The phrase enterprise AI transformation and governance is more than a headline. It describes how companies are applying AI across operations while trying to keep control, build trust, and scale responsibly. In this post, we look at five recent moves — a services expansion, new research on trust, a large acquisition, a consumer AI tool, and a manufacturing CX overhaul — to show what leaders are doing now and what comes next.

## Cognizant’s regional expansion and responsible AI work: enterprise AI transformation and governance at scale

Cognizant is doubling down on both regional reach and responsible AI. The company signed a three‑year deal with Dubai’s DAMAC Group to manage and modernize the conglomerate’s IT infrastructure and applications. Additionally, Cognizant continues to work with SAP on AI-driven ERP modernization. Therefore, the firm is positioning itself as a partner for large enterprises that need both technology muscle and governance frameworks.

This move matters for two reasons. First, it shows demand for service providers who can deliver regional execution — especially in fast-growing markets like the Middle East. Second, it signals that clients want vendors who combine transformation chops with responsible AI practices. In practice, that means projects won’t be judged by speed alone. Instead, buyers will prioritize data controls, model explainability, and compliance support.

Impact and outlook: Expect more enterprises to choose partners who offer packaged transformation programs that include governance tools and operating models. Therefore, service firms that invest in responsible AI capabilities will find more long-term, strategic engagements.

Source: CX Today

The trust gap: why enterprise AI transformation and governance is stalling adoption

New research from Alteryx highlights a clear barrier: trust. The study finds that although AI use is rising — with about two‑thirds of business and IT leaders reporting they use AI more than a year ago — adoption is being slowed by concerns over data quality and trust. In short, organizations want AI, but they are cautious about outcomes and reliability.

Why this matters: AI projects often depend on messy, siloed data. Additionally, executives worry about decisions they cannot fully explain. Therefore, teams spend time reconciling datasets, validating model outputs, and building review processes before scaling. That work slows deployment, but it also creates an opportunity. Companies that tackle governance, lineage, and testing early reduce downstream risk and accelerate adoption later.

What to do: Leaders should treat governance as a first‑class project line item. For example, set clear data quality metrics, designate owners for datasets, and require simple explainability checks for production models. Additionally, involve business stakeholders early to align the AI outputs with decision needs.

Impact and outlook: The trust gap will remain a gating factor for many enterprises. However, firms that invest in data quality and transparent governance will move faster and build more durable AI programs.

Source: CX Today

QXO’s Kodiak deal: consolidation that pushes tech-enabled distribution

QXO’s agreed purchase of Kodiak Building Partners for roughly $2.25 billion shows how M&A is becoming a route to scale technology across physical networks. The acquisition expands QXO into lumber, trusses, windows, doors, and other construction supplies. More importantly, the deal sets the stage for technology-driven operating changes across a broader network of yards and categories.

This is significant for enterprise AI transformation because distribution businesses are now thinking like tech companies. They are investing in inventory systems, routing optimization, pricing algorithms, and shared data platforms. Therefore, when a buyer consolidates brands and yards, it can standardize systems and accelerate AI-driven efficiencies.

Practical effect: Expect integration efforts to focus on harmonizing data and standard operating procedures. That work is necessary before advanced analytics or automation can be deployed widely. Meanwhile, buyers will aim to capture cost synergies and faster service through smarter logistics and demand forecasting.

Impact and outlook: Large acquisitions like QXO’s signal more consolidation in sectors with physical assets. As a result, private equity and strategic buyers will prioritize firms that can show digital roadmaps and governance plans to scale AI across many locations.

Source: Digital Commerce 360

Grocery AI and UX: Uber Eats’ Cart Assistant and the customer-facing side of enterprise AI transformation and governance

Uber Eats launched Cart Assistant, an AI tool that builds full grocery baskets “in seconds” from text or image prompts. The feature is designed to speed shopping and improve discovery. Therefore, it is a clear example of how generative AI is moving quickly into commerce user experiences.

This development highlights two enterprise lessons. First, consumer-facing AI drives measurable UX gains when it simplifies a real task. Customers save time, and platforms can increase basket size or frequency. Second, even seemingly simple tools require governance thinking. For example, product suggestions must respect inventory availability, pricing rules, and dietary or safety considerations. Therefore, product teams must work with legal and operations to ensure suggestions are accurate and compliant.

What companies should consider: Start with a narrow, measurable use case. Then, add guardrails: verified data sources, roll‑out to limited audiences, and clear escalation paths for incorrect outputs. Additionally, capture metrics like conversion lift and customer satisfaction to measure value.

Impact and outlook: Rapid UX wins will encourage other retailers and marketplaces to deploy similar assistants. However, those who pair UX innovation with operational controls and data checks will scale more successfully and avoid reputational risk.

Source: Digital Commerce 360

Protolabs’ digital CX overhaul: automation and AI for quoting and procurement

Protolabs plans a new online customer platform and to expand automation and AI across quoting, design, and ordering of manufactured parts. The company signaled this shift as part of a broader effort to simplify interactions for engineers and buyers. One named outcome is a platform called ProDesk, which aims to streamline procurement workflows.

Why this matters for enterprise AI programs: Manufacturing and procurement are full of repetitive tasks and manual checks. Therefore, automation can reduce cycle times and improve accuracy. At the same time, companies must ensure that AI-driven recommendations respect technical constraints and contract terms. That is where governance and domain knowledge intersect.

Practical steps: Start by automating well-understood steps such as parts quoting or design-for-manufacturability checks. Then, layer in predictive suggestions and smart routing to experts when edge cases arise. Additionally, embed audit trails so buyers and engineers can trace decisions back to data inputs and rule sets.

Impact and outlook: Firms that modernize customer and procurement experiences will gain faster order cycles and higher customer satisfaction. Moreover, integrating governance into these systems will make it easier to scale AI across complex operational environments.

Source: Digital Commerce 360

Final Reflection: Connecting growth, trust, and practical governance

Across these five stories, a clear pattern emerges: organizations are eager to apply AI to growth, customer experience, and operations. However, success depends on one balancing act — move fast enough to capture value, but slow enough to put governance, data quality, and operational controls in place. Service providers like Cognizant are packaging regional execution with responsible AI. Research from Alteryx warns that trust and data quality will slow adoption without action. Meanwhile, M&A and tech consolidation, as with QXO and Kodiak, are creating platforms that can be standardized and modernized. On the customer side, tools like Uber Eats’ Cart Assistant show quick UX wins when paired with operational rules. Finally, Protolabs’ platform plans illustrate how manufacturing and procurement benefit from staged automation and governance.

Therefore, the next 12–24 months will reward organizations that treat governance as part of the product. Additionally, leaders should prioritize measurable pilots, clear data ownership, and simple explainability. In short, enterprise AI transformation and governance are not opposing forces. Instead, they are complementary steps toward durable, scalable value.