As we step deeper into the latest technological era of AI, we again start to see a gap between traditional companies and new ones starting to disrupt and change the status quo.
It may reveal my age, but this marks the fourth major shift I've witnessed in IT as new disruptors emerge. My journey began in the mid-2000s, with the evolution of mobile phones into smartphones—a transformation that reshaped entire industries and set a new standard for disruptive innovation.
Here, I'd like to take a closer look with you at the innovation dilemmas faced by legacy technology giants—mobile phone manufacturers, storage vendors, automotive companies, and SaaS providers—by comparing them to the disruptors in each space.
Personally, I was attracted to the Innovation Dilemma as someone during a hiring interview told me more about the book, 'Innovator’s Dilemma' by Clayton Christensen . I was curious immediately!
By analyzing key factors like technology & integration, customer experience, automation, speed of innovation, data management, scalability, R&D focus, and architectural design, we can understand the common challenges these legacy giants face(d) and how they can either adapt or risk being left behind.
So let's go back to the mid 2000s and let's discover if we can find similarities over these last few decades.
1. Mobile phone manufacturers vs. Smartphone-native companies
Mid 2000s
Mid 2000s, Nokia, Blackberry and Motorola were household names in the mobile phone industry. I had one, and remember one of my first phones, a Nokia, 6310 later replaced by the holy grail, a Blackberry.
Their devices were sturdy, functional, and held massive market shares. But with the launch of the first iPhone in 2007, Apple introduced a radical shift in the industry of mobile phones. Phones became more than just hardware—they became ecosystems. By the mid-2010s, Apple and Samsung had not just captured but dominated the entire mobile (smartphone) market.
The challenge for legacy mobile manufacturers was adapting to a software-first world. Over the years they invested heavily in hardware, optimizing for physical durability and basic phone functions like calling and texting.
“Why would Blackberry change? Everyone loved the typing mechanisms, right?“
Meanwhile, smartphone-native companies built entire ecosystems around apps, internet connectivity, and integration with other devices. Legacy manufacturers couldn’t pivot quickly enough, and the market passed them by. Convergence became a term by itself. It was Apple converging camera, phone and music into one single and seamless platform- 'IoS'. Three apps as a starting point, but let me come back to this later.
Comparing some key factors within the world of “mobile”, we learn the following:
Key factors | Legacy mobile manufactures (Nokia, Motorola, etc.) | Smartphone-native companies (Apple, Samsung, etc.) |
|---|---|---|
| Slower to pivot towards smartphones, gradual adaptation to software-driven features | Rapid, continuous innovation with focus on both hardware and software |
| Fragmented approach to platforms, often dependent on third party OS like Android | Highly integrated ecosystems (e.g., IOS, iCloud) leading to stronger user lock-in |
| Historically hardware-centric, slow in developing proprietary software | Balanced focus on both software (iOS, apps) and hardware (A-series chips, etc.) |
| Depended on brand recognition from pre-smartphone era, lost ground in modern markets | Strong brand loyalty, bolstered by premium customer experience and services |
| Struggled with the smartphone disruption, slow to adapt to new trends | Market disruptors, driving new trends with timely product innovations (e.g., App store, Face ID) |
| Well-developed supply chains but limited flexibility for innovation in high-end devices | Efficient supply chain with control over key components (e.g., Apple's control over chip manufacturing) |
| Initially lower R&D software and ecosystems, focused on cost-effective phones | High investment in both hardware and software R&D to stay ahead of the curve |
The smartphone revolution demonstrated that legacy manufacturers were too slow to shift from hardware-centric models to ecosystems where software, followed by design became the standard. The failure of Nokia, Motorola, and certainly BlackBerry to not adapt quickly enough shows how vital it is for legacy companies to pivot early when a technological revolution is underway.
They all failed to grasp the critical importance of integrating cutting-edge software with hardware, which caused them to lag behind in the smartphone revolution. They faced difficulties in adapting to new user expectations for smartphone ecosystems (apps, integration, etc.).
Even though they tried to shift their focus they kept facing the fast pace of software-driven innovation and integrated ecosystems that smartphone-native companies mastered.
2. Traditional storage vendors vs. Cloud-native companies
2010s
A similar revolution can be seen in the shift from on-premise storage to cloud storage which actually began to accelerate in the 2010s.
Traditional storage vendors like EMC and NetApp, who had dominated the enterprise storage market for decades, were caught off guard by the rise of cloud-native services like AWS and Google Cloud (also Nutanix initially, who converged storage, compute and networking to create a new market category, i.e. Hybrid Cloud Infrastructures)
These cloud services offered unprecedented scalability, flexibility, and cost-efficiency, transforming how businesses manage their data. How did AWS start? Yes, just like Apple, with '3 services' namely S3, VPC and EBS.
