Why do enterprise transformations lose momentum when the strategy, budget, and technology all appear to be in place?

In this episode of Consulting the Future, I speak with Dr. Koby Glass of IDEA Consulting Experts about the operating model problems that quietly stop large programs from delivering. Koby has spent over 20 years entering distressed transformations, stabilizing the work, and designing systems that allow organizations to move from strategy to execution.

His diagnosis begins with friction rather than failure. Decisions remain unresolved, steering committees spend their time refining status reports, every initiative becomes priority number one, and the same people are spread across too many programs. Koby calls one version of this “governance theater,” where executives describe a problem through increasingly polished slides without removing the blocker that keeps teams waiting.

We discuss Koby’s 24, 48, and 72-hour decision spine. Tactical decisions should be resolved within 24 hours, operational decisions within 48, and strategic decisions within 72. He shares an example in which three decisions remained on a CEO’s desk for 18 days while hundreds of people continued activity around a $72 million portfolio. A focused 60-minute intervention released the decisions, exposing a leadership cadence that operated quarterly while the delivery system needed weekly action.

Koby also explains what he does during the first 30 days of a recovery. The opening days establish ownership and escalation points. By the second week, the team creates a kill list for work that does not support the program’s strategic aims. Work-in-progress limits then reduce context switching and move capacity toward the highest-value priorities. Day 30 becomes a go or no-go decision, followed by the design of an operating model the organization can run after the consultant leaves.

The conversation extends to SAP, AWS, and AI programs. Koby warns that technical readiness does not guarantee that a business can absorb a new system. He also describes AI as an accelerator that cannot distinguish between a healthy process and a broken one. If decisions are ambiguous or ownership is unclear, AI may increase the speed of the dysfunction rather than improve the outcome.

His closing advice is to trace one important outcome backward until you find where the work slowed, stopped, or stalled. Organizations often measure thousands of activities while ignoring the age of the decision blocking progress.

Would measuring decision latency reveal why your transformation is losing momentum? Listen to the conversation and share your thoughts with me.

Useful Links

• Dr. Koby Glass on LinkedIn
• IDEA OS transformation diagnostic
• Dr. Koby Glass’s daily transformation tips are published on LinkedIn.

What happens to the subscription economy when consumers stop choosing every service themselves and ask an AI agent to manage the decision?

In this episode of Consulting the Future, I speak with Giles Tongue, Vice President of Marketing at Bango, about the growing tension between subscription demand and subscription fatigue. Giles oversees marketing for Bango's Digital Vending Machine, which helps companies offer subscription bundles or make their own services available through bundles.

Subscriptions have expanded far beyond television and music. Consumers now pay recurring fees for gaming, fitness, news, productivity software, home technology, and AI tools. Giles cites estimates discussed in the interview that value the subscription economy at $700 billion today and project growth to $1.2 trillion by 2030. At the same time, he says consumers are becoming less certain about what they pay for, where they bought it, and whether they still use it.

That tension has brought bundling back into the conversation. The old cable model often forced customers to pay for channels they did not want. Giles describes a newer model built around choice, where customers select services from a larger marketplace, manage them through one account, and may receive a larger discount as they add subscriptions.

Mobile operators have offered entertainment services with phone and broadband plans for years, but the participants are changing. Banks, retailers, and loyalty programs are becoming distribution channels, while content providers are also packaging their services together. Giles points to examples such as Optus SubHub in Australia and the Netflix and Max bundle sold through Verizon to show how indirect subscription sales are becoming a larger part of the market.

The attraction is not limited to discounts. A service provider can use bundling to reach a large existing customer base and remove payment friction by adding a subscription to a familiar bill. For the subscription company, that arrangement can also change customer acquisition economics. Advertising usually requires spending before a sale occurs, while a distribution partner may take a share only when the subscription is sold.

There are tradeoffs. An intermediary can make discovery and payment easier, but it also places another company between the service and the subscriber. Brands need to consider how much control they retain over the customer relationship, data, positioning, and renewal experience. Consumers may gain one place to manage several subscriptions, although a larger bundle can still become expensive or difficult to understand if choice is poorly presented.

Our conversation then moves from bundles to AI because both subjects raise the same question: who helps the customer make sense of abundant choice? Giles says people already struggle to find where a program or sporting event is available. Voice search is one response, but AI agents could go further by finding a service, checking access, starting a subscription, and canceling it when it is no longer needed.

Giles cites Bango research discussed in the interview suggesting that one in five Americans would already allow AI to subscribe, manage, and unsubscribe from services on their behalf, with the figure rising to 40 percent among a younger group. Treat those numbers as Bango research until the full report is linked, but the business implication is worth considering. If an agent cannot connect with a provider, understand its offer, or complete the transaction, it may choose a competing service instead.

Would you allow an AI agent to manage your subscriptions, and what controls would you expect before giving it that authority? Listen to the episode and share your thoughts.

Can a company buy its way to stronger AI capability, or does an acquisition create an entirely new set of integration, talent, and cultural problems?

In this episode of Consulting the Future, I speak with Nadine Mirchandani, EY Global Deputy Vice Chair and EY-Parthenon Global Deputy Leader for Strategy and Transactions. We examine why mergers and acquisitions are becoming a route for companies seeking AI technology, specialist talent, and faster business change.

According to EY-Parthenon’s CEO Outlook Pulse Survey, 48% of CEOs say stronger technology or AI capability is one of the most important factors shaping their M&A strategy. Nadine also notes that 80% of CEOs surveyed expect to increase AI investment.

For many businesses, the build versus buy AI decision comes down to speed, scale, and access to talent. Developing mature capabilities internally can take years, while an acquisition may provide established technology and experienced teams. However, buying the asset does not automatically create the desired business outcome.

