Imagine you are starting your workday as a sales representative. You’ve opened your customer dashboard and see that ABC, Inc. is at risk of churning. You pore through other metrics and dashboards. You’re frantically reviewing notes from recent meetings and have no indication of why this is the case. Should you call the customer? Do you send them recent product updates? What should you do next?
We’ve all experienced information overload. Often, more data doesn’t make decisions easier, just heavier. In our example, knowing a customer’s churn risk is not the same as knowing what to do about it. It is simply making for knee-jerk reactions that may or may not be the most logical resolution for the situation. Your organization has poured millions into predictive analytics, machine learning models, and business intelligence dashboards, but it can be difficult to make sense of it all.
This information debilitation isn’t reserved for sales organizations. Teams across all departments reach the same conclusion when inundated with information: “Now what?” That’s the information paradox. It isn’t a shortage of insights causing analysis paralysis in data consumers; it’s the disconnect between the insights and knowing the next steps to take given those insights.
We recently launched our Sales Next Best Action Agent at various clients to bridge the information-to-action gap for their respective sales organizations. As alluded to, however, this challenge extends far beyond sales teams to every corner of the enterprise, in every industry, from marketing campaigns that aren’t yielding the expected return to financial budget variances that need reconciliation.
In this blog, we’ll explore why predictive analytics, while foundational, is only one piece of the puzzle. We’ll examine how the gap between insight and action impacts different business units and introduce Next Best Action agents as the natural evolution moving from descriptive and predictive insights to prescriptive intelligence that drives measurable business outcomes.
What is Next Best Action Intelligence?
Next Best Action (NBA) is the ability to determine the most effective course of action to take in a given moment based on available information, contextual signals, and business objectives. While traditional analytics explains what happened and predictive models estimate what might happen next, Next Best Action focuses on what should be done now, an activity that seemingly relies on experience, intuition, and decision logic. Organizational leaders often possess extensive knowledge about customers, operations, their industry, and market conditions (which is why they are leaders!). In an ideal state, everyone within a business function would think like their top performers. With the emergence of multimodal AI, this is becoming a reality.
AI agents accelerate through this gap by moving beyond static recommendations and into active decision-making systems. Unlike rule-based engines or standalone models, agents can reason across the tribal knowledge of your organization, which is often buried in unstructured data, SaaS tools, and workflows, to select and execute actions, either autonomously or with human oversight. They continuously adapt based on outcomes and changing conditions, transforming Next Best Action from a point-in-time intuition into a living decision layer embedded directly into business operations.
The Limits of Traditional Analytics
The Dashboard Dilemma
Traditional business intelligence and machine learning have delivered enormous value by providing insights into describing results and predicting future events. Exposing these insights on dashboards assumes that every professional is equipped to interpret complex data visualizations and convert them into actionable steps, an assumption that rarely holds true in practice.
Dashboards also present data in isolation from the organizational knowledge and experience that give it meaning. A customer health score of 63.7 is only useful if you understand how top performers typically respond in similar situations, how that number is being calculated, which interventions have proven effective, and how to adapt those strategies to each unique context.
The Expertise Bottleneck
Every organization has top performers who appear to “just know” the right action to take. They have developed pattern recognition through experience, building mental models that guide their decisions in complex scenarios. However, this expertise often remains concentrated in a few individuals, creating a bottleneck that traditional analytics cannot address.
Scaling expertise across teams is one of the most expensive and overlooked challenges organizations face. When only a handful of sales reps know the right plays for specific customer scenarios, only a few marketers know how to turn around underperforming campaigns, or only experienced operations managers understand the best maintenance sequences, performance becomes inconsistent and difficult to replicate at scale.
Traditional analytics can highlight what top performers achieved, but they rarely capture how those outcomes were reached; the decision processes, contextual factors, and tactical nuances that made the difference. As a result, organizations continue to rely on a small number of experts while the broader team struggles to replicate high-performing behaviors.
The cost of learning through trial and error compounds quickly. Every suboptimal decision or missed opportunity represents not only immediate lost value but also the unrealized potential of better guidance. Traditional analytics, no matter how sophisticated, cannot fully bridge the gap between raw insight and the contextual intelligence required to act effectively.
Broader Adoption of Next Best Action Intelligence
The value of Next Best Action agents is not theoretical; it is evident in how work gets done across real teams and industries. The beauty of our intelligence platform is that it is consistent and modular in its architecture, but the impact looks very different depending on the business function and industry. By combining predictive analytics with organizational knowledge and contextual intelligence, in the form of unstructured data, these agents provide the missing link that turns overwhelming data into clear, actionable guidance. Below are a few areas within business functions and industry-specific use cases where we are seeing Next Best Action agents being used in practice.