The innovation dilemma for traditional storage vendors became eminent by the day. Their business models were built around selling physical hardware that required expensive and time-consuming maintenance and upgrades.
Cloud-native companies offered a completely different value proposition—storage on demand, with flexible pricing models and seamless scalability.
It fundamentally changed customer expectations and forced traditional vendors to rethink their entire product lines, but could they?
Comparing some key factors within the world of “storage”, we learn the following:
Key Factors | Traditional storage vendors (EMC, NetApp, etc.) | Cloud-Native comapnies (AWS, Google cloud, etc.) |
|---|---|---|
| Rigid, hardware-centric systems that require costly upgrades to scale | Highly scalable, elastic storage (e.g. S3, EBS), pay-as-you-go models |
| High upfront costs for hardware, complex maintenance & support models | Lowers costs through subscriptions, usage-based pricing, reduced hardware investments |
| Well-established in enterprise sectors, but lacking ease of use and instant provisioning | Easy to adopt for both enterprises and SMEs with self-service provisioning and global reach |
| Strong focus on security, especially for on-premise, but harder to manage across distributed environments | Built-in security, compliance and data governance across regions but concerns over cloud data sovereignty |
| Slower to adapt, with long development cycles and product lifecycles | Fast and frequent innovation with continuous deployment of new features, services and APIs |
| Limited integration with other systems, requires bespoken integrations | Integrated platform ecosystems, offering storage alongside compute, AI, analytics etc. (e.g. AWS Lambda, Sagemaker) |
| High R&D but largely focused on optimizing hardware solutions, slow to move into cloud services | Massive R&D investments focused on cloud, AI, and emerging technologies, lower infrastructure cost due to economies of scale |
What was the lesson towards the era of cloud native? Did we not see this before ?
Cloud-native companies redefined storage, as legacy vendors continued to struggle to keep up the pace of innovations.
The failure of traditional storage companies to anticipate the shift to cloud highlighted the need for agility in a rapidly evolving market. Actually it is somewhat the same lesson as in the 'mobile era'.
Businesses are no longer interested in complex, expensive storage solutions when they can scale easily in the cloud with far fewer upfront costs. A clear advantage was created for Cloud-native companies like AWS and Google Cloud. They created storage solutions that are highly scalable, flexible, and user-friendly.
Their ability to innovate rapidly and offer customers integrated services across cloud infrastructure gave them a significant edge over traditional vendors. Today, cloud-native companies continuously innovate at a rapid pace, enabling them to provide solutions that align with modern enterprise needs.
3. Traditional car manufacturers vs. Electric car manufacturers
2020s
We're all considering electric vehicles, aren't we?
As we entered the 2020s, the automotive industry began to undergo its biggest transformation in over a century. Traditional car manufacturers like Ford and General Motors were challenged by Tesla and other electric vehicle (EV) manufacturers who were not just creating cars—they were creating software-driven vehicles that could represent the future of transportation.
While legacy automakers had perfected the internal combustion engine (ICE) over decades, they were slow to invest in EVs and autonomous driving technology. Meanwhile, Tesla not only revolutionized electric cars but also embedded advanced AI systems for autonomous driving, transforming the automotive industry.
Did we witness another “innovation dilemma”?
How would traditional automakers balance their profitable ICE businesses while investing heavily in the future of EVs and AI-driven systems?
Comparing some key factors within the world of “automotive”, we learn the following:
Key factors | Traditional car manufacturers (Ford, GM, Toyota, etc.) | Electric car manufacturers ( Tesla, Rivian, etc.) |
|---|---|---|
| Slower to adopt EV technology, incremental innovation in ICE vehicles | Rapid innovation in EV technology, including battery development, autonomous driving, and over-the-air software updates |
| Known for reliability and legacy, but associated with outdated technology | Perceived as cutting-edge, futuristic and environmentally friendly |
| Established, optimized for mass production of ICE vehicles; transitioning to EVs is costly | Vertically integrated, focusing on streamlining battery supply chains and EV assembly lines |
| Extensive gas station networks, limited investment in EV charging infrastructure | Expanding charging networks, often in partnership with governments or utilities (e.g. tesla superchargers) |
| Facing increasing pressure to meet stricter emissions regulations, forced to adapt | Built with zero-emission compliance from the ground up, no, reliance on ICE technology |
| Historically focused on optimizing ICE vehicles, now gradually shifting to EVs and hybrid technologies | Focused entirely on EVs, autonomous driving, and energy-efficient technology (e.g. Tesla's full self-driving capabilities) |
| Strong brand equity based on decades of ICE vehicle production, but perceived as lagging in innovation | Growing brand recognition as leaders in the EV market , attracting eco-conscious consumers and tech enthusiasts |
It has taught us the automotive industry reached a tipping point. The slow shift of legacy automakers toward EVs and autonomous technology shows the difficulty of managing legacy infrastructure while trying to innovate.
