Senior Oracle Executive Advises Measuring AI by Results, Not by Number of Deployed Agents

According to a senior Oracle executive, enterprises implementing artificial intelligence (AI) should start by setting a measurable goal and judge success by the improvement the technology brings, rather than by the number of deployed AI agents. This is especially relevant as AI transitions from assisting employees to performing tasks on their behalf.

Natalie Reichelson, Vice President of Application Development at Oracle, told PTI that it is important to remember that the number of deployed agents matters less than the improvements they deliver. She advised business leaders to choose a measurable outcome, such as reducing the time spent correcting accounting errors or decreasing transportation costs.

First and foremost, a baseline must be established so that it can be assessed whether AI has a tangible impact. Next, companies need to properly set up the foundations: ensuring data reliability, clearly defining process ownership, having appropriate control mechanisms, and training employees on working with AI agents. The greatest progress is achieved in parts of the process where work is delayed or requires repeated manual intervention.

The most common misconception among business leaders is that AI is only intended to speed up existing tasks. Reichelson stressed that this is not the case; the true purpose of AI is to rethink the very way work is done. She gave an example: if a process that previously consisted of 10,000 steps is now reduced to five because AI performs all intermediate actions, that is redefining the business process.

Previously, AI tools were mainly used for answering questions, drafting content, and providing recommendations. Modern AI agents are capable of coordinating subsequent steps and performing authorized actions within a business process. For instance, a company aiming to increase its customer retention rate by 2 percent can set this goal, and a team of specialized AI agents can analyze the data, develop a plan, and implement it.

In a hospital system, AI could handle scheduling nurses based on their specializations and shifts. If a nurse falls ill, the system could restructure the schedule and notify those who are supposed to replace her. Quick results are likely to be achieved in routine processes where teams spend a lot of time gathering information and monitoring task completion. In the financial sector, AI agents can help reconcile accounts before closing the books, and in human resources, they can identify potential payroll issues. In supply chains, they can compare delivery options or determine how to allocate scarce materials, with plans undergoing review and approval by teams.

However, humans remain at the center of control, as Reichelson noted. Enterprises must clearly define which actions are permitted for the agent, how its work will be verified, and at what point human approval or intervention is required. Reichelson, who has worked at Oracle for about 15 years, believes that AI represents a more significant shift than the move to cloud computing, although it raises familiar questions. She compared it to the fears surrounding the Y2K bug at the end of the millennium, when 'the world seemed to be coming to an end, and then absolutely nothing happened.'

Similar stories

business-standard.comNovember 3, 16:39

WSO2 CEO asserts that enterprises must understand the behavior of AI agents in networks

WSO2 CEO Harry Ault stated that corporations need to gain a deeper understanding of how artificial intelligence (AI)-based systems behave and interact with all network components, rather than focusing solely on access to these networks for specific agents.

WSO2, an American company specializing in open-source software and founded in 2005, deals with software system integration, user identity security, and enterprise access.

According to Ault, although AI-based agents have been used for some time, most of them were applied in deterministic systems where security and management were built directly into the network, leaving agents with few opportunities to perform tasks outside established boundaries.

He noted that the shift to generative AI leads to more probabilistic scenarios where outcomes cannot be predicted, which poses a serious problem for production-level applications, and this transition is occurring across all enterprises.

To control such agents, companies must develop their own AI governance plans and create emulation gateways and authentication services, instead of using off-the-shelf solutions, thereby preserving intellectual property indefinitely.

Ault took over as CEO of WSO2 from founder and long-time leader Sandeep Iyeravaran, who retired in August of this year. Ault believes that all problems arising from AI, such as uncontrolled agent behavior or large language models generating responses irrelevant to their task, will be solved through 'open source and open standards,' allowing a larger number of people to collaborate on the same issue.

He added that the majority of revenue opportunities for almost all global technology and product companies come from AI-related demand, and this trend is likely to continue in the foreseeable future.

Speaking about growth, Ault emphasized the necessity of solving all existing AI issues before the company discusses budgets with other firms and offers them corresponding solutions.

He reported that in countries and regions such as the USA and India, the 'AI-first' approach has become standard for all companies. He specifically highlighted India, noting that the scale and value the technology can provide in that country may not be found in any other region.

