The company introduces AI, and the standard has shifted from "demonstration" to "integration and operational results."

Companies are moving from an “expectation” phase of artificial intelligence to a stage focused on actual results. Some analyses point out that the key to success now lies less in the model itself and more in whether it can naturally integrate into existing systems, as well as how to balance automated design with human roles.

Data and AI service company Quantiphi explained at the recent “Phi Moments @ Next” event that product performance alone is not enough to turn AI into tangible work results on the ground. Ishaan Aggarwal, head of the customer experience department at Quantiphi, stated that while software licenses can provide functionalities, ultimately it is the engineering partners who unlock their potential. Quantiphi emphasizes its advantage in accelerating development speed with “accelerators” and reducing contextual loss through orchestration layers that enable AI to work across multiple platforms.

Customer experience-centered collaboration is the starting point for AI integration

Quantiphi’s partnership with cloud contact center provider Five9 has garnered attention for its focus on “end-user experience” rather than just technical sales. Ray Dean, Vice President of the Five9 Cloud Marketplace Acceleration team, explained that clear goal setting and building consensus among teams enhance trust and execution.

The core is “seamless integration,” meaning adding AI capabilities without compromising customer experience. For enterprises, this means that rather than just adding new AI features, it is more important whether they can directly translate into results without disrupting existing workflows.

In Indonesia, “digital sovereignty” becomes a keyword for AI proliferation

Indosat Ooredoo Hutchison, an Indonesian telecom company, showcased a collaboration with Quantiphi that links AI to the country’s digital competitiveness. Harshini Infanta from Quantiphi explained that the focus is on combining cutting-edge AI infrastructure, local understanding, and engineering capabilities to create tangible changes that citizens can feel.

Vishal Gupta, Chief Technology Transformation and Procurement Officer at Indosat, commented that this transformation is not just a simple technology upgrade but a reorganization of the entire organization. This reaffirms that in AI transformation, senior leadership’s direction should precede frontline deployment.

Healthcare prioritizes patient outcomes over “profits”

In collaboration with Highmark Health, a subsidiary of Highmark, the patient-centered goal is prioritized. Dinesh Kabaleeswaran, North America sales leader at Quantiphi, stated that compared to short-term profitability, focusing on patient impact and actual medical outcomes is crucial.

Nik Acheson, Vice President of Data Strategy, Architecture, and Engineering at Highmark Health, explained that they aim to leverage data-driven insights and cross-industry experience to improve healthcare accessibility and anticipate patient needs. This indicates that healthcare AI is evolving from simple automation of consultations to a direction that simultaneously enhances service quality and operational efficiency.

In industry sites, balancing innovation and operational efficiency is key

In a case with Honeywell International ($HON), the emphasis was on balancing AI innovation with operational stability. Quantiphi enhanced innovation speed through AI capabilities, while Honeywell leveraged its experience in industrial automation and control to adapt to real-world operations.

Results are reflected in specific metrics such as improved asset performance and increased industrial process safety. Ankur Manake, Head of Data and AI at Honeywell Forge, stated that in complex industrial environments, such “North Star” metrics are the standard for judging AI success. This clearly indicates that the evaluation criteria for AI have shifted from flashy features to improving on-site KPIs.

Game industry focuses on user retention, not “ticket processing”

To minimize friction in user support, Helpshift, a customer support platform for the gaming industry, partnered with Quantiphi. Ram Kasi, head of Quantiphi’s GCP business in EMEA, explained that applying rapidly evolving AI technology stably in production relies heavily on contact center transformation experience.

Erik Ashby, Product Research Director at Helpshift, said the goal is not just to handle support tickets but to get users back to the game as quickly as possible. This shows that the performance metrics for AI agents are shifting from internal volume handling to actual user satisfaction and churn reduction.

Complex data migration also depends on “trusted partnerships”

The 219-year-old publishing company John Wiley & Sons collaborated with Quantiphi and Google Cloud to address data fragmentation. Mehul Trivedi, Vice President of Wiley’s Technology Group, said that migrating over 30k tables and 300TB of data, which would normally take two years, was shortened to 6 to 9 months.

However, the real challenge is not just cloud migration. Debopriyo Nag, head of data analytics at Quantiphi, pointed out that it is more important to contextualize decades of fragmented data into forms that AI can practically utilize. Ultimately, the competitiveness of enterprise AI depends less on model deployment and more on data organization and integration design.

AI market shifts from “demonstration” to “results validation”

These cases demonstrate that the enterprise AI market is no longer just about impressive demos. Only when AI addresses core industry issues—such as customer experience, digital sovereignty, patient outcomes, industrial safety, user retention, and data modernization—can its value be proven.

From the overall market perspective, companies are now more concerned with “how to connect, how to operate, and what results are produced” rather than AI itself. Compared to flashy technical demonstrations, “seamless integration” and measurable outcomes are becoming the new standards for AI adoption.