How AI Transforms Business Strategy and Ecosystems with LBS Professor Michael Jacobides
Hi, it's Roman and welcome back to the Partner Insight newsletter!
This week, we'll break down AI's impact on business strategies and ecosystems. I'm excited to share key insights from my recent discussion with Michael Jacobides, Professor at London Business School, and Yan Zhang, VC advisor and frm. COO of PolyAI.
We'll explore the AI paradox, successful adoption strategies, potential AI moats, and the shifting dynamics of ecosystems. From the hyperscaler advantage to the challenges faced by traditional SaaS companies, this edition is packed with actionable insights for business leaders navigating the AI shift.
Speaking of navigating new territories, our next Cloud GTM Leader Course cohort is starting on October 29th. The early bird discount is ending soon, so don't miss this opportunity to accelerate growth of your cloud partnerships. Join a community of leaders who are shaping the future of tech alliances.
Let's dive in!
How AI Transforms Business Strategy and Ecosystems
With AI set to reshape how businesses operate, understanding its influence on strategies and ecosystems is crucial. To explore this topic, I turned to two incredible experts:
Michael Jacobides: Professor of Strategy and Entrepreneurship at London Business School. Professor Jacobides is a leading global authority on business ecosystems, advising academia, think tanks, and corporate boards.
Yan Zhang: VC advisor and former COO of PolyAI, one of UK's top AI companies. Yan brings practical wisdom from leading sales, partnerships, and operations and also investing in the AI sector.
Our discussion, which blends academic rigor and hands-on experience, offers a unique perspective on:
How companies adapt to and adopt AI
Business model evolution in the AI era
The rise and influence of AI-driven ecosystems
The Gen AI Paradox: Bridging Hype and Implementation
As Generative AI grows rapidly, Michael Jacobides' recent research unveils a stark disconnect between executive enthusiasm and practical AI implementation.
Jacobides observes: "We're in a very funny and historically unprecedented kind of place where I think that there is both excessive hype because people know it's important but they haven't translated it for their own organizations, and sometimes an under-appreciation of what the technology can do."
This paradox manifests in two key ways:
Funding without Strategy: Boards are writing "very big checks" for gen AI initiatives, yet organizations struggle to integrate these technologies effectively. As Jacobides notes, "People are … saying, 'I don't know what that means. I don't know how to integrate it.'"
Untapped Potential: While there's significant hype, many companies are also "not taking enough advantage of what this technology can offer." This suggests a massive opportunity for those who can effectively harness gen AI's capabilities.
Gen AI Adoption: The Path of Least Resistance
Yan Zhang’s experience in Poly AI and in VC provides a unique perspective on where gen AI is gaining traction. Zhang identifies three key areas where gen AI is seeing successful deployment:
Outsourced Workflows: Gen AI adoption is highest in processes already outsourced to BPOs (Business Process Outsourcing). Zhang explains, "Gen AI... is most quickly fitted in workflows that have already been outsourced." This includes customer service, content moderation, and document processing. The rationale? "Things that have already been carved out of the organization are things that are the first to be automated with gen AI."
AI Component Enhancement: Companies providing core AI functionalities like voice recognition, LLMs, and text-to-speech are seeing significant success as they support other AI-driven products.
Augmentation and Co-pilots: Substantial adoption is seen in areas like sales automation. Zhang notes, "We're seeing a lot of adoption in sales automation, in companies that are using AI to help outbound or enter details into the CRM."
Maximizing Gen AI Impact: Modularity and Strategic Integration
Talking about companies that see most success with AI, Michael Jacobides' research reveals two key factors:
Modularization: Companies with modular digital infrastructures see higher satisfaction and impact.
Jacobides notes, "Companies that already have a digital DNA and infrastructure, where things are modularized, created in neater little packages, can say, 'This is right for me to put a solution that is AI or gen AI based.'" This modularity enables easier integration and adoption.
Systematic vs. Sporadic Use: Organizations that use gen AI systematically, rather than sporadically, report greater impact. Jacobides explains, "You have a much greater impact when you use it not only sporadically but systematically." This systematic approach involves molding processes to accommodate gen AI and applying it to core organizational functions like operations, IT, HR, and legal.
Beyond these factors, Jacobides highlights a critical mindset shift:
"The people that find the greatest excitement are those that don't only think about it as a cost saving... but also those that are trying to either benefit and expand on the opportunities of personalization at the mass level, or those that try to create new revenue models, new ways in which we're able to make money."
This insight suggests that the most successful gen AI implementations go beyond simple efficiency gains, focusing on creating new value through personalization and innovative revenue models.