Nadine argues that the acquiring company supplies something equally valuable: context. This can include proprietary data, existing customers, industry knowledge, capital, and routes to market. The opportunity comes from combining that context with the acquired team’s specialist skills without burying it beneath unfamiliar processes.

That creates one of the hardest questions in AI mergers and acquisitions. How does a large institution integrate a smaller technology company while preserving the culture, working methods, and people that made it attractive?

We discuss why integration planning needs to begin before the agreement is signed. Nadine explains that leaders must give employees on both sides a credible picture of their future within the combined organization. Trust, relationships, and clear opportunities to work together can determine whether specialist talent stays or heads for the exit.

AI is also changing the M&A process itself. It can analyze large datasets, identify patterns, support deal sourcing, and provide teams with better information during due diligence. Nadine discusses EY-Parthenon’s OneEdge platform and its role across strategy, transactions, and subsequent business change.

However, data cannot determine whether two leadership teams will work effectively together or whether their cultures are compatible. Major deals will continue to require experienced people who can assess business risk, relationships, trust, and behavior.

The conversation ends with three questions for any CEO considering an AI acquisition. Why does the deal support the wider business strategy? Can the company integrate the capability and retain its talent? Does buying provide an advantage that an internal build cannot deliver quickly enough?

If acquiring AI capability is the easy part, are businesses giving enough attention to the people, culture, and execution required after the deal closes? Listen to the conversation and share your thoughts with me.

What happens when AI allows finance teams to work at unprecedented speed, but enables mistakes to travel just as quickly?

In this episode of Consulting the Future, I speak with Graeme Fleming, Industry Principal for Governance, Risk and Compliance at Workiva, about how CFOs, CIOs, auditors, and risk leaders can capture the productivity benefits of AI without sacrificing governance, data integrity, security, or human judgment.

Graeme brings more than 25 years of experience spanning Big Four consulting, internal audit, risk management, and GRC. He explains why the current AI wave presents a different challenge from previous technology changes. Adoption is moving quickly, employees already have access to powerful AI tools, and companies that fail to provide approved options risk creating a growing problem with shadow AI and sensitive corporate data being entered into systems they do not control.

The conversation examines how AI is changing the role of the CFO. By automating data gathering, reconciliation, analysis, and reporting tasks, finance teams can spend less time managing spreadsheets and more time interpreting information and supporting business decisions. But faster analysis only creates value when leaders can trust the data going in, understand what AI systems are doing, and critically review the results coming out.

As Graeme puts it, companies also risk "making mistakes at the speed of AI."

We discuss why governance and controls should not be viewed as barriers to AI adoption. Graeme compares controls to the brakes on a bicycle: they do not exist simply to make you stop, but to give you the confidence to move faster because you know you can remain in control.

That becomes increasingly important as businesses introduce AI agents and give automated systems greater autonomy. Graeme explains why companies need clarity around what an AI system is designed to achieve, where its data comes from, what happens to that information, how the model operates, and who remains accountable for reviewing its output.

We also examine automation bias and the danger of trusting authoritative-looking AI responses when employees are already under pressure. Human review cannot become a box-ticking exercise. Finance professionals, auditors, and risk teams need to retain the ability to question outputs, identify anomalies, understand context, and take ownership of decisions.

Graeme shares practical examples of where AI is already delivering value in financial reporting and audit, including reviewing lengthy annual reports against disclosure requirements, checking consistency across documents, benchmarking reporting, automating reconciliations, and conducting compliance testing at scale. These applications can remove hours of repetitive work while allowing experienced professionals to concentrate on exceptions, insight, and judgment.

The conversation also looks at the emerging role of the AI auditor, both using AI to support audit work and auditing the AI systems businesses increasingly depend upon. Model governance, security, data provenance, controls, and accountability are becoming business concerns that CFOs and risk leaders can no longer leave solely to technology teams.

For CFOs, CIOs, auditors, consultants, and risk leaders, this episode provides a practical perspective on AI governance, financial reporting automation, shadow AI, automation bias, data integrity, and maintaining meaningful human oversight.

The opportunity is not to choose between AI-driven productivity and strong governance. It is to design controls that give companies the confidence to use AI at greater scale while remaining accountable for the decisions and information their systems produce.

What happens when AI stops being a tool that sits alongside a business and starts becoming part of the delivery model itself?

In this episode of Consulting the Future, I sit down with Esteban Sancho, CTO for North America at Globant, to explore how AI is changing the economics, structure, and expectations of modern consulting. Esteban shares how Globant's AI Pods combine agentic AI, automation, and experienced engineering talent into what he describes as a delivery system rather than a traditional project team.

We discuss why so many organizations remain stuck in AI pilot mode despite enormous investment and executive pressure to demonstrate results. Esteban explains the gap between what AI is capable of and what most businesses are operationally prepared to support. From rethinking workflows and team structures to creating governance models that build trust, he offers a practical view of what it takes to move from experimentation to production.

The conversation also examines the future role of consultants in an AI-powered world. Will AI replace consulting teams, or simply change what expertise looks like? Esteban argues that human judgment, systems thinking, and the ability to design reliable AI delivery models are becoming more valuable, not less.

We also explore why outcome-based delivery is gaining momentum, how financial institutions are adopting AI faster than many expected, and what business leaders should consider when balancing innovation with oversight and accountability.

If you're trying to understand where consulting, software development, and enterprise AI are heading next, this episode offers a thoughtful look at how some organizations are already adapting to a very different future.

What role do you think human expertise will play as AI becomes embedded in every business process? I'd love to hear your thoughts.

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?