Transformation programs have become ubiquitous, with more than one-third of large organizations engaged in some form of transformation at any given time. But the conventional model that most companies rely on for transformation has faltered in the wake of today’s rapidly changing business environment. Leaders need a fresh approach — one better suited to the continuously evolving nature of our world.

Typical transformation efforts are organized as programs with a defined beginning and end, often overseen by a program management office. Rooted in a change model popularized by German American psychologist Kurt Lewin in the 1950s, this approach involves three stages: “unfreeze, change, and refreeze.” While effective for discrete projects like implementing a new payroll system, this model falls short in today’s dynamic business environment. The continuous evolution of the external landscape demands ongoing business transformation, with no room for pausing, refreezing, and stepping away.

In this article, we draw on our research and experience to describe the three key drivers of continuous transformation:

1. Adopt an Agile Mindset

Unlike the conventional “unfreeze, change, refreeze” approach, which implies change represents only a temporary disruption, an agile orientation embraces the philosophy of “rethink, reshape, repeat” — an ongoing quest for excellence. Under this paradigm, transformation becomes a perpetual journey involving assessing the company’s strategy, prioritizing critical issues, carefully considering potential alternatives for addressing each issue, choosing the best course of action, adapting the organization accordingly, and then moving to the next critical issue. Much like agile software development, where tasks progress from “to do” to “doing” to “done,” business transformation should involve a similar continuous process of issue identification and resolution.

By embracing an agile mindset, organizations unlock the potential for sustained innovation and adaptation, thereby making continuous change a core capability. The transformation of State Farm, a company we worked with, serves as a compelling example. As a leading provider of various insurance services including auto, home, life, health, and business coverage, State Farm had long been a trusted name in the industry. However, in 2019, the company’s leadership recognized a shift in the fundamentals of their business. The insurance landscape was evolving beyond merely offering coverage; it was now about delivering personalized experiences, swift service, and seamless claims processing within an intensifying pricing environment. Customers increasingly demanded convenience, efficiency, and tailored solutions. In response, State Farm recognized the need to rethink its operations, processes, and technology infrastructure to remain competitive in this swiftly changing market.

A pivotal factor in State Farm’s successful transformation was the adoption of an agile mindset by its leadership. While previous change efforts at the company had been treated as episodic, the pace of change in the insurance industry now necessitated continuous transformation. Accordingly, State Farm built a “persistent change capability,” essentially a suite of tools empowering executives to continually reassess and reshape the company’s strategy and operations. In addition, in working with State Farm, we saw how the company’s newly established “Change Center of Excellence (CoE)” provided support and coaching to leaders throughout the organization in driving transformative change and helping the impacted workforce adapt. This group also served as a central repository for best practices, approaches, and tools utilized at State Farm to strengthen ongoing change initiatives.

State Farm’s transformation journey has already yielded significant benefits for both the company and its customers. By embracing digital technologies and modernizing processes, State Farm improved customer experience, enhanced operational efficiency, and achieved substantial cost savings. Notably, between 2018 and 2023, State Farm Mutual’s net worth — a critical measure of financial strength in the insurance sector — grew from $100 billion to nearly $135 billion, and the growth across its primary lines of business surpassed the average growth rate for each sector. 

2. Use aspirations to continuously challenge and stretch the organization.

Relying solely on top-down targets, even those based on external benchmarks and industry best practices, often falls short of fostering sustained performance improvement. While these targets may initially inspire efficiency enhancements, they often lead to complacency once achieved, perpetuating the rigid “unfreeze, change, refreeze” model of transformation.

Instead, organizations should cultivate aspirations that challenge and motivate at every level. Unlike static targets, aspirations serve as dynamic catalysts for ongoing improvement. They represent goals currently beyond the reach of the company’s existing plans, but not so distant as to be perceived as unattainable. Unlike fixed targets that stagnate progress once achieved, aspirations evolve with the organization and its strategy. They continuously push the organization to adapt to changing market conditions, technological advancements, evolving customer needs, and even growing environmental concerns.

Consider the example of Ørsted, a multinational energy company based in Denmark formerly known as DONG Energy. In 2008, its leadership embarked on a journey to transition the company from a traditional power provider reliant on fossil fuels to a pioneering force in renewable energy.

At the heart of Ørsted’s transformation was a singular, audacious goal encapsulated in a simple number: 85%. Historically, the company derived 85% of its energy from fossil fuels, with only a meager 15% from sustainable sources. The leadership resolved to flip this ratio entirely, aiming for 85% of its energy to come from renewables like wind and solar.

Initially, Ørsted lacked a concrete plan to achieve this ambitious objective. There were no existing benchmarks to study, nor industry best practices to emulate — Ørsted ‘s goal surpassed anything seen in the sector before. This bold ambition compelled everyone within the company to confront harsh realities about its business model, including the urgent need to address climate change and the finite nature of fossil fuel resources, and reshape its business portfolio.

Henrik Poulsen, who served as Ørsted ‘s CEO from 2012 to 2020, leveraged this bold ambition to rally a workforce initially skeptical of such monumental change, inspiring them to embrace creative solutions. The company sold off its oil and gas business, phased-out the use of coal for power generation, and acquired Deepwater Wind and other assets to expand its position in offshore wind.  Ørsted set a timeline of 30 years to achieve its goal, yet remarkably accomplished it in just a decade.

3. Build transformation into the company’s operating rhythm.

To thrive in today’s fast-paced environment, leaders must seamlessly integrate transformation into the company’s operating rhythm. This involves incorporating change initiatives into existing governance processes and forums, ensuring that evolution becomes a natural part of running the business.

By embedding transformation initiatives into the operating rhythm, leadership can ensure alignment between strategic goals and day-to-day activities. This integration fosters a holistic approach to the change effort, enabling organizations to respond swiftly to evolving market trends, customer demands, and competitive pressures. When transformation becomes part of the company’s operating rhythm, accountability for driving change is distributed across all levels of the organization.

Take the remarkable evolution of Dell Technologies. When Michael Dell, in collaboration with Silver Lake Partners, took his company private in 2013, he recognized the imperative for broad-based transformation. This decision came at a time when many were predicting the “death of the PC.” Proving these skeptics wrong necessitated the transformation of Dell Computer into an enterprise technology powerhouse, known today as Dell Technologies.

As we describe in our recent HBR article, Michael Dell has embedded transformation into his company’s operating rhythm. Following the privatization, the executive leadership team (ELT) instituted the Dell Management Model (DMM), supplanting the existing strategic planning, resource allocation, and business performance review processes.  At the heart of the DMM lay the Dell Agenda — a backlog of strategic imperatives essential to the company’s transformation. Addressing items on the Dell Agenda became a primary focus of ELT meetings and other leadership sessions. The Dell Agenda remained dynamic; as issues were resolved and integrated into the company’s strategy, new issues were added, ensuring the transformation remained an ongoing process. As the DMM became ingrained in the company’s operating rhythm, transformation became an integral part of daily leadership practices at every level of the company.

The results speak volumes. By the close of 2023, the ongoing transformation at Dell had yielded a 10-fold return on Michael Dell’s initial investment in 2013. Our analysis of total shareholder returns for large companies reveals that this number surpasses the performance of any other public company with revenues exceeding $30 billion.

In today’s fast-paced world, business transformation can no longer be treated as a one-time project. Continuous change necessitates ongoing transformation. The traditional approach of “unfreeze, change, refreeze” must give way to a new mindset: “rethink, reshape, repeat.” It’s through this continuous process of transformation that companies can truly realize their full potential.

