GenAI AI has truly transformed content creation by automating text, image, and video generation from simple prompts, slashing the time and skills once needed. Canva leads this shift, blending an intuitive interface with expansive templates and cutting-edge AI tools. This empowers anyone – individuals or businesses – to produce professional-quality visuals with ease, breaking down barriers and making design truly accessible.
Canva’s “Create 2025” event in Los Angeles showcased its evolution from a simple design tool into a full enterprise platform for productivity, content creation, collaboration, and brand management – embedding visual communication across the modern workplace. For tech teams, marketers, and leaders, this shift brings opportunity but also demands careful strategy, integration, and governance to unlock Canva’s full potential in enterprise settings.
Canva Create 2025: Key Announcements
Visual Suite 2.0: A Unified Workspace & Single Design Canvas. Canva unveiled Visual Suite 2.0, a seamless platform combining presentations, documents, whiteboards, spreadsheets, and video editing into one design canvas. This unified workspace helps organisations streamline workflows, eliminate tool fragmentation, and ensure consistent visual communication across teams.
Canva Sheets: Where Data Meets Design. Canva Sheets reimagines the spreadsheet by focusing on visualising data with rich charts, colour-coded cells, smart templates, automation, and AI-powered insights. Designed for teams that share data rather than just analyse it, Sheets empowers every user – including the “data shy” – to become a confident data analyst.
Canva AI: GenAI for the Creative Enterprise. The enhanced Magic Studio integrates AI-driven writing, image editing, template creation, and video animation into one toolset. Features like Magic Write, Magic Design, and Magic Animate enable teams to create branded, engaging content at scale – quickly and cost-effectively – across the entire Canva platform.
Canva Code: Low/No-Code Interactive Content. Canva Code enables users to build interactive content such as calculators, quizzes, websites, apps, and chatbots without complex coding. Combining this with Canva’s design and brand management tools lets teams create on-brand digital experiences and publish them to customers in minutes – transforming everyone into a coder and accelerating customer-facing innovation.
Why Enterprises Should Adopt Canva
Canva’s evolution into an enterprise platform offers several key advantages for larger organisations:
- Streamlined Workflows. A unified workspace and single design canvas cuts the need to switch between tools, boosting efficiency and team collaboration.
- Brand Consistency at Scale. Centralised brand controls and template governance ensure all content – from marketing to regional sales – stays on-brand. For example, eXp Realty’s central design team creates assets that agents nationwide confidently use, maintaining brand integrity.
- Scalable Content Creation. GenAI accelerates content creation and localisation, while Canva Sheets lets designers update assets at scale, reducing days of work to a single click.
- Cross-Functional Collaboration. By making design accessible, Canva empowers marketing, operations, sales, and finance teams to collaborate seamlessly on visuals, cutting bottlenecks.
- Lower Barriers to Creativity. With an easy-to-learn platform, more employees can contribute to visual storytelling without needing design expertise.
Beyond Licensing: Strategic Enterprise Adoption
Successful enterprise adoption of tools such as Canva goes beyond licensing – it requires organisational change. Here’s how enterprises can prepare:
1. Integration with the Digital Workplace Ecosystem
Enterprises must integrate new platforms with the broader toolset employees use daily. Without this, they risk becoming just another siloed app, limiting adoption and ROI.
- Enable SSO and identity management (e.g. via Azure AD or Okta).
- Integrate with storage platforms like SharePoint, Google Drive, or Box.
- Connect to collaboration and productivity tools such as Slack, Teams, Trello, and Salesforce.
2. Structured Training and Enablement
Though intuitive, enterprise features require tailored training to boost adoption and build a self-sustaining user community. Customers benefit from dedicated support – including brand kit setup, onboarding, billing, SSO configuration, and company-wide training with a dedicated Customer Success Manager.
- Deliver role-based training for marketers, HR, sales, and support.
- Establish champions in each business unit to drive adoption.
- Provide regular updates and tips as new features launch.
3. Design Governance and Brand Control
Enterprises must address concerns around brand fragmentation. This ensures that the platform acts as brand enabler – not a brand risk.
- Set up Brand Kits to enforce logos, fonts, and colours.
- Use locked templates for consistency while enabling localisation.
- Create layered permission structures to reflect organisational hierarchy.
4. Data Security, Compliance and Governance
As with any enterprise SaaS platform, security and compliance must be foundational and built into the rollout plan from day one.
