What it Takes to Create Business Value with AI

Artificial intelligence has made experimentation significantly easier. Teams can now prototype ideas faster, accelerate development, and explore new possibilities with much less friction than before. But as AI capabilities advance, many enterprises still face a harder challenge: turning that speed into measurable business value at scale.

In a recent TAG Data Talk episode sponsored by Wavicle, host Shashank Honavar spoke with Hari Chidambaram about the realities of AI adoption, the conditions that often prevent AI initiatives from delivering value at scale, and what organizations must do to create sustainable impact from their investments.

Speaker details

• Host: Shashank Honavar – Managing Prinicpal, S-square D-square, LLC
• Guest Speaker: Hari Chidambaram – Global Head of Data and AI at Intuit

Here are five key takeaways from their conversation.

1. Business impact matters more than technical complexity

Many professionals focus heavily on mastering tools, models, and technical frameworks. While technical expertise remains important, the ability to connect data and AI initiatives to business outcomes is becoming increasingly valuable.

According to Hari, the core challenges organizations face are often remarkably similar across industries. Whether in transportation, banking, retail, payments, or technology, business leaders are generally trying to achieve one of three outcomes: increase revenue, optimize costs, or improve processes.

“Ultimately, you’re trying to impact the P&L — grow revenue, optimize cost, or improve process.”

Successful AI initiatives begin by understanding which of these outcomes matter most and how technology can contribute to them.

Rather than focusing solely on technical execution, professionals should develop a clear understanding of stakeholder priorities, customer needs, and organizational goals. AI delivers value when it helps solve important business problems tied to revenue, cost, process improvement, and customer outcomes, not simply when it demonstrates technical sophistication.

2. AI is making technical knowledge more accessible

The rise of AI is changing how organizations think about skills and expertise.

Historically, access to information and specialized knowledge created competitive advantages. Today, information is readily available, and AI is increasingly making knowledge more accessible through conversational interfaces, automated research, and intelligent assistance.

As technical capabilities become easier to acquire, differentiation shifts elsewhere.

Hari mention the professionals who stand out will be those who understand business context, think systematically, and can articulate clear solutions to complex challenges. Domain expertise, strategic thinking, and the ability to connect technology decisions to business outcomes are becoming critical differentiators.

“The differentiator would be understanding the problems you’re looking to solve, having a clear point of view, and thinking system-wise.”

AI may make knowledge easier to access, but organizations still need people who understand the problem, can think systemically, and have a clear point of view on what to solve and why.

3. Building AI prototypes has become easy. Scaling them has not

One of the most important observations from the discussion is the growing gap between experimentation and enterprise-scale adoption.

In the past, building a prototype required extensive planning, documentation, prioritization, and cross-functional coordination. Today, AI tools allow teams to create functional prototypes within minutes.

While this acceleration creates exciting opportunities, it can also create unrealistic expectations.

A successful prototype does not automatically translate into a successful enterprise solution. Organizations must still integrate with existing systems, address security requirements, manage operational complexity, and support ongoing governance.

Hari says many AI initiatives stall because leaders underestimate the effort required to move from proof of concept to production.

“Prototypes look really good — but do we have the prerequisites that need to be met, and what’s the fastest path to scale?”

The challenge is no longer just generating ideas or building quick prototypes. It is understanding what prerequisites must be in place, how those solutions will scale within real enterprise environments, and what talent and operating discipline are required to make them work.

4. Strong data foundations remain a prerequisite for AI success

Despite the excitement surrounding AI, the quality of outcomes remains heavily dependent on the quality of underlying data.

Organizations that lack trusted, well-governed data foundations often struggle to realize meaningful value from AI investments. Models can only perform as well as the information available to them.

Hari highlights several foundational capabilities that become increasingly important in an AI-driven environment, including master data management, customer data platforms, knowledge bases, and knowledge graphs.

“Think MDM, CDP, knowledge base, knowledge graph — you need real data understanding and foundations for AI to take advantage of it.”

These capabilities help organizations build the kind of data understanding and foundation AI depends on in order to generate useful, reliable outcomes.

Without these foundations, organizations risk amplifying existing data quality issues rather than solving them. AI may accelerate processes, but it cannot compensate for unreliable or fragmented data.

The principle remains unchanged: better data leads to better outcomes.

5. Governance is not optional

As AI becomes more embedded in business processes, governance becomes mandatory. Not just as oversight, but as an operational requirement for scaling AI responsibly.

Recent industry events have demonstrated that even organizations operating at the forefront of AI innovation can experience governance challenges. These incidents reinforce the importance of establishing clear controls, accountability mechanisms, and oversight processes.

Hari mentions effective AI governance extends beyond creating review committees or governance councils. It must be embedded directly into workflows, development processes, and operational practices.

“Organizations need to take a more holistic approach to AI governance… it can’t be just AI councils… AI governance needs to be embedded within workflows”

Organizations must embed governance directly into workflows and make clear decisions about evaluation, privacy-first design, and where human oversight must remain in place. They also need to think carefully about where automation is appropriate and where human judgment is still essential.

Trust takes years to build and moments to lose. As AI adoption accelerates, governance becomes one of the most important enablers of long-term success.

Measuring AI success beyond productivity

Many organizations begin their AI journey by focusing on productivity improvements, such as faster development cycles, accelerated content creation, or increased operational efficiency.

While these gains matter, they represent only one category of AI value.

“AI use cases fall into three broad buckets within an enterprise: productivity, speed of learning and decision-making, and customer benefit.”

A broader view of AI value includes three categories:

• First is productivity, where organizations improve speed of development, speed of deployment, and overall execution efficiency.
• Second is learning, experimentation, and decision-making, where AI helps teams test ideas faster, learn more quickly, and improve how decisions are made.
• Third is customer benefit, where AI improves time-to-value and helps organizations address more complex or adjacent customer pain points.

Looking at AI through all three categories creates a more complete view of value, one that connects productivity gains to better decisions and stronger customer outcomes.

Final takeaway

The future of AI will not be defined by how quickly organizations can build prototypes alone. It will be defined by how effectively they translate experimentation into measurable business impact.

Organizations that succeed with AI will combine strong data foundations, embedded governance, scalable execution, and a clear focus on business and customer outcomes. AI may make experimentation faster, but realizing enterprise value still requires the right foundations, the right decisions, and the ability to scale responsibly.

The hard part of AI is not simply building something quickly. It is scaling it inside real organizations and turning that innovation into lasting business value.

AI has made experimentation faster, but speed alone does not create value. Organizations still need strong data foundations, embedded governance, scalable execution, and a clear connection between AI initiatives and business outcomes. The hard part is no longer building a prototype. It is scaling AI responsibly inside real enterprise environments and turning innovation into lasting business value.

Watch the full podcast below:

AI may be making experimentation easier, but enterprise value still depends on strong foundations and disciplined execution. Wavicle helps organizations move from pilot to production with the data readiness, governance structure, and scalable delivery model required to make AI work in the real world. If you are looking to turn AI ambition into measurable business outcomes, get in touch today!

Disclaimer: Quotes in this blog are excerpted from a longer conversation and have been edited for length and clarity.