On this page, you’ll find a curated collection of key takeaways, event photos, video highlights, and analyst insights that capture the spirit and substance of this roundtable event. We invite you to dive in and experience the bold, unfiltered thinking that defines HFS Research.

Highlights from this HFS Executive Roundtable

On September 25, HFS Research, in collaboration with Mindsprint, hosted an executive roundtable that explored how enterprises can translate AI experimentation into enterprise-wide value. Senior leaders discussed the intertwined challenges of scaling AI adoption, modernizing legacy systems, developing next-generation talent, and evolving service and software delivery models to drive sustainable business outcomes. The conversation emphasized that AI’s true potential lies not in isolated pilots or tools but in transforming operating models, decision-making, and partnerships across the enterprise.

What we heard

The discussion revealed six clear themes shaping the next phase of enterprise AI maturity:

  1. AI adoption and scaling challenges:
    Scaling AI requires synchronized improvements in process, culture, data, and technology. Only a fraction of pilots reach production today, and leaders are focusing on governance, transparency, and business alignment to increase success rates. AI adoption is shifting from replacing roles to enhancing them—empowering employees to focus on higher-value work.
  2. Services-as-Software: Market and business models:
    The emerging services-as-software economy is blurring the line between traditional services and software, moving toward outcome-based, subscription-led models. Vendors and enterprises alike are embedding AI into platforms, partnering with startups, and experimenting with real-world automation—from AI-led procurement to intelligent sales operations.
  3. Technology and infrastructure:
    AI at scale demands rethinking outdated technology foundations. Many enterprises remain constrained by legacy ERP systems that limit agility and automation potential. AI orchestrator platforms are emerging as a key enabler—managing multiple models, optimizing performance, and accelerating agent-driven software development.
  4. Talent, culture, and organizational impact:
    People and culture remain the linchpins of AI success. The focus is shifting to reskilling teams for AI-enabled roles and fostering trust in automation. Centralizing specialized talent, improving employee experience, and addressing regulatory and ethical challenges are central to sustainable adoption.
  5. Partnerships and ecosystem evolution:
    Provider relationships are evolving toward outcome-based, risk-sharing, and co-innovation models. Enterprises expect partners to deliver tailored, research-backed solutions and integrate seamlessly into their innovation ecosystems, balancing agility with compliance and governance.
  6. Strategic vision and leadership insights:
    AI success requires decisive leadership, realistic expectations, and alignment between business ambition and technology execution. The group agreed that AI should be treated as an enabler of transformation—not a silver bullet—and that mastering today’s capabilities is more valuable than chasing tomorrow’s hype.

Scenes from the Roundtable

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Executive Interviews: Perspectives That Push the Conversation Further

We captured five dynamic interviews that continue the roundtable’s bold conversation. 

Where to Put AI: Business Ops vs. Tech Delivery

Navin Nair, VP, Digital Tech at Mindsprint, discuss how the AI question has shifted from “why?” to “where?” Business leaders are embedding agentic AI into operations to cut time-to-value, boost efficiency, and reduce costs, while CIO/CTO teams focus on AI-assisted software delivery to ship to production faster and more safely.

Services-as-Software: From T&M to Outcomes

Balasubramanian Kumarappan, VP, Data Analytics at Mindsprint, discuss how the shift to a “services-as-software” era—powered by LLMs and agentic systems is reshaping go-to-market models. Providers are moving beyond time-and-materials and capacity-based engagements toward outcome-based pricing, clearly scoped work packages, and co-creation with real skin in the game from build through execution.

From Pilot to Scale: Fix Data & Governance

Nitesh Mirchandani, Chief Business Officer at Mindsprint, discusses why many AI pilots stall and how to scale them. The two biggest blockers: data fragmentation and weak governance. Mindsprint’s approach strengthens these foundations with responsible data architecture and clear guardrails so pilots don’t just succeed—they scale.

HelloFresh on AI: Automating Ops for End-to-End Efficiency

Suman Rao, VP, Global Technology & Analytics at HelloFresh, discusses why AI is a cross-functional productivity lever in retail/CPG. From procurement to fulfillment, data-driven automation and intelligent workflows replace manual steps, improve end-to-end outcomes, and save time by better connecting internal and external partners.

Cracking the AI Scaling Wall

Ashish Chaturvedi, Executive Research Leader at HFS, discusses a new joint study. They focus on two aims: (1) assessing the current state of AI adoption, challenges, initiatives, and ambitions—and (2) testing the emerging services-as-software narrative. Market signals are clear: many enterprises plan to shift portions of human work to AI, while vendors are launching offerings that blend software and services. 

What’s next?

The conversation about building enterprise-scale AI strategies and navigating the services-as-software economy is just beginning. If your organization is striving to modernize legacy systems, strengthen data foundations, or turn AI ambition into measurable business impact, we’d love to continue the dialogue. Let’s connect to explore how to evolve from experimentation to transformation. 

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