Next Best Action Intelligence Across Business Functions
Marketing Next Best Action Agent
The Challenge: Marketing teams receive detailed campaign performance analytics showing click-through rates, conversion metrics, and audience engagement scores, but struggle with optimization decisions when campaigns underperform.
The Solution: A Marketing Next Best Action agent analyzes campaign data alongside historical performance patterns and successful optimization strategies used by top marketers. Rather than simply reporting that a B2B LinkedIn campaign is underperforming, the agent provides specific, contextual recommendations to address the issue.
Example in Action: “Your B2B LinkedIn campaign targeting IT directors has a 0.8% CTR, 40% below benchmark. Top marketers in similar situations typically allocate 30% of their budget to video content and adjust targeting to focus on decision-makers with ‘data transformation’ in their job titles. Based on your industry vertical, consider highlighting ROI case studies rather than feature comparisons. Would you like me to draft revised ad copy and suggest budget reallocation?”
Finance Next Best Action Agent
The Challenge: Finance teams excel at forecasting cash flow issues, identifying budget variances, and detecting financial anomalies, but translating these insights into specific actions requires the kind of contextual decision-making that separates experienced financial leaders from their junior counterparts.
The Solution: A Finance Next Best Action agent combines financial analytics with institutional knowledge about successful cost management strategies, investment priorities, and cash flow optimization tactics used by financial leadership.
Example in Action: “Q3 cash flow projections show a $2.3M shortfall due to delayed receivables and accelerated vendor payments. Historical analysis reveals that similar situations were resolved through a combination of renegotiated payment terms and strategic expense deferral. Top finance leaders typically prioritize extending key vendor terms by 15 days while deferring non-critical capital expenditures. Recommend contacting vendors A, B, and C to request payment term extensions and postpone the office renovation project. This approach has historically improved cash position by 18% within 30 days. Draft vendor communication templates and revised capex timeline attached.”
HR Next Best Action Agent
The Challenge: HR professionals have access to comprehensive employee engagement surveys and retention risk models, but translating insights, such as “Sarah’s engagement dropped 40%,” into effective retention strategies requires a contextual understanding of what works for different employee profiles.
The Solution: An HR Next Best Action agent analyzes engagement data alongside successful retention strategies, career development patterns, and individual employee preferences to recommend personalized interventions.
Example in Action: “Sarah’s engagement score dropped from 8.2 to 4.9 over the past quarter, primarily driven by ‘growth opportunity’ and ‘work challenge’ factors. Analysis of similar high-performing software engineers shows an 85% positive response to stretch project assignments and technical mentorship opportunities. Recommend offering the Innovation Lab project leadership role and pairing with Senior Architect Martinez for technical mentoring. Draft conversation guide and project proposal attached.”
Next Best Action Intelligence Across Industries
Healthcare: Clinical Decision Support
The Challenge: Healthcare organizations generate vast amounts of patient data through electronic health records, diagnostic imaging, and monitoring systems. While predictive models can identify patients at risk for readmission or complications, clinicians need specific guidance on treatment protocols and care coordination that align with clinical guidelines and institutional best practices.
The Solution: A Healthcare Next Best Action agent combines patient data with clinical guidelines, institutional best practices, and treatment outcome patterns to inform next best actions. When a patient’s risk score indicates potential complications, the agent provides specific interventions based on similar patient profiles and successful treatment protocols while ensuring HIPAA compliance and regulatory alignment.
Example in Action: “Patient shows early indicators of diabetic nephropathy based on lab trends. Similar patients respond well to adjustments in ACE inhibitors and consultation with a nutritionist within 48 hours. Dr. Johnson’s protocol for comparable cases shows 78% improvement in kidney function markers. Suggested care plan and specialist referral prepared.”
Financial Services: Risk Management and Regulatory Compliance
The Challenge: Financial institutions excel at risk scoring and fraud detection, but translating these insights into specific risk mitigation strategies while maintaining regulatory compliance requires sophisticated decision-making that balances business outcomes with regulatory requirements.
The Solution: A Financial Services Next Best Action agent combines risk analytics with regulatory requirements, institutional policies, and successful intervention strategies. When credit risk models flag potential defaults, the agent provides specific guidance on collection strategies, workout options, and regulatory reporting requirements.
Example in Action: “Commercial loan #4429 shows 73% default probability based on cash flow analysis and balance sheet changes. Regulatory guidelines require an immediate review of risk classification. Successful workout strategies for similar manufacturing clients include payment restructuring with asset-based collateral. Recommend scheduling a client meeting within 5 business days, preparing a workout proposal with an 18-month payment plan, and initiating enhanced monitoring protocols. Compliance documentation and meeting agenda attached.”