Potential Moats for AI Solutions: Orchestration, Access, and Future Competitive Advantage
The explosive growth of AI has sparked intense industry debate about sustainable competitive advantages of AI companies and solutions. Yan Zhang offered his insightful perspective into potential moats for AI companies.
Orchestration as a Moat
Zhang emphasizes that while foundational models like GPT-4 or Claude are powerful, they aren’t inherently problem-solvers; they aren’t trained to be. The real value lies in orchestration:
"For you to use these models to solve these problems, there is orchestration that's needed. So what are the components of the problem? Then what model do I use to solve this part of the problem, and then what model do I use to solve the second part of the problem?"
This orchestration - the ability to combine and optimize various AI components - creates a significant barrier to entry. Zhang likens it to assembling a PC or car, where "you're adding a lot of value by optimizing the parts."
Problem Access and Domain Expertise
Privileged access to specific problems and associated domain expertise is crucial for moat-building. Zhang notes:
"Not everybody has access to a specific corporate workflow. So if you want to moderate content, for example, you need to be familiar with the trust and safety rules of many different user-generated platforms."
This combination of problem access and deep domain knowledge allows companies to fine-tune AI solutions in ways generalist models can't easily replicate.
Data as a Differentiator
While data is crucial for training models, Zhang highlights its importance in product creation:
"The data is not just for you to train your models to perform better, but also just for you to create a product or an orchestrated kind of stack of AI models to solve the problem."
Companies with rich, relevant datasets have a significant advantage in creating effective AI solutions.
The Wildcard: Future Model Capabilities
Meanwhile, Zhang acknowledges the looming question: "Will a foundational model come out that's so smart... that it's going to figure out all of these in-between steps?" The answer could reshape the competitive landscape, potentially eroding some current moats.
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Gen AI's Disruptive Force: Winners, Losers, and Strategic Imperatives
Evaluating AI adoption, Michael Jacobides offers a grounded perspective on gen AI's impact across industries, challenging overly optimistic projections and highlighting the nuanced reality facing businesses.
Jacobides emphasizes that gen AI's effect will vary significantly across sectors, suggesting that AI strategies must be tailored to specific industry contexts rather than following a one-size-fits-all approach.
Jacobides' research identifies two key factors that predict gen AI disruption:
Pattern Recognition:
"Those industries and those business models where identifying patterns - pattern recognition - is important, you see an expectation of disruption, an expectation of substitution (i.e., we're going to be all wiped out), but also a big expectation that some firms will be able to leverage their advantage."
Industries heavily reliant on pattern recognition, such as advertising and SEO, are particularly vulnerable. Jacobides notes, "The growth of gen AI means that SEO, the efficiency of websites to find stuff is now done by automated agents better than even seasoned professionals."
Proprietary Data:
"The second thing that was even more important was the relevance of proprietary data - not of technical knowledge, not of tacit knowledge, but proprietary data."
Companies with valuable proprietary data are better positioned to leverage gen AI advantageously. This suggests that data-rich firms have a significant edge in the AI race.
Jacobides observes an emerging split in gen AI adoption:
"We may end up seeing some bimodality with lots of companies in the middle that are too scared to do the jump properly, and some of the very small companies who say, 'Forget it, this is important. We need to experiment. We need to get that right.'"
This suggests mid-sized companies might risk falling behind in AI adoption due to overly cautious approaches.
Beyond Technology Focus:
A critical insight is the need to look beyond the technology itself:
"Ironically, most of the people I see focusing on the technology itself, which is kind of missing the point, because what you need to think is: What are the complements of this technology in terms of the strategic capabilities and the strategic assets, in terms of the pre and the post, in order to be able to drive this transformation?"
Jacobides emphasizes understanding how gen AI fits into broader strategic capabilities and assets.
He highlights key areas for strategic integration:
Upstream: "Data engineering that is going to provide the right kind of data"
Downstream: "Embedding the insights into the operations and linking them to the product that you get"
Strategic Assets: "Proprietary data and the other knowledge assets that you can create, or the repertoire of answers that might potentially give some network externalities"
Tailored Leadership Approach:
Finally, Jacobides stresses that leadership involvement should be tailored to specific gen AI use cases:
"It is important for senior leadership to be involved. But there isn't a one-size-fits-all which says you always need to have a CEO involvement, and it always needs to be part of the CTO. No, it may need to be part of the COO mission, it may need to be part of other people who run business development or strategy, depending on what you want to do."