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Is it time for System 3 thinking by humans? Just two months after its launch in late 2022, ChatGPT reached 100 million monthly active users. Along with other advanced language models, it quickly started to encroach on territory traditionally exempt from automation, such as tasks requiring creativity, intuition and decision-making. So, what does this mean for managerial work? We predict that the blend of artificial intelligence (AI) and  human thought will remain indispensable – at least for now – but with an unexpected twist. Far from being limited to grunt work, AI will be entrusted with some of the more creative and intuitive components of decision-making, tasks viewed as fundamentally human . It won’t replace managerial work but rather reshape it. Two styles of thinking: fast and slow In his best-selling book,  , Nobel laureate Daniel Kahneman brought to the mainstream the  Thinking, Fast and Slow concept of two distinct modes of human thought. “System 1” thinking is fast, intuitive, instinctive, yet prone to mistakes in unfamiliar circumstances. “System 2” thinking, on the other hand, is slow, intentional and better able to conquer new situations by applying rules that it has learned in the past. We generally approach tasks intuitively, only engaging System 2 if something in the environment suggests that thinking harder might be required. Though our System 1 improves naturally from the experiences and feedback we amass over time, we make conscious efforts to improve our System 2 thinking, for instance, through formal education to develop our logic. With sufficient practice, the acquired skills of System 2 become embedded in the intuition of System 1, in a sort of virtuous cycle. AI’s evolution has taken a different path. Its starting point is logic, akin to System 2 thinking in humans. Rapid logical computation is what allowed IBM’s DeepBlue to triumph over Grand Chess master Garry Kasparov in 1997. However, the advent of machine learning brought forth a novel variant of machine intelligence. It demands extensive training on data, after which it operates almost instantly. While notoriously opaque, its workings are remarkably effective on average. We argue that this mirrors humans’ System 1 thinking: Human intuition is built on years of experience but operates almost instantly. This development allowed Alphabet’s AlphaGo in 2016 to triumph over Lee Sedol, the top player at Go, a game that humans were supposed to always dominate because intuitive play is crucial to success. Combining thinking styles across humans and AI How will humans work alongside AI? The fundamental premise of most narratives is that tasks can be divided into subtasks, which humans and machines undertake based on their relative strengths. This line of reasoning echoes enduring principles behind specialisation, outsourcing, offshoring and strategic alliances. We propose a shift in focus from task specialisation to a specialisation by thinking type. If machine intelligence is capable of intuitive reasoning (System 1) on a superhuman scale, and if existing computational systems already outpace humans in logical reasoning (System 2), where does that leave room for humans? We contend that the answer is in the integration of these two systems. Though the ability to pivot between System 1 and System 2 has long been emphasised in decision-making  research , with debate over how well  humans are able to  do so , it is not generally seen as its own system. Yet if System 1 and System 2 tasks are carried out by AI, this pivoting between the two – call it System 3 – is where human intelligence comes into play.  As it stands, humans hold both an absolute and relative edge in this System 3 form of thought. They can identify when a process needs to be changed and select between different options and analyses. The durability of this advantage remains uncertain, as advancements in computer science seem poised to combine traditional computation with machine learning. However, it’s evident that humans will remain the sole masters of System 3 thinking for a substantial window of time. What does it mean for managers? To bring this idea to life, let’s consider a classic managerial dilemma: “Which project should I invest in among several options?” Some process of funnelling is necessary to go from a large set of projects to a smaller set that bear closer examination. The projects could be candidates for recruitment, or potential partners for a strategic alliance or takeover targets. Conversely, creating a large enough list of initial candidates (ideation) is also important to ensure a good coverage of the possibilities. Given the vast data associated with various projects, some of which may not be easily processed, some form of intuition or judgment can be helpful, particularly under time pressure. This is where System 1 thinking kicks off the process for most managers. Their years of experience in a context may have generated insights that operate sub-consciously, producing what we think of as managerial intuition. But what if, rather than relying solely on their gut instinct for the initial selection, managers enlisted a large language model (LLM) to sift through the myriad of initial options and generate a shortlist of feasible alternatives? A list generated by an LLM could be both larger and begin with a larger candidate pool. This shortlist could then undergo a rigorous review using systematic, logical procedures that can be thoroughly checked and explained. This is within the purview of well-trained, methodical managers using System 2 thinking, as well as traditional rule-based AI systems. But here too, the scale and computational power of AI offer advantages. Checking facts, conducting analyses, ranking candidates on multiple criteria, clustering them in higher dimensional spaces – these are all procedures that machines can and have been doing for a long time. However, this process is iterative and doesn’t end with one cycle. The strict application of rules to the shortlisted candidates might expose flaws in both the shortlist and the applied rules. The ability to identify these shortcomings, and fine-tune both the shortlist creation and the selection process, exemplifies quintessential System 3 thinking. We believe that this form of thinking is where human managers should invest their skill development efforts. It presents an exciting fusion of human cognitive flexibility in harnessing “machine precision” with “machine intuition,” maximising the strengths of both and mitigating their weaknesses. The metaphorical image we have is that of a human charioteer guiding the twin steeds of machine precision and machine intuition, yoked together to produce rapid progress in decision-making. Edited by: Isabelle Laporte View Comments Leave a Comment

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Not every organization is eager to solve its problems. In the absence of an obvious performance crisis, leaders often clutch onto the status quo and underweigh its risks. This type of resistance to change can show up in many forms, some of them hard to decipher. Here are 10 signs your company may be trying to slow you down, from wherever you’re trying to lead in the hierarchy.

1. A task force is being assigned to the problem.

A small, intrepid team of reformers is one thing; indeed, it’s a thing we often recommend to accelerate action. Most task forces, it turns out, do not fit this profile. How can you tell? If you’re being asked to rely on a structure or process that lacks status, legitimacy, or decision rights — or the sponsorship of someone who has any of the above— then it’s unlikely to help you make a difference.

2. You’re being thanked for your time and effort.

If you suspect you’re being indulged and dismissed, then you probably are. By the way, this is not the same thing as being disagreed with, which is a perfectly acceptable response to wherever your diagnostics lead you. Your obligation as a changemaker is to make the persuasive case for your ideas. Your colleagues’ obligation is to engage with them in good faith, not to uncritically agree with you.

3. People doubt whether the organization (really) has a problem.

Be prepared for some of your colleagues to push back on your premise that the company has a problem. Hard truths are, by definition, difficult to face; this is particularly true for data that suggests cultural issues such as a lack of full inclusion. Stay strong. Be fluent in the evidence you gather today that the problem truly exists — and in resonant stories about the price the organization is paying for it.

4. You’re asked to respond to the grave concerns of unidentified critics.

These exchanges often start with some variation on, “As your friend, I think you should know what people are saying.” This is usually a tactic to keep you in check rather than empower you with helpful information. Don’t take the bait and react to rumor and hearsay. Encourage your critics to reveal themselves so that you can engage directly with their concerns, which may very well be valid. Collaboration happens in daylight.

5. The specter of “legal issues” is being invoked.

The antidote to this one is to work directly with the legal team, which is often made up of people who are far more creative, flexible, and solutions-oriented than the detractors who are using their name. Lawyers are rarely the risk-intolerant killjoys they’re made out to be by non-lawyers, so partner with them early.

6. Your colleagues point out all the other problems that need to be solved.

This response assumes there’s some kind of measurable limit on a firm’s capacity to absorb positive change — and you’re getting dangerously close to that line. People tend to underestimate their company’s capacity to adapt to a better reality (as well as the true cost of continued inaction). The problem you surface deserves a rapid response that reflects the frustration, the mediocrity, and, in some organizations, the real pain of the status quo.

7. You keep hearing about a future state where the conditions for change will be much, much better.

This may be the most common expression of resistance we see: the fantasy that it’s going to be easier to change things at some point in the future. In our experience, this is almost never the case, and the opposite is usually true. The clarity and momentum you have right now are tremendous assets, but they’re also perishable ones. In most cases, the “fierce urgency of now” wins the day, particularly when the success and well-being of the people around you are on the line.

8. The timeline for action is growing.

This is another common delay tactic, a proposed antidote to the concerns expressed in items 6 and 7. Your diagnosis is embraced at a conceptual level, but the proposed timetable for change is long and vague. Treat this development as an existential threat. When it comes to solving mission-critical problems, the right time to take action is now.

9. Your colleagues think they can wait you out.

Management thought leader Earl Sasser calls this “kidney stone management” — the assumption that this, too, shall pass. Make it clear that you’re not going anywhere, preferably with a smile. If you’re not the boss, then show up in her office with a cup of coffee, just the way she likes it, every morning until you get her to engage. That move, by the way, has never failed us.

10. You keep hearing, “We’ve already tried that.”

The company may have already wrestled with some version of the problem you’ve surfaced, with little to show for it beyond a legacy of frustration and cynicism. If so, do your homework and learn what you can from whatever went wrong. Regardless, context changes, including the very material context of your willingness to lead on these issues. You haven’t tried before, which is going to make all the difference.

This content was originally published here.