Manufacturing: Digital Twin Maintenance Intelligence
The Challenge: Operations teams benefit from sophisticated predictive maintenance models that forecast equipment failure with remarkable accuracy, but need specific guidance on maintenance sequences, resource allocation, and backup protocols.
The Solution: An Operations Next Best Action agent combines predictive maintenance alerts with institutional knowledge about successful intervention strategies, resource availability, and operational constraints.
Example in Action: “Equipment failure predicted for Line 3 in 72 hours based on vibration patterns and temperature anomalies. Recommended action sequence: Schedule maintenance team Alpha for Tuesday, 6 AM, order replacement bearings (Part #XY-4401) and hydraulic seals (Part #ZZ-9922), and prepare Line 2 for a 15% capacity increase. Historical data indicate that this intervention prevents 94% of predicted failures when implemented within this timeframe. Backup system activation protocols attached.”
Retail: Customer Experience and Inventory Optimization
The Challenge: Retail organizations collect extensive data on customer behavior, inventory metrics, and sales performance analytics, but require specific guidance on personalized engagement strategies and inventory optimization decisions that drive both customer satisfaction and business outcomes across all touchpoints.
The Solution: A Retail Next Best Action agent combines customer analytics with successful engagement strategies, inventory availability, and seasonal patterns to recommend specific actions. The agent integrates online and offline customer interactions, inventory systems, and fulfillment capabilities to optimize the entire customer experience.
Example in Action: “Customer segment ‘Frequent Buyers – Electronics’ shows 25% engagement decline over the past month. Historical data indicates this segment responds well to early access offers and technical product education. Current inventory levels support targeted promotion on smart home devices. Recommend a personalized email campaign highlighting new IoT products with a 15% early access discount. Campaign template and inventory allocation prepared.”
Building Effective Next Best Action Systems
Building a Next Best Action (NBA) system isn’t just about deploying a new tool; it’s about creating the decision-making core of your business, an adaptive, intelligence layer that continuously learns from your data, your people, and your operations. NBA systems work only when the organization invests in the foundations that make real-time decisioning possible: clean, connected data; curated semantic meaning; and iterative human–AI collaboration. When these elements come together, information becomes insight, insight becomes action, and action becomes a repeatable system that continually improves.
1. Good Data Foundation
An AI agent is only as good as the data it reasons upon. Before an agent can recommend a “next best action”, it requires a robust data foundation that can support AI at scale.
Unified Context: Agents cannot reason effectively across disconnected silos. You must integrate your data, both structured (operational metrics, financial records) and unstructured (internal documents, communication logs), into a unified intelligence platform.
Governance First: Every AI initiative must build upon—not bypass—fundamental data governance and quality frameworks. By keeping the agent native to your data platform, you ensure that security and governance are inherited from the source, rather than bolted on later.
2. Strong Semantic Curation and Metadata Management
Raw data, even well-modeled data, lacks the nuance required for AI to make recommendations. To bridge the Information Paradox, you must curate the semantic layer that gives that data meaning.
Harmonization: This involves creating semantic models that clearly define business logic, ensuring the agent understands not only that a metric has changed, but also what that change signifies in a business context.
Contextual Intelligence: By managing metadata and linking unstructured knowledge (such as playbooks or successful past interventions) to structured signals, you provide the agent with the business acumen typically reserved for top performers. This curation allows the system to determine why a specific action is the right one.
3. The Intelligence Engine: Agentic + Human Iterations
The goal is not to replace human decision-making but to augment it. The intelligence engine functions through a continuous loop of recommendation and refinement.
Agentic Workflows: Unlike passive dashboards, agents actively propose specific steps (e.g., “I recommend drafting a budget reconciliation plan” or “You could make the recommendation for a clinical intervention”) based on curated data and semantic context.
Human-in-the-Loop Learning: We design for agentic and human iteration cycles. When the agent makes a recommendation, the human reviews and refines it. This isn’t just a safety check; it is a learning mechanism. The system captures these human refinements to update its understanding, ensuring the platform continuously adapts and improves its “judgment” over time.
4. UX That Makes Sense
Adoption fails when tools are difficult to use. A successful Next Best Action system must integrate seamlessly into the user’s daily workflow, whether in Slack, Microsoft Teams, or a specialized portal, and support various interaction types. Let’s use our Sales NBA Agent as an example:
Proactive Nudges: The agent monitors data in the background and alerts the user when a specific threshold or pattern is detected:
Inactivity: “WidgeCo has not received any outreach in 30 days; you should share information on our latest features.”
Upcoming activity: “You have a meeting with Facility Inc. in 2 days. Have you incorporated their feedback on our feature gaps into your agenda?”