AI-First Startups DNA: Navigating Uncertainty and Edge Cases
Our discussion on how AI-first companies operate differently from traditional SaaS businesses offers insightful lessons for AI adoption.
Yan Zhang, drawing from his AI startup experience, highlights a fundamental difference between AI and traditional SaaS:
"AI is a non-deterministic system, and so not everything works on day one, and then not everything might work on day 60 or as business as usual. And so there are lots of edge cases."
This difference requires a different operational mindset, focused on continual refinement and problem-solving.
Edge Case Management:
The core challenge for AI companies lies in handling edge cases:
"The whole game of trying to make AI useful is how do you deal with the edge cases that might come out. And then in every use case, you will find edge cases that are difficult, that are minor, that are frequent, infrequent. And a lot of AI implementation is taking care of those edge cases."
This focus demands more adaptive operational structures compared to traditional software development.
AI companies also often require more complementary services:
"You tend to see more services that complement AI companies than services that complement traditional SaaS. And so, you know, whether it is a more rigorous monitoring system where you are trying to get rid of edge cases or automate edge cases, or you have a human fallback..."
This hybrid model of technology and services can create unique business profiles and margin structures.
AI Ecosystems: The Hyperscaler Advantage and SaaS Disruption and New Incentives
One of the key focus areas for our discussion was to analyze the emerging AI ecosystems.
Zhang observes a clear market belief in hyperscaler dominance:
"The market believes that most of the value, as the market lays in the current configuration of things, is going to be captured by the hyperscalers and the cloud giants, and not so much by the other kind of SaaS vendors."
This trend is supported by hyperscalers' key advantages: they have the best scientists, compute power, and capital.
Zhang identifies two main categories of SaaS companies facing potential disruption:
Systems of Record
Automation Workflows
"Do you still need Salesforce as it is today, which is a CRM with lots of different fields that someone has to go fill out, when AI is doing a lot of the actual business processes? It probably will look different."
This suggests a potential reshaping of the SaaS landscape as AI capabilities expand.
Meanwhile, Michael Jacobides highlighted an important distinction between AI influence on different SaaS providers:
"I'd be much more concerned if I was Salesforce than if I was SAP, in the sense that CRM is much more on the chopping block than ERP because you need a single source of truth."
This suggests companies providing more core, hard-to-replace services may be better positioned in the AI era.
Talking about emerging ecosystems, Jacobides highlights Nvidia's approach as a model for creating lasting advantage:
"Nvidia is playing an ecosystem game in order to prepare itself for the inevitable time that it isn't as difficult to create the chips as it is today."
Both experts highlight how AI is reshaping organizational boundaries and relationships:
Jacobides: "Is this technology lubricating the possibilities of connecting with those outside? Is it making it easier for me to cross outside to think about new combinations, to facilitate things like contracts or searching the market? The answer is an absolute yes."
Zhang adds that AI may reshape traditional partnerships: "The physics of ecosystems is incentives. And AI is bringing a different set of incentives into the ecosystem."
Zhang points out the evolving incentives in the AI ecosystem:
"AI, as I said, is spinning out different types of professional services for implementation. Some of it is more profitable for the systems integrator to do, some of it taking away."
This shift may reshape traditional partnerships and value chains in the tech industry.
AI Ecosystem Dynamics: Bottlenecks and the Orchestrator-Complementor Balance
Michael Jacobides offered an insightful perspective on the evolving dynamics of open vs closed ecosystems and how this may influence AI ecosystems.
The Openness Paradox:
Jacobides highlights a common pattern in ecosystem development:
"There is a self-professed excitement with openness from organizations that simply try to enlist support. And then once they start ruling the world, they start cracking the whip."
This tendency for initially open ecosystems to become more closed as they gain dominance is crucial for alliance strategies.
Learning from Metaverse Missteps:
Jacobides uses Meta's experience with the metaverse as a cautionary tale:
"The metaverse folded partly because of a very narrow-minded strategy in terms of ecosystem plays. Meta wanted to hold everything... But all they cared about is, 'Give me 47.5 percent of your top line.'"
This illustrates the potential pitfalls of overly closed ecosystem strategies.
Strategic Openness:
Jacobides advocates for a more nuanced approach to ecosystem management:
"Being more thoughtful about where you are open and when you're closed, and being more focused on joint value creation with a sensible, fairly straightforward distribution of value as opposed to wanting to be greedy and focusing everything to yourself, is a more effective solution."
This balanced approach can lead to more sustainable ecosystem growth and value creation.
