Editor's Letter

Welcome to the August edition of Illuminar, As India prepares to become the world’s third-largest economy, the conversation is rapidly evolving from growth to strategic self-reliance. In this edition, we explore why the time for technological sovereignty is now—and what it really means for enterprises, policy-makers, and national resilience.

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This edition’s cover story, “Trust as the Currency of AI Adoption in B2B Ecosystems,” dives into why AI is no longer just about accuracy—it’s about trust. In B2B ecosystems, where decisions carry financial, regulatory, and reputational weight, explainability and accountability have become the true drivers of adoption. Yet, while most enterprises use AI, only a small fraction have governance frameworks or responsible AI standards in place. This gap creates risks that erode stakeholder and employee confidence alike. The way forward is clear: enterprises that embed transparency, oversight, and responsible practices into their AI systems will not only accelerate adoption but also unlock deeper business value. Trust, ultimately, is the real currency of AI in B2B.

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We’re also delighted to feature an insightful interview with Rene Dortmundt, Director – Global Shared Services at Brightstar Lottery. Drawing on his global experience across Europe, the U.S., Brazil, and India, Rene shares practical and strategic advice for enterprises beginning their SSC/GBS journey. From building governance and process ownership to embedding culture and communication, his reflections are a masterclass in how to set up shared services for long-term impact.

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As always, we’re incredibly grateful for your continued support of Illuminar. Your feedback, ideas, and engagement continue to shape this platform, and we look forward to hearing your thoughts on this edition.

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Wishing you and your families continued success, health, and happiness.

Have a great day and stay safe!

Best regards,

Srividya Kannan

Editor

Trust as the Currency of AI Adoption in B2B Ecosystems

Artificial Intelligence (AI) has crossed the threshold from experimentation to everyday business use. In B2B ecosystems, however, adoption is not simply about whether an algorithm can achieve 95% accuracy. The real question is: Can decision-makers, employees, customers, and regulators trust it?

Trust has become the true currency of AI adoption—and it rests on two pillars: explainability and accountability.

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Why Accuracy Isn’t Enough

In consumer tech, accuracy often reigns supreme. A recommendation engine that predicts your next purchase or a chatbot that resolves 80% of queries is considered a success. But in B2B settings—procurement, supply chain, finance—AI outputs influence millions of dollars, compliance risks, and corporate reputation.

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That is why enterprises increasingly demand AI systems that not only work but can also explain how they work.

A recent McKinsey survey found that 40% of executives identified explainability as one of the top risks to AI adoption, yet only 17% had active initiatives to address it. This gap shows that while leaders understand the importance of trust, most are still figuring out how to operationalize it.

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The Governance Gap

Trust is not built on algorithms alone; it requires governance. Yet most enterprises lag behind here as well.

A Trustmarque report revealed that while 93% of organizations use AI, only 7% have embedded governance frameworks, and just 8% include AI oversight in their software development lifecycle. Similarly, an EY survey (2025) showed that although 72% of C-suites have embraced AI, only a third have robust controls in place.

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This lack of oversight is a ticking time bomb. It means AI is often deployed without the same rigor applied to finance, HR, or cybersecurity—areas where accountability is non-negotiable.

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When AI Missteps Erode Confidence

The risks are not hypothetical. An Infosys study (2025) found that 95% of executives had experienced at least one AI mishap, ranging from biased outputs to regulatory breaches. Shockingly, only 2% of firms met responsible AI standards.

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Such incidents don’t just undermine projects—they erode stakeholder trust and slow down enterprise-wide adoption.

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Employees Don’t Fully Trust AI Either

Trust issues extend to the workforce. A global KPMG-University of Melbourne study involving 48,340 employees found that:

57% admitted to hiding AI use from their managers,
66% don’t validate AI outputs, and
nearly half upload sensitive company data to public tools.

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These behaviors stem from both overconfidence (“the AI must be right”) and lack of guidance. The result? Enterprises face both compliance risks and a widening trust deficit inside their own walls.

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Trust as the Currency of AI Adoption in B2B Ecosystems

Artificial Intelligence (AI) is moving from pilots to enterprise-wide adoption. In B2B contexts—procurement, finance, supply chain, infrastructure—the impact of AI goes beyond efficiency metrics or accuracy percentages. What enterprises increasingly realize is that trust is the real driver of value.

