Predictive AI offers tremendous potential – but it has a notoriously poor track record. Outside Big Tech and a handful of other leading companies, most initiatives fail to deploy, never realizing value. Why? Data professionals aren’t equipped to sell deployment to the business. The technical performance metrics they typically report on do not align with business goals – and mean nothing to decision makers.

For stakeholders and data scientists alike to plan, sell and greenlight predictive AI deployment, they must establish and maximize the value of each machine learning model in terms of business outcomes like profit, savings – or any KPI. Only by measuring value can the project actually pursue value. And only by getting business and data professionals onto the same value-oriented page can the initiative move forward and deploy.

Why Business Metrics Are So Rare for AI Projects

Given their importance, why are business metrics so rare? Research has shown that data scientists know better, but generally don’t abide: They rank business metrics as most important, but in practice focus more on technical metrics. Why do they usually skip past such a critical step – calculating the potential business value – much to the demise of their own projects?

That’s a damn good question.

The industry isn’t stuck in this rut for only psychological and cultural reasons – although those are contributing factors. After all, it’s gauche and so “on the nose” to talk money. Data professions feel compelled to stick with the traditional technical metrics that exercise and demonstrate their expertise. It’s not only that this makes them sound smarter – with jargon being a common way for any field to defend its own existence and salaries. There’s also a common but misguided belief that non-quants are incapable of truly understanding quantitative reports of predictive performance and would only be misled by reports meant to speak in their straightforward business language.

But if those were the only reasons, the “cultural inertia” would have succumbed years ago, given the enormous business win when ML models do successfully deploy.

The Credibility Challenge: Business Assumptions

Instead, the biggest reason is this: Any forecast of business value faces a credibility question because it must be based on certain assumptions. Estimating the value that a model would capture in deployment isn’t enough. The calculation has still got to prove its trustworthiness, because it depends on business factors that are subject to change or uncertainty, such as:

• The monetary loss for each false positive, such as when a model flags a legitimate transaction as fraudulent. With credit card transactions, for example, this can cost around $100.
• The monetary loss for each false negative, such as when a model fails to flag a fraudulent transaction. With credit card transactions, for example, this can cost the amount of the transaction.
• Factors that influence the above two costs. For example, with credit card fraud detection, the cost for each undetected fraudulent transaction might be lessened if the bank has fraud insurance or if the bank’s enforcement activities recoup some fraud losses downstream. In that case, the cost of each FN might be only 80% or 90% of the transaction size. That percentage has wiggle room when estimating a model’s deployed value.
• The decision boundary, that is, the percentage of cases to be targeted. For example, should the top 1.5% transactions that the model considers most likely to be fraudulent be blocked, or the top 2.5%? That percentage is the decision boundary (which in turn determines the decision threshold). Although this setting tends to receive little attention, it often makes a greater impact on project value than improvements to the model or data. Its setting is a business decision driven by business stakeholders, representing a fundamental that defines precisely how a model will be used in deployment. By turning this knob, the business can strike a balance in the tradeoff between a model’s primary bottom-line/monetary value and the number of false positives and false negatives, as well as other KPIs.

Establishing The Credibility of Forecasts Despite Uncertainty

The next step is to make an existential decision: Do you avoid forecasting the business value of ML value altogether? This would prevent the opening of a can of worms. Or do you recognize ML valuation as a challenge that must be addressed, given the dire need to calculate the potential upside of ML deployment in order to achieve it? If it isn’t already obvious, my vote is for the latter.

To address this credibility question and establish trust, the impact of uncertainty must be accounted for. Try out different values at the extreme ends of the uncertainty range. Interact in that way with the data and the reports. Find out how much the uncertainty matters and whether it must somehow be narrowed in order to establish a clear case for deployment. Only with insight and intuition into how much of a difference these factors make can your project establish a credible forecast of its potential business value – and thereby reliably achieve deployment.