- Understand data residency and privacy policies.
- Use admin controls, usage analytics, and audit logs to maintain oversight.
- Define clear policies for external sharing and publishing.
5. Defining Success Metrics
Adoption should be measured by capturing metrics that enable IT and marketing leaders to demonstrate value to the C-suite.
- Benchmark operations before and after rollout.
- Track usage, asset creation, and publishing speed.
- Monitor template use versus freeform content to gauge brand adherence.
- Survey users on productivity improvements and satisfaction.
Driving Adoption and Innovation: The Tech Team’s Mandate
For the success of tools such as Canva in enterprise settings, technology teams must move beyond gatekeeping and become proactive enablers of adoption and innovation. This involves integrating them smoothly with identity management, storage, productivity, and collaboration tools to deliver a seamless user experience. At the same time, they must enforce strict security and access controls, manage user provisioning, and monitor usage to ensure compliance and safeguard sensitive data.
But technology’s role doesn’t stop at governance. Teams need to set clear internal service standards, build strong vendor relationships, and drive consistent rollout across the organisation. Crucially, they should partner with business units to co-develop templates, embed these tools into daily workflows, and experiment with new features like AI-powered design, localisation, and self-service content creation.
Ecosystm Opinion
Canva is no longer just a tool for simple social posts or pitch decks; with its latest updates at Create 2025, it has evolved into a core platform for modern, visual-first enterprise communication. To fully realise this potential, organisations must approach Canva like any other critical enterprise platform – implementing the right structure, strategy, security, and support. For companies aiming to empower teams, speed up content creation, and maintain brand consistency at scale, Canva is now poised to take centre stage.
The energy at ServiceNow’s Knowledge25 matched the company’s ambitious direction! ServiceNow is repositioning itself as more than just an IT service platform – aiming to be the orchestration layer for the modern enterprise. Over the past two days, I’ve seen a clear focus on platform extensibility, AI-driven automation, and a push into new functional territories like CRM and ERP.
Here are my key takeaways from Knowledge25.
Click here to download “ServiceNow Knowledge25: Big Moves, Bold Bets, and What’s Next” as a PDF.
AI Everywhere: Agents and Control Towers
ServiceNow goes all in on AI Agents – and makes it easy to adopt.
Like Google, Salesforce, and AWS, ServiceNow is betting big on agents. But with a key advantage: it’s already the enterprise layer where workflows live. Its AI Agents don’t just automate tasks; they amplify what’s already working, layer in intelligence, and collaborate with other agents across systems. ServiceNow becomes the orchestration hub, just as it already is for processes and change.
ServiceNow’s AI Control Tower is a critical accelerator for AI at scale. It enforces policies, ensures compliance with internal and regulatory standards, and provides the guardrails needed to deploy AI responsibly and confidently.
The bigger move? Removing friction. Most employees don’t know what agents can do – so they don’t ask. ServiceNow solves this with hundreds of prebuilt agents across finance, risk, IT, service, CRM, and more. No guesswork. Just plug and go.
Sitting Above Silos: ServiceNow’s Architectural Advantage
ServiceNow is finally highlighting its architectural edge.
It’s one of the few platforms that can sit above all systems of record – pulling in data as needed, delivering workflows to employees and customers, and pushing updates back into core systems. While most Asia Pacific customers use ServiceNow mainly for IT help desk and service requests, its potential extends much further. Virtually anything done in ERP, CRM, SCM, or HRM systems can be delivered through ServiceNow, often with far greater agility. Workflow changes that once took weeks or months can now happen instantly.
ServiceNow is leaning into this capability more forcefully than ever, positioning itself as the platform that can finally keep pace with constant business change.
Stepping into the Ring: ServiceNow’s CRM & ERP Ambitions
ServiceNow is expanding into CRM and ERP workflows – putting itself in competition with some of the industry’s biggest players.
ServiceNow is boldly targeting CRM as a growth area, despite Salesforce’s dominance, by addressing gaps traditional CRMs miss. Customer workflows extend far beyond sales and service, spanning fulfillment, delivery, supply chain, and compliance. A simple quoting process, for instance, often pulls data from multiple systems. ServiceNow covers the full scope, positioning itself as the platform that orchestrates end-to-end customer workflows from a fundamentally different angle.