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The days of operations that pursue speed, quality, and cost efficiency above all else have come to an end. Today, the objectives that manufacturers must pursue to ensure competitiveness have evolved. Sustainability, long seen as incompatible with these traditional goals, is now a strategic imperative.

This raises a question for manufacturers: How can they make products and production processes more sustainable while remaining profitable?

There is a vibrant ecosystem of companies that have started to break this trade-off with deep tech: the innovative use of emerging physical technologies enabled by digital technologies to solve large-scale problems. As they try to increase profitability and sustainability, it’s time for manufacturers to use deep tech more widely and at scale.

A confluence of factors is converging to make this possible. First, the urgency to deliver sustainability is on the rise, driven by net-zero commitments and stakeholder demands. Second, financial and regulatory support for developing green technologies has never been greater. The Inflation Reduction Act in the United States and the European Green Deal in the EU are prime examples of this. And third, potentially transforming technologies are moving out of the lab and into the real world, with many demonstrating success at scale. These shifting dynamics present opportunities for manufacturers such as creating or moving to new markets or securing a head start in addressing supply scarcities, especially for green materials.

To capture these opportunities, break their trade-offs, and deliver sustainability, leaders should apply deep tech across their products as well as their processes. They will also need to consider how to collaborate with the innovation ecosystem, especially with younger deep tech firms.

Deep Tech Can Enhance the Profitability-Sustainability Profile of Products

With deep tech, manufacturing companies can reimagine the very essence of their products. Instead of offering incremental enhancements or partial substitutions — which often don’t yield significant benefits and may even be counterproductive — it’s essential for businesses to consider more fundamental product updates.

Manufacturers can develop and use new materials that are delinked from legacy materials’ supply chains or environmental footprints. Synthetically produced biological materials can act as sustainable building blocks or as drop-in substitutes. Market segments in which a sustainable manufactured product translates into a new relevant product claim are more likely to command a green premium, for example in the personal beauty space.

Consider Geno, a San Diego-based company that has developed plant-based alternatives for palm oil and fossil-based beauty, cleaning, and apparel ingredients based on its synthetic biology platform. The carbon footprint of Geno’s synthetic biology–based palm oil is up to 50% lower than conventional palm-derived ingredients. By offering traceability and lowering the reliance on traditional palm-oil supply chains, its use also drives supply chain transparency and resiliency. Similarly, Modern Meadow has developed a synthetic biology–derived collagen with unique bioactive effects. Specialty ingredients supplier Evonik has partnered with Modern Meadow to bring this to scale across the personal care and cosmetics sector.

Deep tech can contribute to both supply and price stability of raw materials, which are key for a manufacturer. Switching to renewable or more abundant materials answers both of those needs and can even shorten supply chains, reducing the complexity and carbon footprint of their logistics network. Niron Magnetics, for instance, has developed and is scaling up production of high-performance magnets. Their magnets are made from widely abundant nitrogen and iron, a viable alternative to alleviate the rare earth metals supply chain, which is concentrated in a few sources and processed in only a few plants worldwide. While initially targeting applications such as sensors or loudspeakers, these magnets could soon contribute to making EV motors, such as those from Tesla, rare earth-free.

Another way to enhance the profitability-sustainability profile is to change the performance profile of a product, for example by extending its life or endowing sustainable product properties that aren’t possible with conventional materials.

Basilisk, for instance, has developed a bio-based drop-in treatment, made of engineered microorganisms using syn-bio techniques, that can be mixed in with mortar or applied to existing concrete structures. This repair system uses limestone-producing microorganisms to eliminate the need for crack repair and maintenance. Depending on the product, this can lower CO₂ emissions by 30–50% versus conventional concrete by extending the lifetime of the concrete and reducing the amount of steel needed for reinforcement. If applied at scale, this could mean a reduction of global CO₂ emissions by more than 1.5%, since currently more than 6% of global CO₂ emissions come from concrete.

Deep Tech Can Make Sustainable Process Economics More Compelling

It’s not just the products themselves that create constraints, but also the processes used to make them.

Boston Metal reimagined the conventional carbon-intensive steelmaking process by inventing a new electrolytic process and associated apparatus able to run on 100% renewable electricity and emitting no CO₂. The molten-oxide electrolysis technique, made possible by new electrode materials that can withstand high temperatures and are stable in the presence of oxygen, has the potential to reduce the carbon footprint of the steel industry, currently responsible for as much as 8% of global GHG emissions.

Deep tech can enable innovation on processes to deliver step-changes in energy efficiency. Combined with new production enablers — like a shift from linear assembly lines to small, cellular factories — manufacturers can increase flexibility, introduce optionality for local manufacturing, and transition to a lower carbon footprint production network.

For instance, Syzygy Plasmonics’ scalable photoreactor combines LED light and photocatalytic nanoparticles to conduct chemical reactions, made possible by combining advancements in architected materials and innovation in photonics. Because the reactor focuses the energy from light exactly where it is needed, conversions become much more efficient and sustainable than a conventional reaction. By removing the need for combustion in the emissions-heavy chemical industry, Syzygy Plasmonics aims to avoid one gigaton of CO₂ emissions by 2040.

Lastly, manufacturers can develop and use entirely new processes, like new additive manufacturing or precision fermentation techniques, that enable the realization of conventionally unmanufacturable sustainable products. With deep tech, manufacturers can engineer processes that reduce the number of energy-requiring transformations by building from the atom up.

Geno has successfully scaled their synthetic biology technology and farm-based supply chain to tackle large ingredients markets like butanediol (BDO), an industrial chemical, and nylon critical to the sustainability goals of its brand partners like Unilever, L’Oréal, and Lululemon.

Operations executives should look for incentives, subsidies, and other opportunities created by recent government policies. In the United States, the IRA alone covers $71 billion in stimulus for advanced manufacturing, industrial facilities, and energy efficiency.

Build an Innovation Ecosystem

Deep tech operates at the intersection of several emerging technologies, such as synthetic biology and 3D printing. It addresses complex issues that cross different scientific fields. It needs to serve real market needs, while often demanding significant funding and extended development periods.

Traditional companies may find it difficult to navigate this swiftly changing landscape. And few, if any, will have the tools and capabilities to do it alone.

To start making progress, manufacturers should seek to collaborate with partners — through commercial alliances, incubators, or ecosystems that can even include governments and academia, for example. Counterintuitive as it might seem, younger deep tech firms and incumbent manufacturers should not compete but rather collaborate. Established manufacturers can expect to get access to novel technologies and specific know-how that enables them to apply deep tech in their products and processes. When partnering with deep tech startups, manufacturers should look for solutions that are most compatible with their domain and build on the unique assets and capabilities they can leverage to create value for their joint efforts.

Chicago-based biotech company LanzaTech, for instance, has developed a microbe-based technology that uses residual gases containing carbon monoxide and hydrogen as feedstock to produce bioethanol. In partnerships such as with BASF, the world’s largest chemicals manufacturer, LanzaTech is working to convert the carbon in the exhaust gases from industrial processes, such as steel making, into sustainable raw materials for various industries. By capturing and utilizing carbon in this way, the technology can help reduce the carbon emissions of many manufacturers and associated supply chains. BASF thus improves the sustainability of their customer’s resource use and helps create demand for LanzaTech’s technology.

This example is just one of many. When looking to collaborate with deep tech startups, manufacturers can consider partnerships, joint ventures, or M&A approaches. The optimal engagement strategy and suitable partner will depend on the specific constraints a manufacturer faces in their domain, the need to complement missing in-house capabilities, and desired ambition level or scale.

To achieve commercial success activated by deep tech at scale, manufacturers should combine their product or process innovation with foundationally different value chains. Our extensive research on business ecosystems shows that value chain transformations, while challenging and often requiring extensive collaboration, will unlock enormous value. Manufacturers can leverage their unique capabilities and existing relationships to influence suppliers and distributors to transition to solutions or infrastructure that will support the new processes and products or help navigate the regulatory environment.

Consider the fashion industry, which accounts for around 4% of greenhouse gas emissions globally. Sustainability innovation efforts, which need to be effective throughout complex global fashion value chains, are particularly challenged in the current economic environment. Companies are pooling risk by collaborating cross-industry through a consortium like Fashion for Good. Fashion for Good has established an innovation program that connects brands, retailers, manufacturers, and investors to develop and scale disruptive solutions within the textile industry.

