Why AI in Business Is About Execution, Not Tools
Most organizations have moved past the question of whether to adopt AI. McKinsey’s 2025 State of AI report found that 88% of organizations now use AI in at least one business function—up from 78% just one year earlier. In many ways, access to AI tools has become nearly universal.
What is less clear is how to turn those tools into meaningful results. The EY 2025 Work Reimagined Survey, covering 15,000 employees and 1,500 employers across 29 countries, found that while 88% of employees use AI at work, only 5% use it in ways that fundamentally transform how they work. Companies may be missing up to 40% of possible productivity gains due to gaps in training, integration, and organizational readiness.
This gap is not just a technology problem; it is an execution problem. Knowing how to use AI in everyday business work is a different skill than building AI systems or selecting tools.
Access to AI is no longer the differentiator. The ability to embed AI into workflows, govern it responsibly, and sustain its performance over time is what separates organizations that capture real value from those that stay in pilot purgatory.
Why AI Adoption Breaks Down in Real Organizations
In its “2025 survey of AI project adoption,” MIT examined 300 public AI deployments and found that 95% of enterprise generative AI pilots show no measurable impact on profit or loss. The researchers pointed to a clear reason: the issue is not the technology itself, but whether organizations are prepared to use it effectively.
In many cases, organizations were trying to fit AI tools into existing workflows without changing how the work actually gets done—what the study calls the ‘learning gap.’ At the individual level, employees are already using AI productively—to accelerate a task, draft a document, or analyze data. However, individual productivity gains do not automatically translate across an entire organization.
For AI to create real impact, organizations need to make clear decisions about how it’s used. This includes deciding which AI initiatives deserve investment, how AI outputs should flow into business processes, who is responsible for results, and how performance is tracked over time. This is the actual work of AI in business—and it is what most organizations are missing.
The Myth That AI Success Is About Better Tools
When AI initiatives stall, the instinct is often to look for a better model. However, research consistently shows that the biggest barriers to the effective use of AI in business are organizational: unclear ownership, limited coordination across teams, weak connections to everyday workflows, and lack of accountability for outcomes.
EY Americas Cultural Insights Leader Marcie Merriman has noted that employee trust in AI is ‘not just about safety and security—it’s whether the technology works’ in the environments where people are expected to use it. Without proper training, clear workflows, and defined responsibilities, it becomes difficult for teams to use AI effectively in their day-to-day work.
Why Pilots Rarely Translate Into Scaled Impact
AI pilots often succeed because they are set up under ideal conditions. They typically use clean data, focus on a narrow problem, involve a small and motivated team, and operate within a short timeline. However, those conditions don’t hold at a larger scale.
As AI expands across an organization, it needs to fit into the right workflows. Outputs must reach the right people at the right time, and teams need clear guidance on how to use them. This includes understanding when to trust the system, when to override it, and who owns the outcome when things go wrong.
Performance should also be monitored continuously, especially as business conditions evolve. Building and maintaining these conditions is the real work of AI in business—and it is where many organizations struggle
AI Changes Workflows and Decision-Making, Not Just Outputs
When organizations introduce AI into their operations, they are not just adding a faster tool to an existing process. How AI is used in business determines how decisions get made, which people or teams are responsible for outcomes, and how work flows across functions. Understanding and managing those changes—before deployment, not after—often determines whether an AI initiative delivers lasting value.
Klarna’s experience illustrates what the intentional use of AI in business looks like when done well. In Q1 2024, the company reduced its sales and marketing spend by 11% while running more campaigns and producing more creative assets, with AI accounting for 37% of total cost savings. This result did not come from simply deploying a better model; it came from redesigning the workflows around image production, copywriting, and agency management so AI could be integrated consistently into daily operations.
Similar patterns are emerging in other industries. In logistics, predictive AI models are rapidly improving efficiency, and individual marketers are saving anywhere from 10 to 13 hours per week by using AI tools.
How AI Alters Roles, Handoffs, and Accountability
When AI becomes part of a workflow, existing roles and responsibilities shift in ways that are not always obvious. For example, a model that recommends a course of action may change who is effectively making the decision. If organizations do not clearly establish who reviews AI outputs, who can override them, and who is ultimately responsible for the outcome, accountability gaps can form.
In high-stakes environments—such as healthcare, finance, and legal services—those gaps create both compliance and performance risks. Organizations that manage the use of AI in business effectively tend to define accountability structures before deployment, not in response to problems that have already occurred.
The Risk of Blurred Decision Rights
One common pattern in AI adoption is automation bias: the tendency to accept AI outputs without closely reviewing them.. When decision rights are not explicitly defined, automation bias compounds over time. Errors that would have been caught by human review go undetected, trust in the system erodes, and accountability for outcomes becomes unclear.
The solution is not to reduce the use of AI, but to put clearer structures in place. This includes defining ownership for AI-assisted decisions, setting clear processes for when to involve human review, and creating oversight processes that make accountability visible.
Why Execution Matters More Than AI Capability
The organizations capturing the most value from AI are not necessarily those with the most sophisticated models. McKinsey’s 2025 data shows that roughly 6% of organizations report significant value from AI, with at least 5% of their operating budget tied to its use.—These organizations stand out not because of the tools they use, but because of how they operate. This includes redesigning workflows for AI, setting performance targets beyond simple efficiency gains, and actively managing AI-enabled systems over time.