Its Core Business Suite – an AI-powered solution that transforms core processes like HR, procurement, finance, and legal – also challenges traditional ERP providers, With AI-driven automation for tasks like case management, it simplifies workflows and streamlines operations across departments.
Closing the Skills Gap: ServiceNow University
To support its vision, ServiceNow is investing heavily in education.
The refreshed ServiceNow University aims to certify 3 million professionals by 2030. This is critical to build both demand (business leaders who ask for ServiceNow) and supply (professionals who can implement and extend the platform).
But the skills shortage is a now problem, not a 2030 problem. ServiceNow must go beyond online learning and push harder on in-person classes, tutorials, and train-the-trainer programs across Asia Pacific. Major cloud providers like AWS broke through when large enterprises started training their entire workforces – not just on usage, but on development. ServiceNow needs similar scale and commitment to hit the mainstream.
Asia Pacific: ServiceNow’s Next Growth Frontier
ServiceNow’s potential is massive – and its opportunities even bigger.
In Asia Pacific, many implementations are partner-led, but most partners are currently focused on the platform’s legacy IT capabilities. To unlock growth, ServiceNow needs to empower its partners to engage beyond IT and connect with business leaders.
Despite broader challenges like shrinking tech budgets, fragmented decision-making, and decentralised tech ownership, ServiceNow has a clear path forward. By upskilling partners, simplifying its narrative, and adapting quickly, it’s well-positioned to continue its growth and surpass the hurdles many other software vendors face.
A lot has been written and spoken about DeepSeek since the release of their R1 model in January. Soon after, Alibaba, Mistral AI, and Ai2 released their own updated models, and we have seen Manus AI being touted as the next big thing to follow.
DeepSeek’s lower-cost approach to creating its model – using reinforcement learning, the mixture-of-experts architecture, multi-token prediction, group relative policy optimisation, and other innovations – has driven down the cost of LLM development. These methods are likely to be adopted by other models and are already being used today.
While the cost of AI is a challenge, it’s not the biggest for most organisations. In fact, few GenAI initiatives fail solely due to cost.
The reality is that many hurdles still stand in the way of organisations’ GenAI initiatives, which need to be addressed before even considering the business case – and the cost – of the GenAI model.
Real Barriers to GenAI
• Data. The lifeblood of any AI model is the data it’s fed. Clean, well-managed data yields great results, while dirty, incomplete data leads to poor outcomes. Even with RAG, the quality of input data dictates the quality of results. Many organisations I work with are still discovering what data they have – let alone cleaning and classifying it. Only a handful in Australia can confidently say their data is fully managed, governed, and AI-ready. This doesn’t mean GenAI initiatives must wait for perfect data, but it does explain why Agentic AI is set to boom – focusing on single applications and defined datasets.
• Infrastructure. Not every business can or will move data to the public cloud – many still require on-premises infrastructure optimised for AI. Some companies are building their own environments, but this often adds significant complexity. To address this, system manufacturers are offering easy-to-manage, pre-built private cloud AI solutions that reduce the effort of in-house AI infrastructure development. However, adoption will take time, and some solutions will need to be scaled down in cost and capacity to be viable for smaller enterprises in Asia Pacific.
• Process Change. AI algorithms are designed to improve business outcomes – whether by increasing profitability, reducing customer churn, streamlining processes, cutting costs, or enhancing insights. However, once an algorithm is implemented, changes will be required. These can range from minor contact centre adjustments to major warehouse overhauls. Change is challenging – especially when pre-coded ERP or CRM processes need modification, which can take years. Companies like ServiceNow and SS&C Blue Prism are simplifying AI-driven process changes, but these updates still require documentation and training.
• AI Skills. While IT teams are actively upskilling in data, analytics, development, security, and governance, AI opportunities are often identified by business units outside of IT. Organisations must improve their “AI Quotient” – a core understanding of AI’s benefits, opportunities, and best applications. Broad upskilling across leadership and the wider business will accelerate AI adoption and increase the success rate of AI pilots, ensuring the right people guide investments from the start.
• AI Governance. Trust is the key to long-term AI adoption and success. Being able to use AI to do the “right things” for customers, employees, and the organisation will ultimately drive the success of GenAI initiatives. Many AI pilots fail due to user distrust – whether in the quality of the initial data or in AI-driven outcomes they perceive as unethical for certain stakeholders. For example, an AI model that pushes customers toward higher-priced products or services, regardless of their actual needs, may yield short-term financial gains but will ultimately lose to ethical competitors who prioritise customer trust and satisfaction. Some AI providers, like IBM and Microsoft, are prioritising AI ethics by offering tools and platforms that embed ethical principles into AI operations, ensuring long-term success for customers who adopt responsible AI practices.