Study of 300+ executives identifies misalignment between marketing and IT as key barrier to AI transformation

CHICAGO, Sept. 10, 2025 /PRNewswire/ — Bounteous, a leading global digital transformation consultancy, released a new white paper titled “The AI Whitespace: Addressing Challenges to Unlock Potential.” The report helps enterprises accelerate artificial intelligence (AI) adoption by identifying and closing organizational gaps between marketing and technology functions.

Based on a survey of more than 300 senior executives across North America and Europe, the report highlights key misalignments slowing AI adoption and provides a framework to help enterprises align, invest, and lead with AI. The full report is available here for enterprises looking to lead with AI.

With generative AI rapidly moving from experimentation to business-critical operations, Bounteous emphasizes that successful AI integration requires more than technical implementation; it demands a coordinated, company-wide transformation.

“Integrating AI across a business isn’t just a technology play; it’s an organizational shift,” said Martin Young, EVP, Data & AI at Bounteous. “To bridge the gap from early experimental wins to more impactful value across core business functions, organizations transform skillsets across their workforce.”

Young was recently appointed to lead the company’s global AI practice, reinforcing its commitment to helping clients scale AI initiatives responsibly and effectively. With more than 20 years of experience driving digital transformation, Young brings deep expertise in AI strategy, data governance, and enterprise change management.

“The AI Whitespace” provides C-level executives with practical strategies to assess AI maturity, identify organizational bottlenecks, and chart a path toward scalable, business-driven AI adoption.

Additionally, for the third time in a row, Bounteous was recognized as a Representative Vendor in the 2025 Gartner® Market Guide for Global Digital Marketing Agencies. The report noted, “Agencies are making significant investments in AI training and technology,” citing the Bounteous merger with Accolite Digital as an example.

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About Bounteous
Bounteous is a premier end-to-end digital transformation consultancy dedicated to partnering with ambitious brands to create digital solutions for today’s complex challenges and tomorrow’s opportunities. With uncompromising standards for technical and domain expertise, we deliver innovative and strategic solutions in Strategy, Analytics, Digital Engineering, Cloud, Data & AI, Experience Design, Digital Experience Platforms, and Marketing. Our Co-Innovation methodology is a unique engagement model designed to align interests and accelerate value creation. Our clients worldwide benefit from the skills and expertise of over 4,500+ expert team members across the Americas, APAC, and EMEA. By partnering with leading technology providers, we craft transformative digital experiences that enhance customer engagement and drive business success. Discover more about our impactful work and expertise by visiting www.bounteous.com and following us on X, LinkedIn, Facebook, and Instagram.

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SAN JOSE, Calif.–(BUSINESS WIRE)–Sep 10, 2025–

Today, Adobe (Nasdaq:ADBE) announced the general availability of AI agents that will reshape how businesses build, deliver and optimize customer experiences and marketing campaigns. Powered by the Adobe Experience Platform (AEP) Agent Orchestrator, Adobe is also creating an AI platform for businesses to manage and customize agents from Adobe and across third-party ecosystems—ensuring agents can understand context, plan multi-step actions, refine responses and more. AEP, used by many of the world’s leading businesses to connect real-time data across their organization, anchors Adobe’s offerings in a deep understanding of enterprise data, content and workflows. This provides the foundation for agents to take contextually relevant actions and deliver ROI.

This press release features multimedia. View the full release here: https://www.businesswire.com/news/home/20250910079322/en/

“Adobe has long helped businesses deliver engaging experiences to their customers, by turning digital data into actionable insights. We are now leveraging agentic AI to build specialized agents and embedding them into data, content and experience creation workflows,” said Anjul Bhambhri, senior vice president of engineering, Adobe Experience Cloud. “Our agentic AI innovations are elevating customer experience orchestration by reimagining processes, unlocking productivity for marketing teams and delivering personalized experiences at scale to drive growth.”

