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?