Every company is a software company: Six ‘must dos’ to succeed

2. Innovators dominate headlines, but scalers dominate markets

See if this sounds familiar: an exciting new technology hits the scene, and a mad scramble results in lots of experiments and a few promising developments that often run into headwinds, fail to scale, and peter out. Gen AI could become the latest victim of this pattern. Our unscientific but plausible view is that large language models (LLMs) that underpin gen AI represent only about 15 percent of the effort to scale but currently occupy 85 percent of the airtime. It’s critical to remember that getting the full potential value from technology requires companies to be able to scale it.

Achieving scale comes not just from building the range of supporting capabilities (see more about this in Idea #3) but also from focusing on the specific processes and mechanisms that enable scale from the beginning of any venture, whether that’s harnessing a technology or launching a new business. While the question still stands whether start-ups—unencumbered by legacy systems and having access to LLMs and cloud technologies—have the advantage over legacy businesses when it comes to scale, even they need to keep a clear eye on building capabilities that enable scale. Successful CEOs are as passionate about scale as they are about strategy.

Questions to consider:

• How are you incentivizing scale (not just innovation)?
• What specific initiatives on your road map directly support scaling?
• Have you identified the specific roadblocks to achieving scale, and are you clear about how to deal with them?

A deeper look:

The big boost: How incumbents successfully scale their new businesses

Executive’s guide to developing AI at scale

Tipping the scales in AI: How leaders capture exponential returns

3. Leaders master the digital ‘More law’ of compounding value

We’ve all heard of “Moore’s law”: the number of transistors in an integrated circuit (IC) doubles about every two years. There’s a similar trend at play when it comes to digital and AI in terms of compounding advantage—we call it the “More law.” The distance between digital and AI leaders and their industry competitors is growing. A set of leading companies haven’t just figured out how to harness digital and AI to generate value but also how to do it faster and increase the distance between themselves and other players. This is happening because digital and AI, implemented well, compound competitive advantages.

These leaders know it’s not about building one “magic” use case. Instead, it’s about having hundreds of technology-driven solutions working together to create great customer and employee experiences, lower unit cost, and generate value. This is hard to do, but also hard to copy. We see clearly in our banking research that digital leaders were much better at integrating digital and AI across the entire landscape of customer journeys. This reduced friction points, which increased their advantage in online sales over digital laggards and reduced their costs to serve. These effects contributed to their profit-and-loss (P&L) edge and enabled multiple expansions, resulting in significant TSR outperformance.

Questions to consider:

• Do you have a clear view of your rate of technology-driven growth today compared with that of your direct competitors?
• Are you developing those hard-to-copy capabilities (processes, workflows, automations) that power the products and services you need to build and improve?
• Are you clear about the three most important improvements you should make to increase the pace of your development?

A deeper look:

How to implement transformations for long-term impact

In digital and AI transformations, start with the problem, not the technology

5. If knowledge is power, data is knowledge

It’s long been fashionable to talk about the “knowledge economy” as something distinct from the broader economy, but the reality is that every company is in the knowledge business, and every worker is becoming a knowledge worker. This is increasingly evident as advanced AI capabilities and enhanced tools and techniques are made available to every worker. How well companies embrace their knowledge dividend will come down to how well they harness their data. It’s fair to say that no company can have an AI or business strategy without having a data strategy. That’s because without good, clean data that is easily (and responsibly) accessible across the business, it will be impossible to generate business, operational, and AI value.

Questions to consider:

• Are you clear on how your proprietary data, combined with the world’s public data, will lead to competitive advantage?
• What standards and best practices do you have in place for building data products across the organization, and are they easily accessible by relevant teams?
• Do you have data governance in place that builds digital trust with your customers and stakeholders?

A deeper look:

The data-driven enterprise of 2025

How to unlock the full value of data? Manage it like a product

6. A workforce with gen AI ‘superpowers’ needs a human breakthrough

Gen AI has started as a copilot technology and may evolve to become an automated pilot for some tasks. This essentially means everyone will have a utility belt of AI superpowers, creating a workforce of “superworkers”. Tech breakthroughs have increased productivity and created both different and more work for humans. For this reason, companies need to shift their focus to human breakthroughs in learning, reskilling, upskilling, and career management to enable their workforce to best take advantage of gen AI and other technologies.

The gains in productivity will not be evenly distributed and will depend on the complexity of tasks and the maturity of the AI copilot. More important will be understanding what skills humans need in order to adapt and take advantage of their copilot’s capabilities. Gen AI will make natural language, for example, the new user interface, requiring people to learn how to talk differently to machines.

Questions to consider:

• Have you identified the most important roles in your business that could benefit from a gen AI copilot?
• Which communities of developers are active in sharing knowledge?
• In practice, how well have your data scientists and engineers learned to work with their copilots?

A deeper look:

What separates top product managers from the rest of the pack

The bottom-line benefit of the product operating model

8. IT as a service is the next generation of your tech function

To enable an atomized business, companies need their technology team to operate more as a service function. Distributed digital innovation is the end state of a rewired company in which tech teams can develop digital and AI solutions to improve customer experience and lower unit costs. Tech will eventually be embedded into every product and function, and “heritage” centralized IT functions will be massively automated and delivered like cloud-provisioned services. IT can’t support this kind of distributed innovation environment by sticking to its traditional role as a controlling entity managing technology from the center. The value will now come from IT’s ability to enable innovation by shifting from the protection of big tech assets to purveying small blocks of code. APIs will be the primary way companies expose their digital capabilities. They will be integrated into “superapps” stitched together with gen AI–type code generators for better user functionality.

Questions to consider:

• How often are the code and solutions that developers create reused by other teams?
• How many libraries exist for key artifacts such as APIs and prompts, and are they used frequently by your technology teams?
• How many as-a-service capabilities has your technology function developed?

A deeper look:

The big product and platform shift: Five actions to get the transformation right

In search of cloud value: Can generative AI transform cloud ROI?

Security as code: The best (and maybe only) path to securing cloud applications and systems

9. The name of the game is the same: Value

This might sound like business 101, but it’s surprising how often companies lose sight of it: the point of digital, AI, and tech isn’t getting better at digital, AI and tech; it’s building value. A big reason for falling short of financial targets in a digital and AI transformation is not setting the targets correctly from the start. Too often, companies shoot for marginal gains, but that constrains thinking, and small thinking leads to small results. Our rule of thumb is that a robust digital road map should deliver EBITDA improvement of 20 percent or more.

Questions to consider:

• Is your digital and AI transformation effort focused on a domain that is large enough to create meaningful value but small enough to be accomplished with the resources you have?
• Is the target of your digital and AI transformation to increase incremental value by at least 20 percent?
• How much value have your digital and AI initiatives generated in the past six months?

A deeper look:

Questions to consider:

• How have you changed your approach to strategy and operations based on digital-twin and testing advances?
• How well integrated is your digital-twin platform integrated into your product, solution, or business development?
• How good (and pervasive) is your A/B testing capability?

A deeper look:

Digital twins: The key to smart product development

Digital twins: From one twin to the enterprise metaverse