What separates high performers from the rest is execution capability. This means having the structure and discipline to make the use of AI in business work consistently, at scale, and in ways that hold up over time.
From Insight to Action Inside Real Business Processes
AI value comes from embedding insights into the moments where decisions are actually made—in the right format, at the right time, and with clear next steps. A model may produce accurate predictions, but if it delivers them in a way that does not match how decisions flow through the organization, they are unlikely to make an impact. On the other hand, when AI is built into existing workflows, it becomes much easier to act on those insights.
For this reason, workflow design is not a secondary concern in AI in business implementation. Rather, it is one of the main ways that AI produces business impact—and it requires strong organizational and process understanding, not just technical expertise.
Measuring What Works and Course Correcting Over Time
AI systems are not static. Data shifts over time, user behavior changes, and business conditions evolve in ways that can cause model performance to degrade—a challenge often referred to as data drift or concept drift. A model performing well at launch can quietly become less reliable over months, producing outputs that are increasingly misaligned with the real world.
Organizations that sustain the effective use of AI in business plan for this from the start. They design measurement systems that track business-relevant outcomes, not just technical metrics. They also build feedback loops to catch issues early and create clear processes for reviewing and updating models as conditions change.
Governance Is the Missing Link in AI at Scale
The Diligent Institute Q4 2025 GC Risk Index found that 60% of legal, compliance, and audit leaders now cite technology as their top risk concern—well ahead of the economy or tariffs. Yet only 29% of organizations have a comprehensive AI governance plan in place. This gap between recognizing risk and being prepared to manage it is one of the most significant organizational vulnerabilities in AI in business today.
Governance that arrives after deployment does not build trust; it manages damage. The organizations that scale AI responsibly treat governance as part of the design process. It’s built into workflows, accountability structures, and operating models from the start, not added when problems make it unavoidable.
Accountability When AI Influences Outcomes
Effective governance starts with a simple question: when AI influences a decision, who is responsible for the outcome? That question has to be answered before the system goes live. Governance frameworks define accountability thresholds, outline escalation paths, and set expectations for oversight.—They are most effective when they are built into the operating model, rather than added afterward as a separate compliance layer.
Designing AI Systems That Hold Up Over Time
Governance is not a one-time activity. As AI systems scale, they encounter new data, shifting business conditions, and evolving regulations. Governance frameworks must evolve alongside them. The organizations that sustain AI performance build monitoring, feedback, and escalation systems that improve over time, treating governance as an ongoing management practice rather than a fixed set of rules established at deployment.
An AI business degree can help professionals develop the skills needed to monitor performance drift and adjust models to evolving business priorities and market changes.
A Business Problem First Approach to AI Execution
The most reliable path to AI value starts with a clearly defined business problem. What outcome needs to improve? What constraints matter most? What does success actually look like in operational terms, before any AI solution is considered?
That discipline—starting with the problem, not the tool—is what separates organizations that make AI work from those that accumulate expensive, inconclusive pilots. It also reflects the focus of many in-demand AI leadership roles: professionals who can connect business goals with technical possibilities, design the operational structures that allow AI to perform at scale, and govern that performance over time. Learning this is what a strong AI business degree is fundamentally about.
Using AI Only When It Improves Results
Not every business problem benefits from AI. Some processes are better served by simpler, more reliable approaches. In other cases, AI may require a level of data quality or organizational readiness that is not yet in place.
The ability to evaluate whether AI will meaningfully improve a specific situation—rather than using it simply because it is available—is one of the clearest markers of mature AI leadership. Organizations that apply this discipline consistently avoid the expensive failures that erode confidence in AI across the enterprise.
Redesigning Operating Models Around AI Capabilities
When AI is integrated into the operating model deliberately—with workflows redesigned to support it, roles clearly defined, and governance built from the start—the results are very different from simply adding AI to existing processes.
Redesigning operating models around AI capabilities requires professionals who understand how businesses operate and how to lead complex change. They also need enough technical literacy to engage credibly with the systems they are governing.
How the MS in AI in Business Prepares Leaders for Execution
BU’s Online MS in AI in Business, offered through the Questrom School of Business, is built around common execution challenges. This master’s in business with an AI focus begins with business problems—performance, risk, efficiency, innovation—and develops the skills to design, implement, and govern the AI capabilities that address them.
The program is organized into four integrated modules—foundations, improvement, innovation, and governance—which build skills across the full cycle of AI leadership. Students develop practical playbooks that they can apply within their own organizations while enrolled. Learning is guided by live sessions with Questrom faculty and a cross-functional peer cohort from across business, operations, product, and finance functions.
Learning to Lead AI-Enabled Improvement and Innovation
The Online MS in AI in Business degree focuses on the skills you’ll need to drive both improvement and innovation in business. Human-AI integration is a major focus for aspiring business leaders, with emphasis on continuous improvement and creating new value for businesses.
Building Playbooks for Responsible AI at Scale
From the foundations of responsible AI application to governance frameworks that support long-term performance, the program builds the practical approaches that allow graduates to lead AI initiatives in any organizational context. The curriculum is fully online and designed for working professionals, with a 32-credit structure that supports completion in approximately 16 months. Students do not wait for graduation to apply what they are learning—they bring it directly into their organizations throughout the program.
Learn More About Earning a Graduate AI Business Degree at BU
Interested in advancing your expertise in AI in business? You can learn about program requirements, attend an enrollment event for the Online Master’s in AI in Business, or set up an advising session to determine whether the program matches your goals.