Tesla’s dominance highlights how new entrants can leap ahead of legacy players by focusing on future technologies and skipping the need to maintain legacy products.
To the contrary, traditional car manufacturers were burdened by their legacy investments in ICE technology, supply chains, and manufacturing processes. While they possess brand recognition and extensive market reach, they face a difficult transition to electric vehicles (EVs), which requires significant capital expenditure and a shift in business models.
This slow pace of change opens the door for new players like Tesla to capture market share, as the traditionals faced indeed an innovation challenge: Traditional manufacturers quickly saw the advantage Tesla and other EVs had, benefitting from a head start in electric vehicle technology, with no legacy ICE baggage.
Tesla has pioneered advancements in battery efficiency, autonomous driving, and software integration, setting new standards in the auto industry. Tesla’s vertically integrated approach and focus on scalability also provide significant cost advantages and speed of innovation.
So what's happening today?
4. Traditional SaaS vendors vs. AI-native companies
Late 2010s - 2020s
Starting in the late 2010s and accelerating into the 2020s, SaaS emerged, on the back of Cloud Native. Salesforce, as an example gained an enormous amount of traction as Oracle in those years offered a set of disparate “on premise” apps and were not able to transform into cloud propositions. Oracle customers began to hesitate to upgrade fusion apps to Oracle's newest suite of applications, Salesforce was entering a massive growth trajectory.
Today, traditional SaaS vendors are at a crossroads where they must figure out how to quickly and seamlessly incorporate AI to remain competitive versus the more agile AI-native companies. "AI- a new Era which came fast."
Facing the challenge of balancing their established, feature-heavy platforms with the need for AI-driven innovation is not an easy assignment.
Their existing customer bases rely on their robust but manual-driven tools, while newer companies expect intelligent, real-time automation and insights from AI. Retroactively integrating AI into these platforms is resource-intensive and risks slowing their product evolution.
Today, Salesforce, Freshworks, Zendesk and others find themselves facing competition from AI-native companies whose platforms are built from the ground up with AI at their core.
We witness some key strategic tensions which in this era of AI creates an 'Innovation Dilemma'.
The above-mentioned traditional SaaS vendors are often hampered by their legacy architectures, which were not designed to take full advantage of AI. On the other hand, AI-native companies, like OpenAI, are built from scratch to incorporate machine learning and AI as the foundation of their platforms.
Furthermore, traditional vendors need to meet the growing demand for AI-enhanced automation without alienating existing customers who rely on the functionality of their current products. All these integrations will address challenges, challenges around complexity by far the most.
In contrast, AI-native companies are able to offer advanced features without the burden of supporting legacy systems. Companies who strategically have to think through the next 10 years have to start asking the question “ Is AI the new 'Cloud', what will I need to do to differentiate?”
Emerging Companies like DevRev, OpenAI, and others can and will offer faster, more intelligent, personalized, and automated experiences, forcing traditional vendors to adapt or risk losing their market share.
Comparing some key factors within the world of “SaaS” and what’s next towards SaaS 2.0, we learn the following;
Key factors | Traditional SaaS vendors (Salesforce, Zendesk, etc.) | Al-native companies (DevRev, OpenAl, etc.) |
|---|---|---|
| Gradual integration of Al an add-on or separate features, often as a service | Al-first approach, Al integrated into the core architecture from the start |
| Solid but more generic tools, manual setup required for personalization | Deep personalization driven by Al, dynamic learning, and real-time data |
| Rule-based workflows, needing manual setup and ongoing maintenance | Advanced automation using Al models, minimal manual intervention |
| Slower due to legacy constraints and large feature sets | Fast and agile, leveraging the latest Al breakthroughs to evolve quickly |
| Strong data management, insights often manual or dependent on separate tools | Al-driven insights, real-time analysis, and predictive intelligence built-in |
| Proven scalability for enterprises, but flexibility constrained by legacy systems | Built for the cloud, easily scalable, with high flexibility due to microservices and Al-native design |
| Focused on expanding core business functionality, Al adoption is gradual | Major R&D focus on Al, automation, and continuous platform improvement |
| Legacy architecture, often monolithic or hybrid, making Al integration difficult and time-consuming | Modern, microservices-based architecture |
The AI technology era’s divide: Legacy vs. Native disruptors
We can see a clear divide between legacy companies and native disruptors. Legacy companies, despite their established brand presence and market dominance, face significant innovation dilemmas, which include:
• Technology & AI integration: Legacy companies often have to retrofit AI or new technologies onto old systems, while disruptors build with AI and cloud from the outset, allowing for deeper and more seamless integration.
