What does it really take to move enterprise AI from impressive demos to decisions that show up in quarterly results?

One year into his role as Senior Vice President, Americas Consulting, Neil Dhar sits at the intersection of strategy, capital allocation, and technology execution. Leading the firm’s Americas business and a team of close to 100,000 consultants, he has a front-row view into how large organizations are reassessing their AI investments.

From global healthcare leaders like Medtronic to luxury retail brands such as Neiman Marcus, the conversation has shifted. Early proofs of concept helped executives understand what was possible. Now the focus is firmly on proof of value and on whether AI can drive growth, competitiveness, and measurable return.

In this episode, I speak with Neil Dhar about what has changed in the boardroom over the past year and why ROI has become the central question.

Drawing on more than three decades in finance and private equity, including senior leadership roles at PwC, Neil explains why AI is increasingly being treated as a capital allocation decision rather than a technology experiment.

Every dollar invested has to earn its place, whether through productivity gains, operational improvement, or new revenue opportunities. Vanity projects no longer survive scrutiny, especially when boards and investors expect results on a much shorter timeline.

We also explore how IBM is applying these same principles internally. Neil shares how the company has identified hundreds of workflows across the business, prioritized those with the strongest economic impact, and used AI and automation to drive large-scale productivity gains. The result is a potential $4.5 billion in annual run rate savings by 2025, with those gains being reinvested into innovation, people, and future growth.

It is a candid look at what happens when AI strategy, leadership accountability, and disciplined execution come together inside a global organization.

If you are a business leader trying to separate real value from hype, or someone wrestling with how to justify AI spend beyond experimentation, this conversation offers a grounded perspective on what enterprise AI looks like when it is treated as a business decision rather than a technology trend.

Are you ready to rethink how AI earns its place inside your organization, and what proof of value really means in 2026?

Useful Links

• Connect With Neil Dhar
• IBM Institute for Business Value, “The Enterprise in 2030” study
• Learn More About IBM Consulting

Could AI be the fastest way to make a broken process more expensive?

In this episode of Consulting The Future, I’m joined by Bill Briggs, CTO at Deloitte, to unpack why so many organizations can produce impressive AI pilots, yet still struggle to show real business change. Bill has spent nearly three decades advising leaders across the private and public sectors, and he has a clear point of view on what separates pilot activity from outcomes you can measure, defend in the boardroom, and scale across the enterprise.

We talk about what “good ROI” actually looks like when you strip away dashboards designed for show. Bill explains why early AI efforts often fall into incrementalism, where teams layer new tools on top of old workflows, then wonder why productivity does not move. He also shares a line that will land with anyone watching cloud bills rise, you can end up “weaponizing inefficiency” and turning yesterday’s background automation into today’s token burn.

A big thread in our conversation is the “innovation flywheel,” and Bill translates that into plain English. He argues that innovation cannot live in a silo with nicer office space and a few foosball tables, it has to come from the people closest to the work who know where the friction and “knuckleheadery” hides. He shares Deloitte research that shows a dramatic trust gap, with enthusiasm for AI high in the C-suite, but dropping sharply as you move toward frontline employees. The takeaway is simple, if your people do not feel included, they will resist it, and if they feel threatened, they will quietly avoid it.

We also get into what it takes to move fast without losing control as GenAI and agentic systems spread. Bill points to a pattern he sees repeatedly, organizations invest heavily in technology, but underinvest in the parts that make it safe and sustainable, training, guardrails, process controls, and the engineering discipline that bakes security and governance into how solutions are built. That leads into a practical conversation about rethinking processes from scratch, and why “waiting for the next model release” can become a form of paralysis that competitors will happily exploit.

Finally, Bill offers a refreshingly honest look at technical debt, including his three-part framing of malfeasance, misfeasance, and non-feasance, and how leaders can take a more surgical approach to modernization instead of repeating tired slogans like “cloud good, mainframe bad.” We close with a human moment too, Beach Boys, pinball machines, and a reminder that the best tech conversations still sound like real conversations.

So where do you think most organizations are right now, building real outcomes, or putting a shiny layer on top of yesterday’s problems, and what would you change first, and please share your thoughts?

In this episode of Consulting The Future, I’m joined by Julie Teigland, Global Vice Chair – Alliances & Ecosystems at EY, to unpack the growing role marketplaces are playing in enterprise AI adoption. At a time when organizations are overwhelmed by tools, vendors, and bold claims, Julie offers a pragmatic view of how marketplaces can collapse complexity rather than add to it.

