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.