GenAI and Agentic AI initiatives are far from becoming standard business practice. Given the current economic and political uncertainty, many organisations will limit unbudgeted spending until markets stabilise. However, technology and business leaders should proactively address the key barriers slowing AI adoption within their organisations. As more AI platforms adopt the innovations that helped DeepSeek reduce model development costs, the economic hurdles to GenAI will become easier to overcome.
The promise of AI agents – intelligent programs or systems that autonomously perform tasks on behalf of people or systems – is enormous. These systems will augment and replace human workers, offering intelligence far beyond the simple RPA (Robotic Process Automation) bots that have become commonplace in recent years.
RPA and AI Agents both automate tasks but differ in scope, flexibility, and intelligence:
7 Lessons for AI Agents: Insights from RPA Deployments
However, in many ways, RPA and AI agents are similar – they both address similar challenges, albeit with different levels of automation and complexity. RPA adoption has shown that uncontrolled deployment leads to chaos, requiring a balance of governance, standardisation, and ongoing monitoring. The same principles apply to AI agent management, but with greater complexity due to AI’s dynamic and learning-based nature.
By learning from RPA’s mistakes, organisations can ensure AI agents deliver sustainable value, remain secure, and operate efficiently within a governed and well-managed environment.
#1 Controlling Sprawl with Centralised Governance
A key lesson from RPA adoption is that many organisations deployed RPA bots without a clear strategy, resulting in uncontrolled sprawl, duplicate bots, and fragmented automation efforts. This lack of oversight led to the rise of shadow IT practices, where business units created their own bots without proper IT involvement, further complicating the automation landscape and reducing overall effectiveness.
Application to AI Agents:
- Establish centralised governance early, ensuring alignment between IT and business units.
- Implement AI agent registries to track deployments, functions, and ownership.
- Enforce consistent policies for AI deployment, access, and version control.
#2 Standardising Development and Deployment
Bot development varied across teams, with different toolsets being used by different departments. This often led to poorly documented scripts, inconsistent programming standards, and difficulties in maintaining bots. Additionally, rework and inefficiencies arose as teams developed redundant bots, further complicating the automation process and reducing overall effectiveness.
Application to AI Agents:
- Standardise frameworks for AI agent development (e.g., predefined APIs, templates, and design patterns).
- Use shared models and foundational capabilities instead of building AI agents from scratch for each use case.
- Implement code repositories and CI/CD pipelines for AI agents to ensure consistency and controlled updates.
#3 Balancing Citizen Development with IT Control
Business users, or citizen developers, created RPA bots without adhering to IT best practices, resulting in security risks, inefficiencies, and technical debt. As a result, IT teams faced challenges in tracking and supporting business-driven automation efforts, leading to a lack of oversight and increased complexity in maintaining these bots.
Application to AI Agents:
- Empower business users to build and customise AI agents but within controlled environments (e.g., low-code/no-code platforms with governance layers).
- Implement AI sandboxes where experimentation is allowed but requires approval before production deployment.
- Establish clear roles and responsibilities between IT, AI governance teams, and business users.
#4 Proactive Monitoring and Maintenance
Organisations often underestimated the effort required to maintain RPA bots, resulting in failures when process changes, system updates, or API modifications occurred. As a result, bots frequently stopped working without warning, disrupting business processes and leading to unanticipated downtime and inefficiencies. This lack of ongoing maintenance and adaptation to evolving systems contributed to significant operational disruptions.
Application to AI Agents:
- Implement continuous monitoring and logging for AI agent activities and outputs.
- Develop automated retraining and feedback loops for AI models to prevent performance degradation.
- Create AI observability dashboards to track usage, drift, errors, and security incidents.
#5 Security, Compliance, and Ethical Considerations
Insufficient security measures led to data leaks and access control issues, with bots operating under overly permissive settings. Also, a lack of proactive compliance planning resulted in serious regulatory concerns, particularly within industries subject to stringent oversight, highlighting the critical need for integrating security and compliance considerations from the outset of automation deployments.
Application to AI Agents:
- Enforce role-based access control (RBAC) and least privilege access to ensure secure and controlled usage.