The members of this industry-wide initiative are looking to realize collective goals of making sustainable change happen in areas such as materials, processing, or end of use — but are unable to do it individually. The group has, for example, launched the Renewable Carbon Textiles Project, focusing on producing fibers from polyhydroxyalkanoates (PHA) polymer fibers, which could offer a sustainable, biodegradable alternative to traditional, fossil-based fibers, with the potential to significantly cut emissions in fashion’s value chain while simultaneously allowing manufacturers to fine-tune fabric properties. The project combines expertise of innovators like Danimer Scientific, Bio Craft Innovation, Paques Biomaterials, Helian Polymers, and Newlight Technologies with other players in the industry who will test the solutions, provide funding, and help scale the solution.

The examples of deep tech ventures and collaborations with manufacturing incumbents show that deep tech can already help companies deliver on the operations paradigm, advancing a common sustainability agenda without sacrificing long-term profitability. The real challenge is not the technology itself, but the inertia of long-established capital investments and time-tested manufacturing approaches — overcoming this is no small feat.

Given the pressing urgency for sustainability, manufacturers need to act swiftly and decisively. Manufacturers that leverage their unique capabilities and succeed in identifying the right engagement models can use deep tech as their critical driver of competitive advantage — and now is the time to get started.

This content was originally published here.

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Let’s say you’re making spaghetti marinara for dinner. Sauce from a jar is perfectly fine. But what if you buy your tomatoes and basil from the farmers market to make your own sauce? Chances are it will taste a lot better. And what if you grow your own ingredients from your garden and make your own fresh pasta? A whole new level of deliciousness.

Just as better ingredients can make for a better dinner, better inputs into a generative AI model can make for better results. These inputs are called prompts, and the practice of writing them is called prompt engineering. Skilled prompt engineers design inputs to interact optimally with other inputs in a generative AI tool. These inputs help elicit better answers from the AI model, meaning the model can perform its tasks better, such as writing marketing emails, generating code, analyzing and synthesizing text, engaging with customers via chatbots, creating digital art, composing music, or any of the other hundreds, if not thousands, of current applications.

Let’s look at an example. Say you want a generative AI model to help you promote an upcoming financial-planning workshop with the CFO of your organization, Acme Inc. You provide a generative AI chatbot with a simple prompt: “Write a social post advertising a class.” Here’s the output:

**Elevate Your Skills: Join Our Class!**
Unlock new knowledge, connect with experts, and embrace growth. Limited spots available—reserve yours now!
#LearnWithUs #SkillBuilding #ClassTime

It’s pretty generic and unlikely to generate much interest.

Let’s try again. This time, you make your prompt a little more specific: “Write a short social post advertising a financial-planning workshop with the CFO of Acme, Inc.” Here’s what the model gives you this time:

**Master Your Finances with the CFO of Acme, Inc.!**
Get ready to take charge of your financial future! Join our upcoming Financial Planning Workshop featuring the CFO of Acme, Inc. Gain insights, strategies, and expert advice to secure your financial success.
Limited spots available—don’t miss this exclusive opportunity. Reserve your seat today and pave the way to financial freedom!
#FinancialPlanning #ExpertAdvice #SecureYourFuture

It’s clear that the more specific output has a greater chance of achieving the result you’re after. By creating a more detailed, specific request to the AI chatbot, you’ve just engineered a prompt.

Generative AI has an important role to play in the future of business and society (as well as, maybe, helping you promote any upcoming workshops you may be involved in). But where does prompt engineering fit in? Read on to find out.

What is generative AI?

First things first: a refresher on generative AI. Generative AI models are applications typically built using foundation models. These models contain expansive artificial neural networks, inspired by the billions of neurons connected in the human brain. Foundation models are part of what’s called deep learning, which refers to the many deep layers within neural networks. Deep learning has powered many recent advances in AI—things you’re probably already using, like Alexa or Siri—but foundation models represent a significant evolution within deep learning. Unlike previous deep-learning models, foundation models can process massive and varied sets of unstructured data. AI that is trained on these models can perform tasks such as answering questions and classifying, editing, summarizing, and drafting new content.

How will generative AI affect the workforce?

McKinsey’s latest research suggests that generative AI is poised to boost performance across sales and marketing, customer operations, software development, and more. In the process, generative AI could add up to $4.4 trillion annually to the global economy, across sectors from banking to life sciences.

The breakthroughs powered by generative AI will also change the workforce. One of generative AI’s strengths is that it can help nearly everyone with their jobs. This is also one of the technology’s greatest challenges. McKinsey estimates that generative AI and other technologies have the potential to automate work activities that absorb up to 70 percent of employees’ time today. This is largely due to generative AI’s ability to predict the patterns found in natural language. This, in turn, means that generative AI stands to have more impact on knowledge work associated with occupations that have higher wages and more educational requirements. And this change will likely happen fast: McKinsey estimates that half of today’s work activities could be automated between 2030 and 2060. That’s roughly a decade earlier than our previous estimates.

These developments will mean big changes in the labor market. Generative AI could enable labor productivity growth of up to 0.6 percent annually through 2040—but that all depends on how fast organizations are able to adopt the technology and effectively redeploy workers’ time. Employees with skills that stand to be automated will need support in learning new skills, and some will need support changing occupations.

Are organizations already hiring prompt engineers?

Organizations are already beginning to make changes to their hiring practices that reflect their generative AI ambitions, according to McKinsey’s latest survey on AI. That includes hiring prompt engineers. The survey indicates two major shifts. First, organizations using AI are hiring roles in prompt engineering: 7 percent of respondents whose organizations have adopted AI are hiring roles in this category. Second, organizations using AI are hiring a lot fewer AI-related-software engineers than in 2022: 28 percent of organizations reported hiring for these roles, down from 39 percent last year.

If organizations are hiring prompt engineers, does that mean existing employees will be pushed out?

Prompt engineering is likely to become a larger hiring category in the next few years, but organizations also expect to reskill their existing employees in AI. Nearly four in ten respondents reporting AI adoption expect more than a fifth of their companies’ workforces to be reskilled, whereas only 8 percent say the size of their workforces will decrease by more than a fifth.

How might prompt engineering help organizations—say, banks—serve clients more efficiently?

As just one example of the potential power of prompt engineering, let’s look at the banking industry. Banks have plenty of value to gain from generative AI. McKinsey estimates that generative AI tools could create value from increased productivity of up to 4.7 percent of the industry’s annual revenues. That translates to nearly $340 billion more per year. Prompt engineering has a role to play in helping banks capture this value. Here’s how.

Let’s say a large corporate bank wants to build its own applications using generative AI to improve the productivity of relationship managers (RMs). RMs spend a lot of time reviewing large documents, such as annual reports and transcripts of earnings calls, to stay up to date on a client’s priorities. The bank decides to build a solution that accesses a generative AI foundation model through an API (or application programming interface, which is code that helps two pieces of software talk to each other). The tool scans documents and can quickly provide synthesized answers to questions asked by RMs. To make sure RMs receive the most accurate answer possible, the bank trains them in prompt engineering. Of course, the bank also should establish verification processes for the model’s outputs, as some models have been known to hallucinate, or put out false information passed off as true.

This isn’t just a hypothetical example. In September 2023, Morgan Stanley is set to roll out an AI assistant using GPT-4, with the aim of helping tens of thousands of wealth managers quickly find and synthesize massive amounts of data from the company’s internal knowledge base. The model combines search and content creation so wealth managers can find and tailor information for any client at any moment. A European bank developed a generative-AI-based environmental, social, and governance virtual expert. The model answers complex questions based on prompts, identifies the source of each answer, and extracts information from pictures and tables.

In these examples, hypothetical and otherwise, the better the prompt, the better the output.

This content was originally published here.

The post What is prompt engineering? appeared first on Matt Dallisson Global Executive Search | Leadership Consulting.

The screenwriters’ strike that began last May, and the actors’ strike that followed in July, highlighted an employment model that has given Hollywood a highly flexible workforce for decades: The vast majority of people on a set are not studio employees but independent contractors or employees of other firms. Take, for example, the people who worked on this year’s Oscar winner for best picture, Everything Everywhere All at Once. In addition to the 42 actors; the 49 artists, set dressers, and painters; and the hundreds more who provided art, music, transportation, writing and editing, costumes, casting, sound, and stunts, the rest of the crew came from six production companies, three special-effects firms, and 28 other organizations.

