Shrink, Pinch, Stretch, Move & Skill - The 2025 agility view
The five principles that are shaping businesses this year.
Another year is off to a start. We see a flurry of predictions, ideas, breakthroughs that we think 2025 will bring. As I look at how 2024 was and the unknowns that we are grappling with as we start this year, I articulate the how leaders will possibly approach and shape their businesses through the course of this year.
As much as we see the discussions around generative AI, the bigger question continues to be, ‘Is your company built to win in any economic environment’?
To answer this question, I looked at two sets of companies : large and mid-sized ones. It was easier to spot the key differences: the investments in technology, R&D, go-to-market reorganization. But at a macro-level, these two set of companies seem to have a similar view on how to shape the business through 2025.
The five big principles
In my view, there are five big elements in play to win in 2025, in any economic scenario:
Shrink the time to the customer
Pinch the manager layers
Stretch the augmentation
Move the firm’s intelligence
Skill the upper middle
As you read through this, you should be able to stack up your company and its approach across these five big principles.
As you consider these five big principles, they should also provide some ideas to:
What if the fragmentation in geo-politics of the world continues to increase the ‘hyper-nationalist’ view of being local first?
What if the company is unable to move fast enough to respond to security challenges: data, personnel, regulatory or supply-chains?
What if the large language models do not give you the promised breakthroughs, how would you pivot on approach to AI?
The five principles don’t shape a strategy by themselves. However, they are integral to the choices being made as part of strategy in companies.
Let me walk you through the five principles.
In the face of geo-political uncertainties potentially impacting supply chains, companies have been focussed on finding the optimum balance between buffering for disruption while protecting their margins.
With this we now see a flashback to good old fashioned productivity thinking from the linear business models era - to the determinant of breakthrough strategy choices. But ‘the shrink’ needs technology applied to the core of a business - how you serve your customers. This is beyond the classical digital transformation thinking. This is examining each activity within a firm and evaluating the value impact it has on customers.
This is important as significant productivity is a one time gain, after that it is always marginal till another wave of transformative technology or work method comes through.
We are not yet seeing fully developed or matured intelligent ‘Search to Service’ models. Many of the companies have invested or experimented in the search part of the equation with Gen AI hoping customers can benefit from contextual discovery. However, the translation of this ‘discovery’ to fulfillment of a service or a buy decision does not yet meet the promised potential.
As companies continue to experiment with new technologies, in much of the cases, companies continue to squeeze the dollar to drive their productivity gains. This is focussed on the right allocation of resources, shifting to more scrutinized performance of people, delayering of the organization, rolling back low ROI initiatives or even removing certain options for customers to minimize the cost of service.
The response to this challenge of ‘shrink time-to-customer’ has been pre-dominantly reorganizing go-to-market structures to answer some of these questions:
How can I increase the penetration of my products and services in a known market to reduce my costs of acquisition of new customers?
What other value streams can you piggy back on to the existing service platforms?
Can I transfer more of the companies share of operations to the customer through ‘intelligent search to service models’.?
With the focus on efficiencies and leveraging existing business elements to increase both revenue and margins, we have seen a trend over the past 18 months where layoffs/redundancies announced by companies impact the manager layers more than the execution ones.
In this process, the definition of being a manager has been overhauled. It is an expectation now that direct contribution competence and skills are needed to be an effective manager. It is a flashback to the belief in the technology sector that higher the span of control, the lesser possibility of a manager getting into the sandbox of each reporting person thereby enabling innovation.
(People) Managers are now defined to be 70-80% individual contributors. Mere aggregation of resources or activities is not the defining factor of a managerial role. So the 20-30% of ‘people managerial stuff’ becomes vital to defining the purpose and culture of a company.
As automation increased in manufacturing sector, we have already seen this play out starting a decade ago. It is only now that there is an impact to ‘white collar’ jobs in the knowledge levels of an organization. Technology alone is not the reason for this shift in these two years. The Covid era bench, combined with consumer sentiment and the buzz of AI had all given companies the opportunity to reshape their management philosophies.
It is not that every company laying off managers as part of their overall redundancies has mastered application of AI and other automation technologies. It is primarily because organizations have been slowed and often subverted by layers of managers creating bureaucracy and resistance to change - when rapid change is not just a need to win the future but to survive the present.
Companies will continue to focus on adopting technology and tools that either fully automate or augment people. But the gains from technology while successful in process driven automation, have eluded the impact in knowledge centric jobs. Co-pilots have not solved for clutter and the need for rework after a certain output is received. While we have seen gains in software development through Gen AI tools, this impact has not been across many other types of functions in an organization.
Wall Street Journal reports that 1.6 million Americans have not been successful finding a job despite applying for numerous ones over a period of six months. With the US unemployment rate hovering around 4.1%, should the economy grow beyond the project 1.5% to 2.7% in 2025, there is no talent for businesses to grow with. Therefore, productivity through augmentation is a necessity. Adding to this issue is that hiring is a broken function across industries.