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In these ecosystems, where decisions affect billions in trade, compliance, and reputation, AI adoption is less about accuracy and more about explainability and accountability.

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Why Building Trust Pays Off

Trust directly translates into adoption and ROI.

Higher Adoption: A 2025 LinkedIn analysis of enterprise AI programs showed that companies prioritizing governance and explainability saw ~5% higher revenue growth compared to peers.
Stakeholder buy-in: Gartner research indicates that trustworthy AI initiatives are 3x more likely to gain long-term funding than projects focused solely on technical performance.
Customer Confidence: In B2B ecosystems, partners and clients prefer working with enterprises that demonstrate responsible use of AI, turning trust into a differentiator in competitive markets.

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When stakeholders—employees, partners, regulators, and customers—believe the system is transparent and accountable, adoption accelerates, resistance drops, and benefits compound.

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The Way Forward: How to Embed Trust into AI

Building trust in AI is not a single initiative—it is a combination of technology, governance, and culture. Enterprises leading the way are focusing on:

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Explainability Beyond the Algorithm
- Use interpretable models where possible.
- Provide dashboards that show why AI recommended a decision, not just the outcome.

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Explainable AI (XAI) is growing rapidly, with the market projected to expand from $5.2 billion in 2023 to $22.1 billion by 2031 (20% CAGR). But explainability is not just about technical transparency—it’s about making AI outputs comprehensible to decision-makers and auditable by regulators.

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‍Embedding Governance

Establishing AI governance councils involving compliance, legal, and business leaders.
Embedding oversight into the software development lifecycle so risks are managed upfront, not retroactively.
Creating clear lines of accountability—who owns the decision when AI is involved.

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Trust requires structures as much as systems. Leading enterprises are:

Training the Workforce
- Encourage validation of AI outputs rather than blind reliance.
- Provide policies for safe data usage in AI platforms.
- Foster a culture of AI transparency, where employees feel safe to flag concerns.

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Employees are both the first users and the first line of defense. Training must go beyond tools to address responsible use.

Partner and Ecosystem Transparency
- Include AI accountability clauses in vendor contracts.
- Share explainability standards with partners to ensure alignment.

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In B2B, AI rarely lives inside a single enterprise. Shared data, joint platforms, and vendor ecosystems mean trust must extend across partners.

Measuring and Communicating Trust
- Percentage of AI outputs explained to decision-makers.
- Number of incidents of bias or non-compliance detected.
- Employee confidence scores in AI systems.

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What gets measured gets managed. Enterprises are beginning to use “trust KPIs”:

Regular communication of these measures builds credibility with internal and external stakeholders.

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From Risk Management to Competitive Advantage

The conversation about trust in AI often begins with compliance or fear of misuse. But forward-looking enterprises are reframing trust as a competitive advantage.

It reduces resistance to adoption inside the organization.
It enhances collaboration across B2B ecosystems, where transparency is key.
It future-proofs operations against tightening regulations.
And most importantly, it strengthens relationships with clients, partners, and regulators who are increasingly asking not “how accurate is your AI?” but “how accountable is it?”

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Conclusion

As enterprises scale AI, accuracy alone will not win adoption. In B2B ecosystems, trust is the real enabler—built through explainability, accountability, governance, and culture.

The organizations that succeed will be those that don’t treat trust as a compliance checkbox, but as a strategic currency—one that unlocks adoption, accelerates ROI, and cements their leadership in the AI-driven future.

Interview With Mr. René Dortmundt

René Dortmundt

Director – Global Shared Services, Brightstar

Starting a Shared Services Center (SSC) or Global Business Services (GBS) journey is a bold and transformative decision – one that demands vision, resilience, and a deep understanding of both people and processes. Over the past three decades, I’ve had the privilege of building and leading SSCs across Europe, the United States, and Brazil, each with its own cultural and operational nuances. Today, I manage a globally scoped outsourced BPO organization based in India, where I continue to navigate the complexities of cross-functional service delivery, governance, and strategic alignment. These experiences have taught me what truly drives success – and what pitfalls to avoid – when embarking on the SSC/GBS path.

