI Interns to AI Experts
The “AI intern to AI expert” spectrum is one of Forrester’s most useful frameworks for enterprise AI maturity. An AI intern handles well-structured tasks with clear instructions and limited scope — answering FAQs, classifying documents, routing requests. An AI expert synthesizes complex information from across the organization, understands the business context behind a question, and provides judgment-level analysis that experienced human professionals would recognize as genuinely useful. The session explains what the architecture, data quality, and governance requirements are at each point on this spectrum, and why most organizations are stuck at the intern level not because of model limitations but because of retrieval, context, and governance gaps that better AI models alone cannot fix.
Strategy 2: Embed Institutional Knowledge Directly into AI Agents
The difference between an AI agent that generates plausible responses and one that generates accurate, actionable responses for a specific enterprise context is the quality of the knowledge it retrieves. Generic training data does not contain the organization’s operational procedures, proprietary research, historical decisions, customer context, or domain expertise. The session covers what it takes to build AI agents that retrieve from the organization’s actual knowledge environment — and how enterprise RAG architecture determines whether an AI agent produces outputs a senior professional would trust or outputs that require manual verification before use.
Strategy 3: Turn Underused Enterprise Data into Actionable Intelligence
Most enterprises have more relevant data than their AI deployments actually reach. Documents exist in repositories that are not indexed, in formats that are not parsed, in systems that are not connected, in languages that are not processed. The session addresses the data readiness requirements for enterprise AI that moves beyond simple document retrieval — covering multi-source data unification, structured and unstructured data synthesis, and the connector architecture that determines how much of the organization’s actual knowledge is available to AI agents at the moment they need it.
Strategy 4: Deploy AI That Understands Business Context, Workflows, and Constraints
An AI agent that does not understand the constraints of the environment it is operating in is not a business asset — it is a liability. Business context includes the regulatory requirements the organization must meet, the access control boundaries that determine who can see what, the workflow dependencies that determine what order tasks must be completed in, and the escalation logic that determines when an agent should act autonomously and when it should surface a decision to a human. The session covers how to build this context into enterprise AI deployments and why the organizations that do it correctly are the ones achieving the jump from AI tool to AI business partner.
Strategy 5: Augment Teams Strategically — Build the Right Human-AI Collaboration Model
The most effective enterprise AI deployments are not the ones that automate the most tasks. They are the ones that direct AI capability toward the work where it creates the most value — freeing human capacity for judgment, relationships, and the complex problem-solving that AI augments rather than replaces. The session provides a framework for identifying which tasks and workflows are the highest-value candidates for AI augmentation versus automation, and how to design the human-AI collaboration model that makes AI a productivity multiplier rather than a source of new workflow complexity.
Who Should Watch
- CIOs and CTOs responsible for enterprise AI platform strategy and accountable for moving AI programs from pilot credibility to operational impact
- Heads of Digital Transformation and AI Programs who have delivered pilots and are now navigating the harder challenge of scaling AI across the organization without creating governance risk
- Enterprise architects designing the AI infrastructure layer and needing Forrester’s research perspective alongside the technical architecture requirements
- Business unit leaders evaluating AI investments for their function and needing a framework for identifying which use cases to prioritize and in what sequence
Frequently Asked Question
One of the persistent problems with enterprise AI programs is the measurement gap: organizations track AI usage metrics (queries processed, documents summarized, tasks completed) without connecting them to the business outcomes that justified the investment. Forrester’s research on enterprise AI ROI identifies a consistent pattern in the organizations generating the strongest returns: they defined outcome metrics before deployment, not after. This means identifying the specific business result each AI use case is meant to improve — analyst hours per investment research report, days to complete a due diligence cycle, time from regulatory change publication to compliance assessment — and measuring AI impact against that baseline. The session covers how to apply this measurement framework to enterprise AI deployments and why organizations that skip this step tend to find themselves in a second AI pilot cycle rather than a scaling deployment.
The governance requirements for enterprise AI agents operating on business-critical workflows are substantively different from the governance requirements for AI tools used on low-stakes tasks. When AI agents assist with consequential decisions — investment recommendations, compliance assessments, clinical research synthesis, engineering safety analysis — organizations need answers to four governance questions before deployment: Who is accountable when an AI-assisted output is wrong? How are AI outputs traced back to their source data for audit purposes? What escalation path exists when an AI agent encounters a scenario outside its operating parameters? And how is the AI system’s continued accuracy monitored after deployment, as data environments and business contexts change? The session addresses these governance requirements from both Forrester’s research perspective on what leading organizations are doing and Sinequa’s production architecture experience with the systems and controls that make governed AI deployment operationally sustainable.
The technical requirements for enterprise AI at scale are well-defined. The organizational requirements are less discussed and frequently underestimated. Forrester’s research consistently identifies three organizational factors that determine whether AI programs scale or stall. First, executive sponsorship that is outcome-defined rather than technology-defined: sponsors who are accountable for the business result, not just the deployment. Second, cross-functional AI governance that brings IT, business units, legal, compliance, and risk into AI deployment decisions early, rather than treating them as blockers to navigate after the technical work is done. Third, a workforce model that treats AI capability as a new skill category requiring deliberate development — not an add-on to existing roles that workers are expected to absorb without training or workflow redesign. The session covers how to address each of these organizational requirements in parallel with the technical deployment work, and why organizations that treat them as afterthoughts tend to find that technically sound AI deployments generate disappointing adoption and business impact.
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