Ten unsung digital and AI ideas shaping business

| Article

If you wanted to boil down 2023 to one concept, it would have to be generative AI (gen AI). Few of us can remember a technology that has swept through the business community with such speed and impact. Hardly a day passes without a new development hitting the headlines. While still blanketed with caveats and unknowns, gen AI stands to have a profound impact on how we live and work.

But the very excitement around gen AI is in danger of distracting business leaders from some other core business imperatives and the importance of rewiring their companies. For this reason, we thought it might be helpful to take a quick look at ten underlying ideas that might not be dominating the headlines but are shaping the modern business landscape. Some of these ideas represent significant shifts, such as the importance of architecting the business so that everything can be tested, or how to think about a workforce where everyone has their own gen AI “copilot.” Others, like keeping a tight focus on value, relate to business fundamentals that are often overlooked under the stresses of day-to-day demands and technology hype.

1. Builders are eating the world

We’re all familiar with Marc Andreessen’s frequently referenced insight, “Software is eating the world.” While that’s as true as ever, the more important focus for business leaders is how well they can use that software to build things, from businesses to digital products. The cost of building new digital products and services will continue to come down while the tools available will be easier to use, opening the door to many more citizen builders and making the build process faster and cheaper. Scaling will continue to be a challenge requiring specific focus (see Idea #2), but companies that learn to build, test, and adapt quickly will be in the best position to create value. This is especially true as AI continues to advance and costs for technology-based innovation decline, both of which will challenge not just businesses but also business models.

Questions to consider:

  • How are you building an engineering culture where people have the opportunity and incentives to create and innovate?
  • What are you building in the next 12 months that will create an advantage for your business, not just a commodity or, worse still, a money pit?
  • Are you using software to build products, services, or businesses that create a true competitive advantage for your business?

A deeper look:

CEOs’ choice for growth: Building new businesses

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:

Scaling AI for success: Four technical enablers for sustained impact

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

Never just tech

Creating value beyond the hype

Let’s deliver on the promise of technology from strategy to scale.

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:

The value of digital transformation

Rewired to outcompete

Rewired and running ahead: Digital and AI leaders are leaving the rest behind

Would you like to learn more about McKinsey Digital?

4. Digital and AI leaders must be forever transformers

Digital has seeped into almost every aspect of our lives driven by the confluence of new technologies (cloud, AI), new architectural paradigms (microservices, APIs), and new ways of building software (agile, DevSecOps). As long as tech continues to evolve, your business will need to evolve. That’s why it’s important to think of digital and AI transformation as something you’re going to be doing for the rest of your career rather than as a final destination. A digital and AI transformation is a journey to continuously increase your competitiveness by positioning your business to incorporate new technologies quickly. With technology’s growing importance, distinctions between business leaders and technology leaders will continue to blur. All senior executives will need to know how to get the most from technology in their business area.

Questions to consider:

  • Have you identified the next two to four domains you want to transform and the resources you need to complete the work?
  • What long-term metrics, objectives, and key results (OKRs) do you have in place, and is your board tracking them?
  • Do you have a clear view of which emerging technologies could most enhance your competitive differentiation?

A deeper look:

McKinsey Technology Trends Outlook 2023

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 dividend: Fueling generative AI

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:

Unleashing developer productivity with generative AI

AI could cut time spent coding by 45 percent. But even seasoned tech professionals will need ‘extensive’ training to harness its full power

7. Every company will become a ‘neural business’

Speed and innovation will come from small teams led by engineers with sufficient autonomy and clear guidelines for decision making. Most of us will recognize this as a description of agile. While many might be tired of this overused term, its importance is core to a business’s ability to scale innovation (see Idea #2). As the boundaries of agile expand across an organization, it will need to function like a neural network connecting small teams at its edges to enable the speed companies need to grow and adapt.

Questions to consider:

  • How many of your teams and solutions are led by a high-quality product owner?
  • Is the C-suite aligned on the operating model to enable hundreds of pods to deliver digital innovations?
  • How quickly are you able to conceive of, build, and launch a new product or service?

A deeper look:

The rewired enterprise: How five companies built to outcompete

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:

Technology’s generational moment with generative AI: A CIO and CTO guide

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:

The value of digital transformation

How bold is your business transformation? A new way to measure progress

The economic potential of generative AI: The next productivity frontier

Three new mandates for capturing a digital transformation’s full value

10. The best companies will be the best testers

If you believe that change is only going to continue to accelerate—and we can all probably imagine the hundreds of entrepreneurs cooking up new businesses in their garages right now—adaptability will become one of the most important attributes of a modern company. That means being able to test more, test cheaper, and test faster. The capabilities to do so are here and growing: gen AI to boost productivity, automation to accelerate pace and scale through MLOps, software tools that are easier to use, more-sophisticated digital-twin capabilities, and an increasing number of software developers entering the market.

These developments will change strategy (you can quickly test market demand for a solution), operations (you can test operating models and settings), and design (you can quickly build and iterate on millions of versions of a solution before building it). In some cases—like building live telecom networks—training the AI will be hard or impossible without a digital twin.

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

Explore a career with us