This model has begun to extend far beyond Hollywood. Our research shows, for example, that in early 2020, on average, employees made up only 62% of the teams responsible for transformation initiatives at 404 global technology, financial services, health care, and life sciences companies with at least $1 billion in revenue. The rest came from staffing or consulting firms or were independent contractors. By the fall of 2022, with nearly three years of the pandemic having put companies into transformation overdrive, those numbers were 55% employees and 45% non-employees.

Judging from a study of 1,005 companies we did in the spring of 2023, we predict that the average number of employees on corporate transformation teams will fall below 50% by 2030, for four reasons:

The Pandemic and Project Overload

Over the past three years, the Covid-19 pandemic has propelled mission-critical corporate transformations, such as reorienting supply chains suffering from acute component shortages, installing new financial systems to track revenue and expenses faster and more accurately, and restructuring business processes for data cleanliness. The resulting pressure on full-time employees — and sometimes the absence of key knowledge internally — forced companies to hire outsiders with valuable and hard-to-find skills. “E-commerce, digital data security, data privacy—those are all new for us,” the comptroller for the Asia-Pacific region of a U.S.-based luxury goods company told our researchers. “Local statutory requirements are getting more complex, as well as countries’ political issues. No one organization will know it all.” And large global companies that are running dozens of initiatives at once may have too few internal project leaders to go around.

Successfully staffing and running transformation initiatives with the right mix of insiders and outsiders requires what we call a “dynamic workforce model.” Unlike a static model, which assigns only employees to a critical project team or outsources the work altogether, the dynamic version puts insiders and outsiders on an equal footing. Our study found that the best executors of these initiatives, which were 15% of the 404 companies surveyed, were far more likely to use a dynamic workforce model than the worst executors (24% of the survey base). The best executors’ companies also substantially outperformed the worst executors financially, with an 63% average increase in market capitalization between 2017 and the fall of 2022, compared with an average 24% decline for the worst companies.

But this model also presents steep challenges, including creating a cohesive team culture, building trust among people who don’t know one another, and empowering leaders to keep things on track. Project sponsors and project leaders must address those challenges to succeed.

Creating a One-Culture Team

When a company fields its own team to run a big transformation initiative, or brings in a consulting firm to execute it, everyone on the team is well versed in the organization’s values and beliefs, the behaviors that are permissible, and who are the powerful internal “players.” But if the team includes outsiders, they are like tourists in a strange land. They can’t be expected to understand the full context of the initiative the way insiders do, no matter how well they’ve been briefed. A thorough onboarding — one that informs team members of both the project mechanics and the company culture as well as project steps, roles, deliverables, and tools — will convey the values, beliefs, and behaviors of the company.

The gene therapy firm Asklepios BioPharmaceutical Inc. (AskBio) gets this. Acquired by Bayer in 2020, AskBio has a number of promising therapeutics in development, including for Parkinson’s disease and congestive heart failure. “It’s super important to create a unified team culture,” said Guru Ramamurthy, chief financial offer at the 800-employee firm based in Research Triangle Park, N.C., and CFO of Bayer’s U.S. business. Tapping contractors and consultants has been an important component of rapidly scaling up its infrastructure and capabilities. “Maintaining momentum is critical to execution,” Ramamurthy says, especially since the Bayer buyout. But during a time of rapid growth and change, hiring employees to fill key team roles can be time-intensive and may prove unsuccessful. It can take about three months to discover that a new employee isn’t a good fit, and retraining a replacement can take from six to nine months. Meanwhile, demands and focus may change, altering role requirements. Supplementing with outside experts offers greater operational flexibility and agility.

Since the pandemic began, anywhere from 15% to 30% of AskBio’s project team members have been external, depending on the initiative. “A key area of success is how well we blend our employees with the experts we get from various vendors,” Ramamurthy says. He believes that contractors and consultants, particularly those who work remotely, must be treated as equally valued team members. AskBio provides them with access to information and company tools to enable communication across blended teams.

Once an AskBio initiative has been completed, external team members often ask about company job openings. When Ramamurthy hears this, he knows they didn’t feel like outsiders on their teams. “This is the kind of project experience you want to give every contributor,” he says.

Making the Work Personal, Not Transactional

On a team composed of employees from one company, the members have a chance to know one another personally — their lives outside the job, their passions, their families, their ups and down at work. But outsiders on a team don’t have that experience. And as more people are hired from outside a company, insiders may detach emotionally from the work, especially if they are involved in several critical initiatives. “It’s easy to see people just as numbers or resources,” a chemical company’s chief strategy officer explained.

Remote team members have an even harder time establishing strong personal relationships. A finance executive at a multibillion-dollar medical-products manufacturer told us how difficult it was to manage remote team members — especially when they aren’t employees, but even when they come from other business units. “We find trust, understanding, and engagement are sometimes missing with them,” he said.

Building trust among transformation team members is crucial to success. Consider, for example, a project manager who must promise anonymity to a team member who points out a mistake that needs correcting. “You have to empower people to speak up,” the finance executive said. And strong commitment to the well-being of a hard-to-replace team member may be necessary to pull that person through a bad patch in the life of a transformation initiative. The executive told us that his company’s project leaders are encouraged to make personal visits when relationships with team members appear strained.

An Asia-Pacific regional comptroller watched her Shanghai team members get caught in pandemic lockdowns after 2020. Several times she arranged grocery deliveries to their homes and access to mental and physical health resources. “You can’t expect the usual high performance under those conditions,” she told our researchers. Project leaders must use empathy in such cases to motivate teams. The company’s projects — of which she was juggling 19 — have been long, making it hard to keep team members motivated and focused. Awarding bonuses and other recognition for good work has helped, she said.

Empowering the Project Leader

Among the companies we studied, the best at executing critical transformations had better project leaders than the worst and were more likely to give those leaders the authority to decide what skills were needed on their team and who had them. Some 85% allowed those leaders to replace team members when necessary, whether from inside or outside, whereas only 70% of the worst gave project managers the same authority.

The medical-products company executive told us that empowering project managers increases the chances that they’ll stay with the organization. “Having strong project leaders gives an organization a large competitive advantage,” he said. “There are always projects to be done. If you don’t have a strong project capability, you cannot keep up with your peers.”

At a time when companies must move faster to stay competitive, bringing in outsiders for key roles in transformation initiatives has become a reality. But insiders and outsiders must be managed as one effective, cohesive, motivated, and collegial team. Movie producers and directors know how to do this. Companies in industries that are a long way from Hollywood must follow suit.

This content was originally published here.

The post Creating a Cohesive Team for Corporate Transformation Projects appeared first on Matt Dallisson Global Executive Search | Leadership Consulting.

It wasn’t that long ago that AI was considered the domain of elite experts or data scientists. Companies spoke effusively about its potential to transform business, but only a fraction of their employees had access to it. With generative AI, the game has changed — suddenly everyone is a potential programmer thanks to tools like OpenAI’s ChatGPT, Google’s Bard, or Anthropic’s Claude 2. But as we travel these technological leaps forward, there’s still a fundamental, longstanding capability every organization will need to realize AI’s true potential: judgment.

That idea of judgment in the age of AI was a central tenet of the work of our late colleague and visionary friend Alessandro Di Fiore. Alessandro believed in putting humans at the center and considered technology to be a way to help people augment their creativity, autonomy, and critical thinking. As a frequent contributor to HBR (and the former chairman of Harvard Business Review Italia) he often reflected on how innovation, leadership, and AI went hand in hand. And in this 2018 article, he argued that as AI gets more accessible to all employees, judgment would become as crucial as any technical skill.

Alessandro argued that judgment will be the real competitive advantage for organizations as AI systems rise to new common operating standards. But reinforcing skills alone won’t develop better judgment. Companies will need to radically rethink how they view and deploy judgment in order to adapt to the pace of change. Significantly, Alessandro saw three critical facets of judgment as crucial to this moment: 

This speaks to the idea of who has the authority or permission to exercise judgment.  As an early advocate of democratization of judgment, Alessandro knew that making good decisions was not something confined to the C-suite. As knowledge, data and technologies are more widely distributed, judgment must also be more widely distributed.