- Integrate explainability and auditability features to comply with regulations like GDPR and emerging AI legislation.
- Develop an AI ethics framework to address bias, ensure decision-making transparency, and uphold accountability.
#6 Cost Management and ROI Measurement
Initial excitement led to unchecked RPA investments, but many organisations struggled to measure the ROI of bots. As a result, some RPA bots became cost centres, with high maintenance costs outweighing the benefits they initially provided. This lack of clear ROI often hindered organisations from realising the full potential of their automation efforts.
Application to AI Agents:
- Define success metrics for AI agents upfront, tracking impact on productivity, cost savings, and user experience.
- Use AI workload optimisation tools to manage computing costs and avoid overconsumption of resources.
- Regularly review AI agents’ utility and retire underperforming ones to avoid AI bloat.
#7 Human Oversight and Hybrid Workflows
The assumption that bots could fully replace humans led to failures in situations where exceptions, judgment, or complex decision-making were necessary. Bots struggled to handle scenarios that required nuanced thinking or flexibility, often leading to errors or inefficiencies. The most successful implementations, however, blended human and bot collaboration, leveraging the strengths of both to optimise processes and ensure that tasks were handled effectively and accurately.
Application to AI Agents:
- Integrate AI agents into human-in-the-loop (HITL) systems, allowing humans to provide oversight and validate critical decisions.
- Establish AI escalation paths for situations where agents encounter ambiguity or ethical concerns.
- Design AI agents to augment human capabilities, rather than fully replace roles.
The lessons learned from RPA’s journey provide valuable insights for navigating the complexities of AI agent deployment. By addressing governance, standardisation, and ethical considerations, organisations
can shift from reactive problem-solving to a more strategic approach, ensuring AI tools deliver value while operating within a responsible, secure, and efficient framework.
In my previous Ecosystm Insight, I explored the Automation Paradox – how AI shifts human roles from routine tasks to more complex, high-pressure responsibilities. Now, let’s look at its impact on entry-level roles and what it means for those starting their careers.
AI is reshaping the skills mix in enterprises, automating many repetitive, lower-complexity tasks that traditionally serve as stepping stones for new professionals. Roles like Level 1 IT support or paralegal work – once common entry points – are increasingly being automated or significantly reduced.
The question now is: how will the next generation gain the experience needed to advance?
Click here to download “AI’s Unintended Consequences: Redefining Employee Skill Pathways” as a PDF
Why Are Entry-Level Roles Changing?
- Automation of Routine Tasks. AI-driven tools are taking over routine tasks. AI-driven tools and chatbots now handle common helpdesk issues instantly, eliminating the need for human intervention. Contract review software scans and analyses legal documents, cutting the workload of junior paralegals.
- Demand for Specialised Knowledge. As AI handles grunt work, remaining roles demand higher-level skills – technical, analytical, and interpersonal. For e.g., IT support shifts from password resets to configuring complex systems, interpreting AI diagnostics, and crafting custom solutions.
With routine tasks automated and remaining work more complex, traditional career entry points may shrink – or vanish entirely.
If an organisation no longer has a roster of junior positions, where will young professionals gain the foundational experience and institutional knowledge needed to excel?
The Ripple Effect on Talent & Development
Reduced Traditional Apprenticeships. Entry-level roles have historically provided new hires with an informal apprenticeship – learning basic skills, building relationships, and understanding organisational nuances. Without these roles, new talent may miss out on crucial developmental opportunities.
Potential Skills Gap. By removing the “lower rungs” of the career ladder, we risk ending up with professionals who lack broad foundational knowledge. A fully automated helpdesk, for example, might produce mid-level analysts who understand theory but have never troubleshot a live system under pressure.
Pressure to Upskill Quickly. New recruits may have to jump directly into more complex responsibilities. While this can accelerate learning, it may also create undue stress if the proper structures for training, mentoring, and support are not in place.
Strategies to Create New Skill Pathways
1. Reimagined Entry Pathways for New Employees
- Rotational Programs. One way to fill the void left by disappearing junior roles is through rotational programs. Over the course of a year, new hires cycle through different departments or projects, picking up hands-on experience even if traditional entry-level tasks are automated.
- Apprenticeship-Style Training. Instead of “on-the-job” experience tied to low-level tasks, companies can establish apprenticeship models where junior employees shadow experienced mentors on live projects. This allows them to observe complex work up close and gradually take on real responsibilities.