Zhang emphasizes that we’re still very early:
"The jury is still out on what the ecosystem in AI looks like. Because ecosystems are built around, as you point out, kind of bottlenecks…
The choke point is the chip, is the chip and actually talent. So ecosystems are actually probably being built around Nvidia."
Both experts agree this bottleneck is likely temporary, suggesting future shifts in ecosystem dynamics.
Orchestrator vs. Complementor: Strategic Positioning in AI Ecosystems
Our discussion on how companies should approach their role in AI ecosystems emphasized the role of realistic self-assessment.
Avoiding Delusions of Grandeur:
Jacobides cautions against overambition:
"The biggest problem that I have is delusion of grandeur... Usually it is alpha males, not alpha females, that want to say that 'I'm going to be leading that thing.'"
He advises companies to focus on unique strengths rather than trying to emulate larger players.
Instead, he recommends a nuanced strategy for most companies :
"A clever way of partnering or a way in which you find what they [top players] are unable to do, the data they do not have, the functions that are too idiosyncratic to your value add that you can develop, is what you should be focusing on."
This involves being part of larger ecosystems while orchestrating smaller, focused networks of relationships.
Embedded Incentives as AI Barriers:
Zhang highlights how deeply embedded incentives can hinder adaptation:
"There are embedded incentives within these organizations that are really, really difficult to change."
He uses call centers to illustrate how traditional business models can impede AI adoption, even when companies have necessary resources and data. Traditionally, call centers couldn’t have 100% utility because of human constraints.
"When AI can pick up the phone, it can do so at 100 percent utility all the time. And so all of a sudden, AI vendors are very willing to charge on a per-result or per-active-minute type of model, and these companies can't catch up."
This highlights how AI can fundamentally change industry economics and business models.
Leadership's Crucial Role:
Both speakers emphasize top-level leadership's importance in driving change. Zhang stressed:
“incentive is so deeply embedded in how they do things because if you get rid of it, promotions, bonuses, you know, people's places, revenue margins - these things all have to change. So it is really difficult.
As Professor Jacobides said, you really have to have someone on the very top of the organization saying, "We are absolutely going to do things differently," in order to make the right decision about where you play in the ecosystem in the future. And it's going to take tremendous leadership from a whole bunch of companies.”
I hope these insights inspire you to take action. Whether you're a C-suite executive or an emerging leader, the AI r/evolution presents an opportunity to drive lasting change. The future of your business may well depend on the decisions you make today.
Unlock Cloud Marketplaces: Join Cohort 8
As we gear up for the final Cloud GTM Leader cohort of 2024, a staggering statistic stands out: Customer cloud commitments for AWS, Azure, and GCP are set to reach $500 billion in the next 18 months.
Let that sink in.
This isn't just an enormous number — it's a permanent shift in how the B2B software is consumed and delivered to customers.
Here's what it means:
Marketplace Normalization
Even giants like Salesforce report that 3 of their top 10 deals last quarter were through the Amazon Web Services (AWS) marketplace. Many startups are now joining marketplace too. This is no longer an optional channel—it's becoming critical for growth.
AI-Driven Growth
The surge in cloud commitments is fueled by the AI revolution. Enterprises are racing to leverage AI, driving cloud migration and modernization at unprecedented rates.
Partner Ecosystem Evolution
With 50% of marketplace revenue projected to be driven by partners (Canalys), the entire ecosystem is recalibrating.
But despite this massive opportunity, only a minority of companies have a successful GTM strategy with cloud marketplaces.
A few truly know how to co-sell with hyperscalers, while many still face tensions between alliances and sales teams.
In our last 7 cohorts of the Cloud GTM Leader course, I've watched 150+ alliance leaders experience their "aha" moments.
It's not just about getting listed, having the first transaction, or having a magical conversation with a hyperscaler leader.
It's about strategic positioning, the right sales strategy, internal alignment, disciplined and deliberate execution, leveraging cloud provider initiatives to amplify your own growth, and more - all of which we cover in our 5-week course.
It's about taking advantage of the playbook already developed by others who have spent their precious time and resources to learn, make mistakes, win, and now share.
As we stride towards this incredible $500 billion cloud commitments milestone, I'm curious:
How is your organization planning to capitalize on this massive pool of committed spend?
What's your biggest obstacle in aligning your teams for marketplace success?
Let's decode this together.
🎯 And if you're ready to turn insight into action, join us for Cohort 8️⃣ starting October 29th. It's the last chance this year to gain the strategies that will position you for success in 2025 and beyond.
The early bird discount is ending soon, so don't miss this opportunity to accelerate growth of your cloud partnerships.