Companies need to understand how to leverage and scale generative AI, and trying to prohibit that access will ultimately be a futile effort. Making sure your workers have access to all the ways they can unlock the value in these tools is part of the transformation, of course doing so in a secure and controlled manner. This requires trust and communication. But leaders should consider that an increasing number of new use cases and practices are bound to emerge bottom up rather than stirred top down.

Exercise of Judgment

This speaks to the act or process of deciding or forming an opinion. Alessandro considered judgment as a continuous process rather than a single moment:

Judgment is not only exercised in the moment of making a decision assessing data and information. Judgment is broader than that and it starts with asking the right questions, framing the right problem, evaluating the broader context. Judgment is co-creative, it’s a journey.

This concept of judgment is even more true today. The rise of generative AI expands judgment beyond discrete decisions — it’s now a collaborative human-machine process.

With AI chatbots, the significance of contextual interactions is evident. Judgment emerges through integrated human-AI dialogue, not separated spheres. Our recent ChatGPT experiment on how generative AI can enhance 10 popular management practices is a case in point. In a meaningful dialogue with ChatGPT, we had to exercise our judgment ability before, during, and after the prompting, inputting the right context, crafting the best chain of prompts, and carefully interpreting recommendations.

The experiment confirmed Alessandro’s intuition: the ideal outcomes arise at the intersection of human and machine intelligence. Sound judgment’s future is this symbiotic co-creation process. Such a shift mandates a comprehensive people transformation and reskilling, arming workers with the judgment prowess essential for the AI era.

This speaks to the systems or processes in place to oversee or check decisions. The traditional methods of control are quickly growing out of date. Strict top-down oversight can stifle agility in this new paradigm. Yet totally unconstrained autonomy poses its own risks if ethical AI development and deployment principles are not ingrained across organizations.

The solution consists of two parts: first, building trust and responsibility in the system with a code of ethics for a fair, safe and sustainable usage and to prevent AI models from producing inaccurate information or generating responses that contradict your company’s values; second, providing training to users on how to set the right context for human-AI decision-making. That second part involves explaining appropriate boundaries for prompts and framing inquiries responsibly. Rather than micro-approving choices, leadership should focus on empowering workers with these skills at all levels.

In this vision, control transforms from bureaucratic gatekeeping to fostering collective responsibility. As Alessandro wrote, “Leaders have in first person a duty to set the right context and conditions to empower employees make more autonomous decisions with the help of data and technologies. Giving freedom is good. But helping them exercise their freedom is more crucial.”

Alessandro’s vision underscores the need for sound judgment as AI reshapes society. His legacy remains a constant source of inspiration as we work towards a future where humans and technologies collaborate seamlessly, fostering innovation and progress.

And the future remains ours to shape through vision, ethics, and responsible innovation.

This content was originally published here.

The post Good Judgment Is a Competitive Advantage in the Age of AI appeared first on Matt Dallisson Global Executive Search | Leadership Consulting.

To best understand generative AI (GenAI), look at it through bifocal lenses. Through the top lens, one sees the long view of big, looming issues, such as accuracy, privacy, and bias, as well as GenAI’s potential impact on “knowledge workers” and even economy-wide job losses and societal risks. While this view is important, we have heard over and over that it’s not helpful to executives and board members because it’s difficult to translate from the generic insight that millions of jobs will be impacted to what it means for their businesses today.

That is why we also recommend looking at GenAI through the bottom lens, and taking in what’s directly ahead. Here, leaders can find the immediate opportunities and threats from GenAI that firms face today. In taking this view, however, leaders may also need to adjust what they’re looking for.

Our case studies, based on our growing global community of over 3,000 GenAI practitioners, point to a new category of work, more precise and actionable than “knowledge work.” We call it WINS Work: the places where tasks, functions, possibly your entire company or industry are dependent on the manipulation and interpretation of Words, Images, Numbers, and Sounds (WINS). Heart surgeons and chefs are knowledge workers but not WINS workers. Software programmers, accountants, and marketing professionals are WINS workers.

GenAI has the potential to be power tools for WINS work. It can generate new prose, computer code, images, narration, music, and videos as well as ingest and summarize, critique, improve, and reformat almost any manner of document or analysis. Every WINS task, subprocess, and end-to-end process within your enterprise (and in many cases the entire enterprise) should be evaluated for potential leverage with GenAI.

How Urgent Is It for My Firm to Pay Attention to GenAI?

We believe the easiest way for companies to proceed is to ask themselves two simple questions:

Plotting your company on a 2×2 matrix, ask yourself where it falls. The top-level categorization can be accomplished by looking at the cost base of the firm overall and then by function, and estimating the percentage of work that is WINS work. For example, software development, customer service, marketing, and R&D are just four areas with high levels of WINS work. Here’s a closer look at each competitive position.

In the Crucible

Industries with a high percentage of cost in WINS work and that are highly digitized are “In the Crucible” and must understand and embrace GenAI immediately. Software, entertainment, professional services, financial services, education, and others are In the Crucible, because competitors who adopt GenAI rapidly will be better, faster, and cheaper.

The tools of GenAI provide new, creative answers and expressions for everything from resumes to marketing. Think of it like the power of photography in portraiture. While portrait painting was previously available only to the wealthy — an artifact created slowly by highly trained craftspeople — photography expanded the market radically, transforming the entire idea of a portrait (think about a selfie) and its economics. In the hands of great talent, photography became a new form of art. Likewise, software development, script writing, film production, tax filing, accounting, etc., are likely to be under significant cost pressure — and in time will undergo wholesale transformation. These activities may or may not become totally automated, but just as you’d never hire an accountant today who doesn’t use Excel, having GenAI capability may become table stakes for many tasks.

Holding a Lever

Companies that are “Holding a Lever” are able to gain advantage in cost, time, and quality, even if their cost base is not heavily weighted toward WINS work and their customer end product or deliverable is also not WINS nor digitized. For example, Moderna has recently required all employees be trained in GenAI tools. They believe it is a fundamental skill to drive WINS worker productivity even though their product is a molecule or treatment intervention. In our GenAI learning community, we have found GenAI is excellent for supporting bid preparation when responding to an RFP. Speed of sale is a critical performance variable for all, even those firms with few WINS workers. Many SG&A functions, key aspects of R&D, and even the entire end-to-end product development and supply functions can leverage GenAI.

Next in Line

The category of “Next in Line” in our framework may provide an opportunity to take tasks that are not digitized today and digitize them to create opportunity. For example, many leading home décor companies are investing in what they are calling their “digital front door,” enabling customer engagement in the identification and purchase process. GenAI will enable new levels of customization to help customers take actions to envision home furnishings in much more realistic and imaginative ways, leading to a better experience, greater customer uptake, and far fewer returns for those companies that are in the forefront.

In the Balcony

For companies that are “In the Balcony,” we see low digitization and limited WINS work as characteristic of the value creation process today. These are often industries with high amounts of low-skilled labor or, when high skill is involved, the nature of the skill is more in the creation of a physical product or service. Figure 2 gives a sample of industries in each of the four quadrants.

Unlike much CapEx and technology spend that takes several years to see a return, GenAI, even at this early stage, can often be accretive to EBITDA within the year it is adopted, because the near-term productivity boost is so compelling. Over time, these initiatives may birth strategic investment opportunities to create defensible assets or competitive moats.

Legal and Risk Issues

When using these tools, it’s vital to have human review of important and high-risk decisions. Today’s GenAI models can sometimes “hallucinate” and give wrong answers. Therefore, for now, staff should use it to augment — not fully automate — high-risk tasks. In time, innovators will figure out ways to improve and augment the core models to improve accuracy. In addition, if you are using one of the open access models like ChatGPT, have clear policies as to when you will allow data and queries to be part of their knowledge base and when will you keep it private.

Where to Start

To get your GenAI initiative moving, we suggest the following approach:

1. Get fully educated on the entire suite of GenAI tools that can drive productivity, change, and innovation in your company and industry. This is not a classroom exercise. This is more akin to swimming. You cannot learn to swim by listening to a lecture or watching a video. Roll up your sleeves, dive into the pool, and swim laps. Learn how the tools work and what they can do.

2. The board/CEO should appoint a cross-functional team to start at the task, subprocess, and process levels for practical experiments and report on progress.

3. Have a cross-functional business/technology/finance team examine whether and where you could generalize the lessons of your early experimentation. Discover where you “hold the lever.”