• Customer experience: Disruptors are providing personalized, real-time, AI-driven experiences that are more appealing to modern consumers and businesses, whereas legacy companies rely on older models that lack such capabilities.
• Automation & efficiency: Native companies use AI to automate a variety of tasks dynamically, while legacy companies are still dependent on rule-based systems that require manual setup and ongoing maintenance.
• Speed of innovation: Legacy companies, weighed down by their large, feature-heavy platforms, struggle to innovate quickly, whereas native companies benefit from agile, cloud-native architectures that enable fast iteration.
• Architectural design: The architectural divide is perhaps the greatest challenge. Legacy companies are often stuck with monolithic or hybrid systems that are difficult to modernize, while disruptors take advantage of microservices and serverless architectures, optimized for AI and cloud scalability.
All of this teaches us that with the rise of AI-native companies like DevRev, how deeply embedding AI into the core architecture of software platforms is going to be a huge and a key differentiator. Traditional SaaS vendors like Salesforce and Zendesk are, and will continue to struggle to retrofit AI into legacy systems, which leads to slower innovation cycles and limited automation capabilities.
It is completely understandable that companies like DevRev embody an important philosophy:
“Less is Better- If machines do more, humans can focus and do less but better."
In the past, SaaS 1.0 demanded more: more time from your teams, more operational overhead, and more supplementary apps to fill in gaps.
Even more understandable as companies like DevRev are focussed, similar to how Apple and AWS did in the past with only 3 apps, namely Support, Build and Grow.
The objective to redefine SaaS 1.0 and define SaaS 2.0 where AI and design is converged to create solutions that work for you, not the other way around, is clear.
This new era of SaaS 2.0 is about enhancing customer experiences, facilitating seamless team collaboration, and simplifying the execution of daily tasks.
DevRev is designed to change the way your front office teams interact with customers and back office teams develop products by integrating AI agents alongside human intelligence seamlessly.
By merging 'AI' and 'Design', DevRev automates labor-intensive tasks, offers self-service support without compromising customer experience, and enables your support and product teams to predict and resolve complex customer issues more efficiently. DevRev believes the future of work is augmented.
With DevRev, your operations are enhanced by GenAI agents and your new team of highly skilled virtual employees, capable of performing at the level of senior staff.
With DevRev, your operations are enhanced by GenAI agents your new team of highly skilled virtual employees, capable of performing at the level of senior staff.
DevRev, for many companies, is already a partner in this transformative journey, enabling business to thrive in a world where work is augmented by advanced AI.
The innovation dilemma concluded: Adapt or fall behind
Across industries, the gap between legacy companies and native disruptors continues to widen. Legacy companies face a fundamental challenge: how to modernize their systems and architectures while maintaining the business models that have driven their success for decades. For many, the integration of AI, cloud computing, and automation has proven difficult due to the rigidity of their legacy systems.
However, disruptors like Apple, Tesla, AWS and now DevRev as well show what is possible when companies build with flexibility, AI, and cloud-native principles at their core. These companies have been able to rapidly scale, innovate, and meet the changing expectations of modern consumers in ways that legacy companies are struggling to match.
The key lesson from these innovation dilemmas is that legacy companies must act now if they are to compete in the future, but need to recognize they are behind in the curve.
This involves making tough decisions about where to invest and rethinking their core architectures, and accelerating the adoption of new technologies, which could take years. The companies that successfully balance their existing operations with the need to embrace AI, cloud, and automation will emerge as leaders in the next wave of technological disruption.
The message is clear: adapt or fall behind.
In conclusion, while legacy companies once dominated their industries, the rapid pace of technological evolution demands that they rethink their approaches, or risk being left behind by more agile, tech-driven competitors.
The companies that can strike a balance between maintaining their existing strengths and embracing the new possibilities offered by AI and cloud technologies will be the ones that thrive in the decades to come.
I'm happy to be a part of DevRev and to welcome many to its mission.