Over 70% of AEP customers are already using Adobe’s AI Assistant, which is the conversational interface that will enable teams to interact with agents across Adobe and third parties. Brands including The Hershey Company, Lenovo, Merkle, Wegmans Food Markets, Wilson Company and others have been working with Adobe’s agentic AI offerings to enhance capabilities within their organizations—and deliver impactful customer experiences.

Redefining Customer Experience Orchestration with AI agents

Adobe also announced the general availability of AEP Agent Orchestrator, including a reasoning engine where decision science and language models will enable dynamic and adaptive reasoning. This will ensure user intent can be interpreted from natural language prompts, to dynamically determine which agents are activated as part of an orchestrated plan. With AEP as the foundation, Agent Orchestrator will drive contextually relevant and goal-oriented automated actions, with support for refinement using a “human-in-the-loop” approach.

Out-of-the-box AI agents —which will be surfaced directly within category-leading Adobe enterprise applications such as Adobe Real-Time Customer Data Platform, Adobe Experience Manager, Adobe Journey Optimizer and Adobe Customer Journey Analytics—can be leveraged by businesses to enhance the skills of marketers and accelerate Customer Experience Orchestration (CXO). These AI agents include:

• Audience Agent will enable teams to quickly create, scale and optimize audiences that can be activated for personalization initiatives. High value audiences can be leveraged quickly through actionable recommendations and monitored closely to meet organizational goals and KPIs.

• Journey Agent in Journey Optimizer simplifies the creation and orchestration of customer journeys and campaigns across channels such as web, mobile, app, email and more. The agent will create journeys based on defined goals, optimize touchpoints based on aspects such as customer drop-off and uncover insights to fine-tune interactions.

• Experimentation Agent analyzes experimentation performance data, enabling teams to hypothesize new ideas for optimization efforts, analyze causal impact and identify predicted conversion or lift. It will be available in Journey Optimizer Experimentation Accelerator, for teams to easily turn experiments into AI-powered insights that drive growth.

• Data Insights Agent in Customer Journey Analytics (CJA) streamlines the process of deriving insights from signals across an organization, enabling any team to visualize, forecast and remediate customer experience initiatives. This agent is now available in CJA as well as across other Adobe enterprise applications.

• Site Optimization Agent delivers always-on support for teams to manage brand websites for high performance. The agent automatically detects and raises issues impacting customer engagement—such as broken back links or low performing pages—which can then be easily addressed via Adobe Sites Optimizer.

• Product Support Agent enhances the process of resolving issues for Adobe customers, leveraging a vast array of knowledge sources and organizational data to help users get the most value out of their enterprise applications. This now includes troubleshooting support—including case creation and tracking—directly in user workflows.

Unified experience for customizing and extending AI agents

Coming soon, Experience Platform Agent Composer provides a single interface for businesses to customize and configure AI agents based on brand guidelines, organizational policy controls and more. This will enable teams to fine-tune AI agent actions and shorten time-to-value. Additionally, new developer tools including an Agent SDK and Agent Registry will enable developers to build, extend and orchestrate agentic apps—expanding use cases into new industries and user personas.

As teams embrace agentic AI to augment daily work and drive better results, interoperability amongst AI agents in different ecosystems is critical. Agent Composer equips businesses with tools to drive multi-agent collaboration using the Agent2Agent protocol. This extends the value of agentic AI across more workflows, with customization capabilities that address specific needs. Adobe also announced new agentic AI partnerships with Cognizant, Google Cloud, Havas, Medallia, Omnicom, PwC and VML, enabling seamless execution of workflows across agents, as well customization across industries and use cases.

About Adobe

Adobe is changing the world through digital experiences. For more information, visit www.adobe.com.

© 2025 Adobe. All rights reserved. Adobe and the Adobe logo are either registered trademarks or trademarks of Adobe in the United States and/or other countries. All other trademarks are the property of their respective owners.

View source version on businesswire.com:https://www.businesswire.com/news/home/20250910079322/en/

CONTACT: Public relations contacts

Kevin Fu

Adobe

kfu@adobe.com

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SOURCE: Adobe

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