Alerts: “Churn risk for BobCo has increased by 30% based on their interest in our competitors.”
Ready-Made Prompts: Don’t make users rewrite the same asks they need on a daily basis. Build those into buttons for ready-made GenAI:
“Show me the sales call plan.”
“Create a meeting agenda.”
“What open orders do we have?”
“Any open opportunities?”
“Tell me about the assets.”
“Contacts with the customer.”
“Draft an email for the customer.”
Natural Language Querying: Allow the user to have the freedom to ask for custom actions and answers.
Your Next Best Action Implementation Roadmap
Now that we’ve outlined what goes into building an effective NBA system, including the data foundation, semantic curation, and human–agent intelligence loop, it’s time to explore how organizations actually bring this to life. Implementing an NBA capability isn’t a single project; it’s a staged journey that builds confidence, delivers early value, and creates the momentum needed for enterprise-wide adoption. The roadmap below outlines the process by which teams transition from identifying the right problem to deploying a living decision layer that becomes an integral part of everyday operations.
1. Strategy and Use Case Ideation
Every successful NBA journey begins by getting crisp on the real problems you’re trying to solve. This stage involves identifying where the “Information Paradox” is having the greatest impact, those moments when the business knows what’s happening but struggles to take action. It might be Sales fighting churn, Marketing missing targets, or Finance wrestling with budget drift. By focusing on one high-impact area and understanding the underlying process, organizations can quickly identify a use case that promises fast, visible value.
2. Prove Economic Value with a Quick Win
Once the use cases are defined, the next step is to pick a high-impact use case and prove the concept in a controlled environment. Teams build a minimal viable agent that blends predictive signals with a focused slice of organizational knowledge. The aim here isn’t to deploy the perfect system; it’s to show that an agent can meaningfully close the gap between insight and action. When a small team begins receiving clear, prescriptive recommendations they can actually use, buy-in follows naturally. Many of our customers start with our Sales Next Best Action agent because it’s a contained domain with measurable impact, making it an ideal proving ground for enterprise-wide NBA adoption.
3. Build the Full Intelligence Platform
After demonstrating value, attention shifts to building the broader architecture that supports a scalable intelligence platform. This is where the quick-win prototype evolves into a true decision layer that integrates with core workflows, data sources, and existing tools. The platform becomes capable of reasoning across unstructured knowledge, past interventions, and real-time signals. As it matures, it evolves into a living system that adapts to outcomes and continuously improves its own judgment.
4. Scale and Operate
With the foundation in place, organizations can begin expanding the NBA capability across additional functions and use cases. Scaling isn’t just about deploying more agents; it’s about establishing the processes for continuous learning, monitoring, and refinement. At this stage, the intelligence platform becomes an operational asset, spreading the intuition of top performers across the enterprise and driving consistent, measurable improvement.
Closing: Turning Information into Impact
We began this discussion with the Information Paradox: the frustrating reality where access to more data often leads to more difficult decisions rather than easier ones. While traditional analytics have excelled at telling us what happened or predicting what might happen, they often leave teams asking, “Now what?”
Next Best Action agents bridge the gap between raw insight and the contextual intelligence required to act. By combining predictive signals with the enterprise knowledge of your top performers, whether in Sales, Finance, HR, or Operations, these agents transform passive dashboards into active decision-making systems. They allow you to scale expertise across your organization, ensuring that every employee has the guidance they need to replicate the success of your best decision-makers.
Now that you have a roadmap for building these systems, from establishing a semantic data foundation to designing human-in-the-loop workflows, you are ready to move from theory to practice. Your next step is to identify that single, high-impact use case where your business currently suffers from “analysis paralysis” and define a quick win to prove the economic value of prescriptive intelligence.
Ready to take the next step?
Work with our specialists to turn passive analytics into active recommendations inside your daily tools. We’ll guide you from roadmap to a live NBA agent that your users actually adopt.
FAQs
How is a Next Best Action (NBA) agent different from a standard predictive model?
While predictive models are excellent at estimating future events (like a customer churn risk score), they do not inherently tell you how to resolve the issue. Next Best Action agents take that prediction and layer it with contextual signals and business objectives to determine the most effective course of action to take at the moment. Unlike a standalone model, an NBA agent can reason across unstructured data and workflow logic to actively propose specific steps.
Do I need to overhaul my entire data infrastructure before building an NBA agent?
Not necessarily. While a robust data foundation and unified context are essential for the system to reason effectively, you do not need to solve every data problem enterprise-wide to get started. The recommended approach is to identify a specific, high-impact use case and build a “minimum viable agent” that blends predictive signals with a focused slice of organizational knowledge. This allows you to prove value quickly before scaling the architecture to a full intelligence platform.