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Begin with a Clear Purpose and Ownership

When I stepped into my role as Director of Global Shared Services at Brightstar, one of my first priorities was to establish true ownership – not just of outcomes, but of relationships. Leading an offshore BPO team in India while aligning with stakeholders across the Chief Accounting Office, Finance, as well as Business & Regional Controllers quickly taught me that clarity of purpose and accountability aren’t just best practices – they’re foundational. Without them, even the most well-designed SSC model can fail.

That’s why it’s essential to define your SSC’s mission from the get-go and ensure leadership is not only committed to delivery but also to its ongoing evolution.

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Build from the Ground Up – But Build Smart

At Laureate in Brazil, I had the opportunity to build the SSC from the ground up – implementing Finance, Purchasing, and Service Management functions. We delivered every component on time, within budget, and without rework. That level of success wasn’t accidental; it was the result of meticulous planning, relentless follow-through, and a team that believed in the mission.

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What I learned is that you can’t simply copy and paste existing processes into a new structure. An SSC transformation is a chance to rethink how things are done – to design with scalability, audit-readiness, and continuous improvement in mind. It’s not just about centralizing work; it’s about elevating it.

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Centralization Is a Strategic Lever

During my time at Brightstar and earlier at Laureate, I learned that centralizing Finance functions goes far beyond cost savings – it’s about establishing control, ensuring compliance, and unlocking strategic value. One standout example was at Laureate Brazil, where we centralized Indirect Procurement within our SSC. This move not only drove PO compliance to 95% but also delivered over R$3 million in savings in the first year by streamlining purchasing negotiations and contracts.

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To truly add value through your SSC or GBS organization, use centralization as a lever for efficiency and governance. But just as importantly, communicate its benefits clearly to local teams – transparency and collaboration are key to adoption and long-term success.

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Technology Is a Catalyst, Not a Cure-All

Implementing new ERPs across multiple organizations taught me a critical lesson: technology only delivers results when people are truly ready for it. At Unisys, we were able to reduce order entry time from five to just two days – not simply because of the system upgrade, but because we right-sized the team, supported them with clear process flows, and invested in thorough training to set them up for success.

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The takeaway? Change management and user enablement are just as important as the technology itself. If you want your digital transformation to be a success, invest equally in preparing your people.

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Governance and Metrics Drive Credibility

Whether it was establishing governance frameworks at Brightstar or tracking KPIs during my time at Laureate, one principle consistently held true: what gets measured gets managed. In the SSC and GBS environments I’ve led, robust governance and performance metrics weren’t just operational tools – they were also critical enablers for passing internal audits, meeting internal control standards, and satisfying external audit requirements.

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From day one of any SSC/GBS implementation, it’s essential to embed governance structures and define meaningful KPIs. But metrics shouldn’t exist solely for reporting – they should drive continuous improvement, accountability, and trust across the organization.

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Culture and Communication Are Game-Changers

One of the moments I’m most proud of was leading the change management effort during the SSC rollout at Laureate. We introduced the concept of “energizers” – local champions embedded within each university – who became the face of the transformation on the ground. Their enthusiasm, credibility, and proximity to end users created a ripple effect that no top-down communication could have achieved.

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That experience reinforced a lesson I carry into every transformation: never underestimate the power of internal champions. Because at the end of the day, culture doesn’t just support strategy – it determines whether it succeeds or fails.

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Stay Agile and Keep Learning

From launching SSCs in Amsterdam to mentoring startups in São Paulo, I’ve embraced agility and continuous learning as cornerstones of transformation. Whether through Lean Six Sigma certifications, SCIRE business simulations, or in-house university programs, I’ve consistently invested in evolving both myself and the teams I lead.

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Your SSC should be no different. Treat it as a living system – one that grows, adapts, and improves over time. Pilot new ideas, learn from outcomes, refine your approach, and repeat. That mindset is what turns a service center into a strategic engine.

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Final Thoughts

If your organization is considering, or has just begun, its SSC or GBS journey, this is the moment to act with boldness and strategic intent.

Start by asking the right questions:

What value do we aim to deliver beyond cost savings?
Are our processes mature enough to scale and standardize?
Do we have the right partners and talent to lead this transformation with confidence?

I encourage you to connect with those who’ve navigated this path, collaborate across functions and geographies, and learn from both successes and setbacks.

Whether you’re building from the ground up or refining an existing model, the SSC/GBS journey is one of transformation – and its impact can be truly lasting.

Editor's Letter