4. Perform a strategic review of the cost drivers in the WINS category and current digitization to assess how urgent it is to invest broadly in GenAI. Find out if your company is “In the Crucible.”

5. If you discover you are “In the Crucible” or “Holding a Lever,” create a test-and-learn strategy linked to a six-to-24-month program of improvement. We believe that’s how much time you have before competitive intensity increases in your industry.

6. If you are “Next in Line,” get smart on GenAI and begin to move toward digitization of all WINS work so you are ahead of the curve. You are next to be “In the Crucible,” and if you don’t transform your industry, someone else will.

7. For those “In the Balcony,” continue to learn. While things are less imminent for you, GenAI tools will come along to make your business easier and faster, much like Excel and Word replaced calculators and typewriters.

Looking at GenAI Through Bifocal Lenses

As we said in the beginning, we advocate that all business leaders should put on their GenAI bifocals and look not only into the distance through that top lens, but perhaps more urgently and importantly, look through the bottom lens to see what’s near. Historically it has taken about five to seven years for a disruptive firm to change industry models. Five years after Uber’s 2009 founding, taxi medallion prices in NYC peaked at about $1,000,000 in 2014. By 2017 their value was $250,000 or less and continued down.

Firms with heavy reliance on WINS work need to act today to fend off stiffer competition and to overcome disruptive competitors within 36 to 60 months. Don’t be caught with high costs, old processes, a data disadvantage, fleeing talent, and expensive capital.

This content was originally published here.

The post Where Should Your Company Start with GenAI? appeared first on Matt Dallisson Global Executive Search | Leadership Consulting.

Today’s volatile business environment continues to challenge organizations no matter their size, industry, or geography. Just a few broad trends tell the story: new digital entrants are gobbling up market share across industries; ecosystem-based strategies are gaining ground; and companies committed to environmental, social, and governance (ESG) criteria are increasingly attractive to employees and investors alike.

McKinsey’s research on corporate resilience shows that uncertain times call for leaders and companies to aggressively pursue the upside while also managing the downside. Successful leaders are transforming their businesses to meet this moment, going beyond cost cutting or updating the core business to reinvent the entire organization. This means not only improving financial performance but also focusing on customer experience, employee satisfaction, and positive societal impact.

However, years of McKinsey analysis reveal that successful transformations are difficult to pull off; in fact, a majority of them fail. Of those that do succeed, a tiny percentage deliver multiple times the value of median performers more quickly and sustain profitable growth over the long term. These fall into a class we call “transformative” transformations.

How can companies vault themselves into this exceptional group? Recent conversations with CEOs and other C-suite leaders show that they understand the need to transform and reinvent their businesses, but many are not sure about where to start or if the efforts they’ve already begun will actually create the value they’re targeting.

In this article, we look at the common traits of exemplary transformations, and at the value at stake for organizations that pursue this path. We reveal a new method of analyzing transformation progress, starting with asking the right questions, in ten categories, about both holistic performance and business reinvention. This approach can offer leaders an accurate baseline of where they are succeeding in their transformation efforts and where they have more work to do.

The pillars of successful transformations

Even among the small number of transformations that achieve their targets, there is a huge disparity of performance and outcomes.

Organizations that lead successful business transformations have five common characteristics. These companies do the following:

Transformative transformations go beyond these five actions, adding three more:

McKinsey analysis shows that these transformations represent just 5 percent of all transformations but deliver 4.5 times the value.

In the broadest strategic sense, transformative transformations achieve a well-managed balance between improving performance holistically and reinventing the business, excelling in both. Holistic performance is measured along five dimensions that reflect organizational priorities: financial performance, organizational health, talent and capabilities, customer focus, and ESG impact. Business reinvention involves pursuing an ambitious and significant shift in a company’s core operating model, portfolio, strategic moves, and digital and analytics capabilities.

Top performers understand that while a company can pursue outstanding holistic performance (by, say, doubling its bottom line), it still must develop its reinvention muscles to respond effectively in times of uncertainty. The opposite is also true: an organization can push to reinvent itself, but if it doesn’t deliver financial results, the transformation won’t be sustainable.

After committing to a transformation, the executive team should ask questions in ten categories about the holistic performance and business reinvention it is targeting to help the organization sharpen goals and carry them out rapidly (Exhibit 1).

A transformation tool for the C-suite

We’ve found that many CEOs and other C-suite leaders want to tackle big issues but need help with clarifying the most productive path forward. These issues include how to allocate resources effectively between new and existing businesses; how to determine whether a “big bang” scale-up or a sequenced transformation is more appropriate; how to go beyond managing ESG risks to creating value from ESG criteria; and how to handle large-scale organizational change in a way that is sustainable.

To address these challenges and others, we developed the Transformation Speedometer, a tool that analyzes¹The Transformation Speedometer was designed as an assessment tool that leaders can use to score their organizational maturity on holistic impact and business reinvention measures. The McKinsey Transformation Index (MTI) score provides a baseline for more detailed data-based analysis. a company’s transformation maturity according to its specific contexts and needs through a score we call the McKinsey Transformation Index. It’s rare for a company to outperform in every area of business reinvention and holistic performance; even the healthiest organizations have gaps. And even though leaders know their businesses better than anyone else, the results of this analysis can often surprise them, revealing relevant improvement opportunities.

Different companies, different scores, different paths

When following this method, company leaders give each of the ten categories cited earlier a rating from zero to ten. Out of a 100-point scale, companies that are lagging in their transformation progress rank below a 60; companies with solid-performing transformations rank from 60 to 80; and transformative transformations score above 80.

We’ve occasionally seen companies achieve a transformative transformation score higher than 80, with even scores across all dimensions. But the most common outperformers are companies that have a significant edge in a few dimensions—in which they are truly distinctive—while other areas have plenty of room for improvement. The way forward will depend on the organization and sector context, but in general, the scores can generate intense conversation among leaders about how to improve.

A global retailer had superior financial performance compared with peers and at-par customer experience and ESG practices. Although short-term financial results were strong, management realized that the business would be in danger if the company’s reinvention skills weren’t better developed. It had limited digital and e-commerce capabilities, which reduced its store-format efficiency and innovation. And it wasn’t even scratching the surface when it came to launching new businesses and increasing revenue streams. Its operating model was also very traditional, with a waterfall format that reinforced hierarchical decision making rather than flexibility.

Company leaders decided that processes, systems, and talent management all had to undergo a full remodeling, with a particular focus on investing in analytics and digital capabilities as well as talent.

In another example, an incumbent equipment manufacturer with exceptional holistic performance wanted to strengthen its business reinvention dimensions. The company realized that despite solid growth in recent years, it had to modernize by investing in analytics that would help it more quickly identify customer needs and respond to new trends. It increased its organizational metabolic rate by improving its digital capabilities that, in turn, expanded sales channels, shortened time to market, and improved customer service overall.

Turning scores into actions

This assessment can reveal several important insights for companies. First, it is almost impossible to excel in all ten categories. Making small improvements in all dimensions won’t lead to the best results; acting quickly and boldly in a few will.

Second, the assessment can support CEOs’ and management teams’ decision making as they define priorities for the months and years ahead. The speedometer does not measure management quality or depth; instead, it encourages frank discussion among senior managers about the maturity of their transformations—both in individual dimensions and at overall levels—and identifies potential blind spots.

Third, these discussions are critical for creating agreement among C-suite executives about how to allocate resources and how to define objective targets for measuring impact and assessing progress. For example, analytics often gets rated as a less developed category, with the chief data officer (CDO) struggling to get enough attention or resources. After the assessment, the CDO may be more empowered to articulate the sweeping benefits of data and analytics in value creation, customer focus, and other strategic priorities. In turn, the whole organization can shift to seeing analytics as a priority and create a plan to move forward using this rating system to monitor progress.

Finally, establishing ambitious goals and a shared vision helps maintain a high level of commitment to the work that needs doing.

Resilient businesses view moments of crisis or uncertainty as a time to transform themselves, generating more value in both growth and recovery cycles. As Ayrton Senna, the Brazilian car racing champion, once said, “You cannot overtake 15 cars in sunny weather, but you can when it’s raining.” By targeting holistic performance and business reinvention equally, companies greatly improve the odds that their transformations will create sustainable and disproportionate value. That’s a formula for ensuring that an organization thrives in the future, rain or shine.