We begin by exploring why so many AI initiatives stall between pilot and production. The issue, as Julie sees it, is rarely a lack of technology. It is the friction between tools, governance, integration, and operating models. Marketplaces, when designed well, bring together pre-vetted solutions, integration patterns, security signals, and governance frameworks. That coordination reduces the invisible work leaders often underestimate, such as vendor risk reviews, compliance checks, and architecture validation.

Julie is candid that not all marketplaces are equal. The ones that create value are designed around business outcomes, not product listings. Fraud reduction, supply chain resilience, or industry-specific cost transformation efforts require curated ecosystems. That means benchmarks, peer usage patterns, reference architectures, and assurance around regulatory alignment. For executives facing board-level scrutiny, that difference matters.

We also dive into risk and accountability. Julie highlights how many organizations focus on model ethics and accuracy, yet overlook integration risk. Once AI is embedded into workflows, the questions shift. Who owns the decision? How are outcomes logged and audited? What happens when multiple vendor systems interact? This is where governance can break down, especially at scale.

Through alliances with leaders such as Microsoft and SAP, EY is helping shape how AI solutions are vetted and deployed across industries. Julie shares how EY applies a “client zero” approach, using its own marketplace internally to test value, resilience, and governance before scaling to clients. With 70,000 clients, 668,000 servers, 13,600 workflows, and 269 million transactions processed daily, the system is tested constantly under real operational pressure.

We also examine the influence of the EU AI Act and the contrast between innovation-first and responsibility-by-design approaches across regions. Julie argues that speed and governance are not opposing forces. True acceleration comes from industrializing governance rather than bypassing it.

If AI success depends less on technological brilliance and more on clarity, orchestration, and trust, then marketplaces may be evolving from simple app stores into operational control planes. The question is not whether ecosystems matter, but how deliberately you design them. So as AI adoption accelerates inside your organization, are you simply adding tools, or are you building a marketplace strategy that can withstand scrutiny and scale?

What separates organizations that talk about AI from those that actually redesign how work gets done? In this episode of Consulting the Future, I sit down with David Martin, Senior Partner at BCG, to explore what enterprise transformation really looks like beyond the headlines. This conversation goes straight to the heart of what consulting leaders are advising boards and C suites right now.

David leads BCG’s People and Organization business and sits on the firm’s global AI leadership team, which gives him a front row seat to how large organizations are approaching AI at scale. He shared why companies seeing real returns are doing fewer things with greater ambition, rather than launching hundreds of disconnected pilots. The difference is not access to tools, it is clarity of workflow and leadership intent.

We unpacked why so many organizations remain stuck in experimentation. According to David, the shift happens when leaders rethink work end to end, redesign roles, and embed AI directly into core processes rather than layering it on top. The firms making progress are not chasing marginal time savings. They are restructuring how value is created across functions such as engineering, marketing, and finance.

Training emerged as a defining theme. David explained why short workshops are rarely enough and why immersive, hands on learning makes the difference. Supportive leadership behavior increases effective adoption dramatically, and curiosity at the top signals psychological safety across teams. Consulting advice today is as much about mindset and culture as it is about architecture and infrastructure.

We also discussed agentic AI, governance risk, and the reality of shadow AI inside enterprise environments. While executives worry about low adoption, many employees are already experimenting outside formal systems. The consulting challenge is balancing innovation with controls, embedding governance without stifling momentum, and aligning IT, HR, and operating models around a shared framework.

David reinforced a simple but powerful model. Ten percent algorithms, twenty percent technology, and seventy percent people and process. That ratio has remained consistent across digital waves, and it continues to define where transformation efforts succeed or stall. Consulting firms like BCG are increasingly focused on redesigning workflows and redefining competencies, not simply recommending new tools.

As we look toward 2026, David sees acceleration ahead, particularly in healthcare and knowledge intensive sectors. The pace of change will not slow, which makes structured advisory support even more central to enterprise strategy. The question is no longer whether to adopt AI, but how to do so with intent, discipline, and a clear operating vision.

If you are a business or technology leader thinking about your own roadmap, this episode offers grounded insight from someone helping define the global playbook. What role should advisory firms play in shaping your transformation strategy, and where do you need external perspective most? I would love to hear your thoughts.

On this episode of Consulting the Future, I’m joined by Matt Strentz, co-founder of Iversoft, to unpack what the next wave of digital product development really looks like.

Matt takes us inside Iversoft’s journey from a small mobile studio at the dawn of the iPhone era to an international innovation partner trusted by enterprises and startups alike. We explore why mobile-first, multi-platform strategies are now essential for gathering real-time user feedback and building products that truly stick.