The company, which serves about 950 clients in 92 countries worldwide, also collaborates with major government institutions, regulators, and financial organizations in India. Furthermore, he mentioned the strong development of founder-led product companies in India due to improvements in generative AI technologies, providing companies like WSO2 with many options for acquisition or merger with a parent company.

techcentral.co.zaNovember 3, 10:18

How to turn corporate AI ambitions into real business results with three key elements

Many companies face the challenge of implementing enterprise artificial intelligence (AI): they hire highly intelligent models capable of analyzing vast amounts of information and solving complex problems, yet these models often lack access to customer data, understanding of business processes, or the ability to act within company systems.

Despite pilot projects being conducted and budgets being approved, many executives wonder why powerful AI has not yet transformed workflows. The answer is that intelligence becomes a utility, while context, trust, and execution capability remain critically important.

BlueSky, as a partner of Anthropic, sees the solution in combining Anthropic's Claude intelligence with existing capabilities of Salesforce and Amazon Web Services (AWS). The company believes that this way organizations can move beyond experiments and start applying intelligence in practice.

The Power of Three Components

The first wave of enterprise AI focused on dialogue, where users asked questions and the AI provided answers. While useful, this did not always lead to transformative changes.

The next stage of development will be defined by the ability to execute. For example, when reviewing a loan application from a small or medium-sized enterprise (SME), a powerful AI model can analyze financial statements in minutes. However, if this model cannot access customer records, extract documents, trigger workflows, or interact with approval processes, the process will still take weeks.

The main problem for many organizations is not the intelligence itself, but connecting that intelligence to the systems where the work is actually performed. This is where the importance of the 'Power of Three' concept comes into play, represented by the analogy of 'brain, hands, and spine':

Brain — this is Anthropic, whose Claude model provides the necessary analytical ability, judgment, and reasoning to solve complex business problems.
Hands — this is Salesforce, because intelligence without context is merely an opinion. Salesforce provides customer data, workflows, and business processes, allowing the AI to understand what is happening and take meaningful action.
Spine — this is AWS, as enterprise AI requires a secure, scalable, and fault-tolerant environment to operate. AWS provides the infrastructure, management, and foundation necessary for large-scale AI deployment.

Each of these capabilities is strong on its own, but their combined use yields a much greater effect. They answer three key questions that every executive should ask about AI: Is it intelligent enough? Does it understand our business? Can we trust it to work at scale?

The Fourth Element

Nevertheless, there is a fourth component. The brain, hands, and spine need a nervous system to connect them, and this is the role BlueSky plays. The partnership with Anthropic for BlueSky is not the beginning of their journey in AI, but rather the next stage of years of work helping organizations modernize operations, improve customer experience, and unlock value through Salesforce, AWS, and data-driven transformation. Their task is to connect intelligence, enterprise context, and execution in ways that lead to measurable business results.

It is important to note that this does not mean replacing existing technology investments. On the contrary, organizations should view their Salesforce and AWS environments as the fundamental foundation that makes enterprise AI effective.

Opportunities Beyond Automation

In the long term, the possibilities are far broader than just automation. The world is moving toward a place where organizations will increasingly work side-by-side with digital workers, or what some call 'silicon employees.' These systems will not just answer questions; they will help analyze information, coordinate tasks, prepare recommendations, and advance work across the organization.

This marks a profound shift. AI is not replacing one human skill; instead, it is beginning to augment a very wide range of human capabilities. The leadership challenge will be mastering the ability to utilize intelligence that operates thousands of times faster than any single manager or team.

Organizations that succeed in the AI era will start not with a model, but with the work that needs to be done and the desired outcomes. They will rethink processes around intelligence, realize that execution is more important than conversation, and recognize that scaling AI is ultimately an organizational, not just a technological, challenge.

The winners of the AI era will not be those organizations that have access to the most models, as access to intelligence is becoming universal. Instead, the builders will win: organizations that connect intelligence to their data, systems, and people, transform ideas into actions, and turn intelligence into measurable business results. Any business can now utilize this smartest new employee. The question is whether you are ready to give it a desk.