This content was originally published here.

The post How bold is your business transformation? A new way to measure progress appeared first on Matt Dallisson Global Executive Search | Leadership Consulting.

“Show me the money!” Cuba Gooding Jr., playing Rod Tidwell, made those words a cultural touchstone in the movie Jerry McGuire. He was not just voicing his concerns about committing to a sports agent, played by Tom Cruise in this case; he was also questioning Cruise’s commitment.

Business leaders, shareholders, and board members have increasingly been saying the same thing — albeit using different words — when it comes to their company’s digital and AI transformations. While 89% of large companies globally have a digital and AI transformation underway, they have only captured 31% of the expected revenue lift and 25% of expected cost savings from the effort.

That track record begs some tough questions: Is all this digital effort worth it? Do I really need to lead my industry or is being a fast-follower a smarter strategy? Can I create digital and AI capabilities that give me a lasting competitive advantage or is this just the price of doing business in the modern age?

Until business leaders are convinced of the value and confident in how to get it, they are unlikely to do the difficult, hands-in-the-dirt changes needed to improve their success rate, as we argue in our book Rewired: How to Outcompete in the Age of Digital and AI. But using proprietary data, we’ve found just how and where digital transformations create value — and what businesses can do to beat the competition.

Hard Evidence, Real Value

Hard evidence that directly ties digital and AI transformation to improvements in operational KPIs and financial performance is scant.

To redress this issue, we turned to banking, a sector that has enough of a history with digital transformations to produce meaningful findings and where we own a unique longitudinal dataset.

First, we used McKinsey’s Finalta benchmark, which tracked the performance of 80 global banks every year from 2018 to 2022 against a set of 50 normalized metrics, such as digital/mobile adoption, digital sales by banking product, number of people in contact centers, and number of branches. We then isolated performance in two metrics — the percentage of mobile adoption by their customer base and the percentage of sales originated in digital channels — to define 20 digital leaders and 20 digital laggards. These two metrics are broadly recognized in the industry as core indicators of a digital retail banking model.

Next, we combined this data with McKinsey’s Corporate Performance Analytics to see how banks ultimately perform against financial metrics (e.g., total shareholder return, growth, expenses). We then ran a blind assessment (i.e., the identity of the banks was hidden) of the maturity of digital and AI capabilities of the leading and lagging banks.

The findings have been striking: Digital leaders are creating much more shareholder value than laggards. Between 2018 and 2022, digital leaders achieved average annual total shareholder returns of 8.1% vs. 4.9% for laggards. Leaders also had significantly better return on pre-tax tangible equity (ROTE), growing it from 15.5% in 2018 to 19.3% in 2022, vs. a more modest growth from 13.6% to 15.3% for laggards.

This financial outperformance is a result of leaders’ success in growing revenue and better containing expense growth. Between 2018 and 2022, digital leaders have grown their active customer base at 0.5% and their retail revenues at 0.8% annually, while digital laggards saw zero growth in their active customer base and a decline of 1.4% per year on retail revenues. During the same period, leaders’ operating expenses grew at 1.3% per year, while laggards grew at almost twice that (2.3% per year). So, how are leaders able to outcompete so demonstrably?

Creating Value That’s Hard to Copy

Where does value come from? Let’s look under the “digital hood.” Both digital leaders and laggards are growing adoption of their mobile app at the same rate, with a gap of 14 to 15 percentage points between them staying constant over time. (See below chart.) This is not surprising. As soon as a bank introduces a new mobile app feature, others see it and follow suit relatively quickly. The mobile app is table stakes.

Turning to digital sales provides a much more insightful answer. Here, the gap between leaders and laggards is growing fast, with leaders almost doubling their advantage over laggards over the five-year period. In fact, digital leaders grew digital sales from 40% to 70%, while digital laggards grew from 8% to 17%.

The reason for this large differential is that to drive digital sales, leading banks go well beyond the mobile app to digitally transform what’s hard to see and hard to copy: the end-to-end process from origination to fulfillment to servicing. To do this, they must orchestrate hundreds of teams capable of developing digital and AI innovations, day-in, day-out, across all their customer journeys and core business processes.

At the front end of this process, for example, leading digital banks deploy personalization analytics and digital marketing campaigns to bring relevant offers to (potential) customers. In the middle of this process, they create an omnichannel experience where branch and contact center professionals have the tools and data to support customers at any stage of the sales journey, even if that journey was started online. These leading banks also provide customer approvals in real time, thanks to automated credit-risk decisioning. At the back end of the process, they drive customer self-servicing through well-designed digital workflows enabled by a modern data architecture.

The value of this approach to transformation is also revealed in contact center staffing. Laggards saw an increase of 20% over the past five years, as they were unable to contain inbound calls from customers that enter digital channels. In contrast, digital leaders were able to decrease contact center staffing by 11% as they benefited from their ability to fully fulfill customer demand online and provide effective self-servicing capabilities.

Knowing what to do is important, but executing on the “how” is what makes the difference. Let’s see how a U.S. bank did it for its secured lending business. Traditionally, the bank took about 45 days for a customer to secure a loan, on average. The process involved multiple documentation requests to customers (e.g., pay stubs, W2s, letters of explanation), and back-end processes (e.g., initial file review, file assignment, ad hoc reports) were highly manual.

To transform this journey, the bank’s leadership team reinvented the entire process. To speed pre-approvals, they developed a database of tens of millions of U.S. households combining credit, property and income attributes using internal and external data sources. This data allowed them to generate personalized pre-approved offers that customers could accept with one click. They built a mobile-first customer experience, where customers could personalize their offers based on real-time data and finalize a pre-filled application, either on mobile or with the assistance of a bank employee. They redesigned key processes (e.g., specialized loan “assembly lines”), automated key tasks (e.g., initial file scrub), and developed digital tools for operators to drive productivity (e.g., daily workflow management). And they modernized credit policy execution to enable greater use of data in underwriting (e.g., using direct deposit data for income), while maintaining or increasing risk controls for the bank.

To enable all these innovations, they implemented key technology and data capabilities, including a customer data platform, AI/ML models (e.g., propensity models), data products (e.g., income), a digital app for customers and a workflow tool for the fulfillment center, all deployed on a cloud-based platform-as-a-service infrastructure.

All in all, this transformation required more than a dozen use-cases across the entire journey and massive change management programs (e.g., training, retooling) for agents in branches, contact centers, and operations. But, only 18 months after the initial launch, the approval process was shortened from 28 to 7 days. This leap allowed the bank to become a leading secured lending originator and increase originations by 35%, while reducing origination cost by 20%.

The Capabilities Needed to Outcompete

A company that aspires to outperform needs to do the kind of end-to-end changes the bank above did across dozens of customer journeys and core business processes. That’s only possible when it is rewired with differentiated capabilities. Our study of more than 200 large-scale digital and AI transformations isolated the six core capabilities rewired companies develop:

Creating ambitious and focused transformation roadmaps. This requires business leaders to align their efforts on specific domains (e.g., journeys or processes) that matter to customers and generate significant value.

Building a quality digital talent bench. Leaders prioritize creating an environment that attracts top-notch engineers and allows them to thrive (e.g., tailored career tracks, autonomy).

An operating model where hundreds of small cross-functional “pods” made up of business, engineering, and resources from control functions are mobilized against priority solutions. A single journey (or product) owner responsible for the end-to-end experience.

A distributed technology environment and modern software engineering practices to allow the entire organization — not just IT — to develop digital and AI-based solutions.

Data products and modern data architecture that make it easy for different parts of the organization to consume data for their own applications.

Change management to ensures digital solutions are adopted and can scale by making them easy to use and reuse across the enterprise.

In our blind assessment of these capabilities for the leaders and laggards, we found that leaders stand out across the board on these capabilities. No single one explains their success. All are needed. With that baseline, the most differentiated capabilities are talent and operating model, not technology. Over time, these capabilities create ever-improving customer experiences and drive lower unit cost. Financial rewards follow.

While our research has focused on banking, our experience reflects similar lessons and patterns in every industry, whether B2B or B2C, products, or services. A digital and AI transformation, however, cannot be done in “special project” mode. To pull this off, the entire organization must be able to deliver constant digital innovation, which requires a holistic set of capabilities. The effort is significant, but so is the reward.

This content was originally published here.

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