The conversation also dives into Iversoft’s process-driven consulting model, where transparency and quality assurance begin on day one. Matt shares how human-centered design and user experience must be baked into the foundation of every platform, and why AI should be treated as a practical productivity enhancer rather than a shiny buzzword.

From adapting lessons across industries like healthcare, aviation, and construction to building long-term client partnerships based on trust, this episode highlights a pragmatic approach to digital transformation. If you want to understand how to prepare your business for emerging technologies without falling into endless pilot projects, this conversation is packed with insights.

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When AI steps into the boardroom, it’s rarely a quiet arrival. Expectations are set high, with promises of sharper productivity, streamlined workflows, and measurable efficiency gains. But as Francesco Brenna, Global Leader of AI Integration Services at IBM Consulting, points out in this episode of Consulting the Future, the true opportunity lies in something far deeper than speed. It’s about reimagining the way entire businesses function.

Recorded during a sweltering summer in New York, our conversation breaks down what “agentic AI” really means for leaders under pressure to make AI more than a buzzword. Francesco draws a clear line between the passive assistants many companies have experimented with and the next generation of intelligent agents that not only advise but act. This shift, he argues, demands more than dropping AI into an existing system. It calls for rebuilding processes from the ground up with business outcomes as the starting point.

We dig into why data readiness remains the number one barrier to success, despite years of investment in platforms and governance. Francesco introduces the concept of “data products” to ensure AI agents operate with the right context and memory. He outlines IBM’s three-layer approach to agentic applications: user experience, orchestration, and data. He also explains why standards like Model Control Protocol (MCP) may be the key to integrating AI with legacy systems at scale without sacrificing security or trust.

Francesco shares real-world results from IBM’s work in customer service, insurance, and pharma, where agentic AI has dramatically improved containment rates, reduced months of work to weeks, and enabled smarter decision-making for knowledge workers. He is candid about the human side of adoption, detailing how IBM uses hackathons, hands-on experimentation, and human-centered design to build confidence and capability across the workforce.

For enterprise leaders grappling with how to move AI out of the pilot phase and into meaningful, measurable impact, this conversation offers a grounded, practical roadmap built on doing the right AI, and doing it right.

In this episode of Consulting the Future, I’m joined by James Osborne, Chief Digital Officer at KPMG UK, to explore how one of the world’s largest professional services firms is approaching AI with clarity, intention, and scale.

James shares what he’s learned in his first year as CDO, including the rollout of Ava, KPMG’s internal generative AI assistant now used by over 11,000 employees. We unpack how Ava is surfacing firmwide expertise, why adoption is about people before platforms, and how KPMG’s Trusted AI Framework is helping to embed ethical, human-centric principles into every use case.

We also dig into:

• The mindset shift required to move from AI hype to real-world impact
• How the “Summer of AI” initiative reached 16,000 participants across 14 countries
• The role of digital ninjas in driving cultural change
• How AI is augmenting professionals rather than replacing them
• Why KPMG is focused on curated knowledge and client-specific intelligence
• Lessons learned from early adoption, including why managing expectations matters

James also reflects on how AI can be both revolutionary and familiar, referencing the 1966 ELIZA chatbot as a reminder that hype cycles always have history. His advice to other organisations is simple: don’t wait to get started. Start small, stay pragmatic, and bring your people with you.

Why do up to 90% of new product launches still fail, even in the age of AI?

In this episode of Consulting the Future, I’m joined by Stuart Jackson, Vice Chair of L.E.K. Consulting and author of Predictable Winners, a new book that distills nearly four decades of innovation strategy into a practical framework for reducing failure rates in product and service development.

Stuart shares the hard truths behind failed innovation efforts, from siloed thinking and unmanaged risk to the myth of the “big idea.” He also reveals what top innovators do differently and how AI is beginning to play a critical role in spotting demand signals, identifying unmet needs, and shaping smarter bets.

We explore:

• Why many great ideas still fail without systems to manage risk across the innovation lifecycle
• How AI is improving forecasting, testing, and early-stage product screening
• The power of external innovation strategies like acquisitions and licensing
• Why speed matters, but discipline matters more
• What established firms can learn from startups without falling for the “fail fast” trap
• Real-world examples from litter robots to autonomous aircraft landings

Whether you're building a startup, leading R&D, or navigating innovation inside a Fortune 500, this episode is packed with grounded insights to help you innovate with more clarity, more precision, and a lot less waste.

If you want to shift from product guesswork to a repeatable innovation strategy, this is the place to start.