An unsaid and underlying driver in the pursuit of ‘fast automation’ by companies is that bots cannot form unions and that automation is increasingly cheaper. But in this, the larger purpose of automation for exponential increase in the value of the enterprise through reinvention of work is lost.
Jevons Paradox shows that every time you make something more efficient, the demand actually grows. As Gen AI and language models get efficient, it’s not just that code is being written more efficiently, it becomes easy to build more software for applications so far not focussed upon.
Job augmentation of technology is one such area. Any technology implemented for a customer ends up changing the nature of jobs not just in the serving company but also in the larger ecosystem of jobs in the supply chain. So far, beyond co-pilots and the uneven use of these tools by employees has shown the absence of intentional design by companies. This is also because of the fragmentation and silo nature of data existing within the firm. AI cannot solve for fragmented systems. The human-tech handshake design becomes critical to success in expanding the augmentation of jobs with AI and other technologies.
In my view, there two areas of augmentation that deliver outstanding results to a company: Business Intelligence and Knowledge Management. We know employees spend on an average about forty five minutes each day just searching for information to do their jobs. This search is essentially for two things: analytics & data (business intelligence) or contextual search for information - concurrent, historical, comparative etc. (Knowledge Management). This will take companies beyond off the shelf co-pilots into customized value based implementation of Gen AI tools.
Arguably, harnessing the collective intelligence of a firm and moving it at speed to benefit - to solve problems or identify future opportunities is the single biggest challenge and opportunity in the age of AI.
As workforces shrink, as the manager layers get eliminated organizations lose a lot of tribal knowledge that helps them succeed. It would be safe to say, 50% of day to day activities inside each job in the company are in the grey zone. A simple measure would be to ask employees the percentage of actions they take that do not fall in their normal job descriptions.
This knowledge is vital to be captured as this exists in the minds of people. This is typically accessed through internal networks of informal referrals or relationships. But the limiting factor in this, especially in a large global enterprise is that it is highly localized. The speed and nimbleness companies want to build into themselves is based not so much on layers or structures but on the ability to move firm’s collective intelligence to where it matters.
Companies are inundated with information from across the business functions. But there is little effort (compared to customer data) to collect, organize and synthesize it to the benefit of employees or decision makers.
Organizations are designed to serve the ‘need-to-know’ of managers. This creates a ‘face-to-the-boss and ass to the customer’ approach to how knowledge flows. Agility needs knowledge to flow towards the customer: one - decisions on the future of the business to create new opportunities, two - decisions that make your products and services a ‘wow’ experience for the customers.
Companies are on the path to find the right approach to knowledge management, especially encouraged by Gen AI applications. The key realization for companies is that productivity driven by AI or reduced workforce is limited in absence of powerful knowledge management approach.
Creating enterprise wide knowledge management framework is hard. Once you get past the usual cultural barriers, it is a design challenge. It is easy to consolidate and organization data or information that is hard coded into multiple systems. However, the real value comes from experiences of teams or individuals across multiple sets of scenarios and contexts over a long period of time.
There is a strategic imperative for large companies to focus on this, if you consider digital age conglomerates. But this is absolutely the imperative for small to mid-size enterprises to compete with large companies.
Companies aspire to build future capabilities at all levels of leaders. But I hear two issues:
Operational pressures forcing them to drive immediate outcome based development interventions (teaching P&ls, Balance Sheets, People Manager Fundamentals etc.)
‘We are headed in that direction but its too far out’, we still have basic needs for our leaders to learn and develop
Companies that state these two issues are using it as an excuse to not build real tangible leadership capabilities. Over many years overseeing succession and capabilities across 55+countries, I can say one thing for certain, investment into capabilities has two include two dimensions:
Exposure to a wide range of ideas from within and outside the industry, domain areas and functions - people with the breath of exposure have higher adaptability and openness to change. They are generally more curious than others.
Bias for action - the impatience to get things done but with a strong focus that each action is a building block for the future. They have this ability to articulate the future so clearly that it becomes easier to build for it today.
But with so much change and the focus on efficiency of spends, where should companies invest on leadership capabilities that would deliver exponential results for the company? This is where the ‘upper middle’ comes into play. The leaders in roles at CEO -2,3 & 4 is where the sweet spot for agility is. These are leaders who are influencing decisions, sharing ideas into the future business plans and also overseeing ‘rubber meets the road’ impact. These are some of the toughest roles in any company.
This is also the bandwidth where succession falls apart. We have great operational leaders who can’t make it to the succession paths for top jobs. In part because companies like to keep them operationally focussed. The intellect, in-depth understanding of the business, the relationship networks at these levels are an asset but often under-leveraged for the future of the company.
The inter-connectedness in the company, between markets, functions, levels all come together here. To have these leaders develop multi-disciplinary, inter-connected learning is the difference between entropy and acceleration.
Focussing in developing capabilities for the ‘upper middle’ is the surest, fastest and safest way to drive change at the speed of the customer.