2. Blended Learning & Simulation
- AI-Driven Training. Ironically, AI can help solve the gap it creates. AI simulations and virtual labs can approximate real-world scenarios, giving novices a taste of troubleshooting or document review tasks.
- Certification & Micro-Credentials. More specialised skill sets may be delivered through structured learning, using platforms that provide bite-sized, verifiable credentials in areas like cybersecurity, analytics, or advanced software configuration.
- Knowledge Sharing Communities. Team chat channels, internal wikis, and regular “lunch and learn” sessions can help new employees gain the cultural and historical context they’d otherwise accumulate in junior roles.
3. Redefining Career Progression
- Competency-Based Pathways. Instead of relying on job titles (e.g. Level 1 Support), organisations can define career progression through skill mastery. Employees progress once they demonstrate competencies – through projects, assessments, or peer review – rather than simply ticking time-based boxes.
- Continuous Upskilling. Given the rapid evolution of AI, companies should encourage a culture of lifelong learning. Subsidised courses, conference attendance, and online platforms help maintain an agile, future-ready workforce.
Automation and AI hold immense promise for accelerating productivity, reducing errors, and streamlining tasks across virtually every industry. From manufacturing plants that operate robotic arms to software-driven solutions that analyse millions of data points in seconds, these technological advancements are revolutionising how we work. However, AI has already led to, and will continue to bring about, many unintended consequences.
One that has been discussed for nearly a decade but is starting to impact employees and brand experiences is the “automation paradox”. As AI and automation take on more routine tasks, employees find themselves tackling the complex exceptions and making high-stakes decisions.
What is the Automation Paradox?
1. The Shifting Burden from Low to High Value Tasks
When AI systems handle mundane or repetitive tasks, ‘human’ employees can direct their efforts toward higher-value activities. At first glance, this shift seems purely beneficial. AI helps filter out extraneous work, enabling humans to focus on the tasks that require creativity, empathy, or nuanced judgment. However, by design, these remaining tasks often carry greater responsibility. For instance, in a retail environment with automated checkout systems, a human staff member is more likely to deal with complex refund disputes or tense customer interactions. Or in a warehouse, as many processes are automated by AI and robots, humans are left with the oversight of, and responsibility for entire processes. Over time, handling primarily high-pressure situations can become mentally exhausting, contributing to job stress and potential burnout.
2. Increased Reliance on Human Judgment in Edge Cases
AI excels at pattern recognition and data processing at scale, but unusual or unprecedented scenarios can stump even the best-trained models. The human workforce is left to solve these complex, context-dependent challenges. Take self-driving cars as an example. While most day-to-day driving can be safely automated, human oversight is essential for unpredictable events – like sudden weather changes or unexpected road hazards.
Human intervention can be a critical, life-or-death matter, amplifying the pressure and stakes for those still in the loop.
3. The Fallibility Factor of AI
Ironically, as AI becomes more capable, humans may trust it too much. When systems make mistakes, it is the human operator who must detect and rectify them. But the further removed people are from the routine checks and balances – since “the system” seems to handle things so competently – the greater the chance that an error goes unnoticed until it has grown into a major problem. For instance, in the aviation industry, pilots who rely heavily on autopilot systems must remain vigilant for rare but critical emergency scenarios, which can be more taxing due to limited practice in handling manual controls.
Add to These the Known Challenges of AI!
Bias in Data and Algorithms. AI systems learn from historical data, which can carry societal and organisational biases. If left unchecked, these algorithms can perpetuate or even amplify unfairness. For instance, an AI-driven hiring platform trained on past decisions might favour candidates from certain backgrounds, unintentionally excluding qualified applicants from underrepresented groups.
Privacy and Data Security Concerns. The power of AI often comes from massive data collection, whether for predicting consumer trends or personalising user experiences. This accumulation of personal and sensitive information raises complex legal and ethical questions. Leaks, hacks, or improper data sharing can cause reputational damage and legal repercussions.
Skills Gap and Workforce Displacement. While AI can eliminate the need for certain manual tasks, it creates a demand for specialised skills, such as data science, machine learning operations, and AI ethics oversight. If an organisation fails to provide employees with retraining opportunities, it risks exacerbating skill gaps and losing valuable institutional knowledge.
Ethical and Social Implications. AI-driven decision-making can have profound impacts on communities. For example, a predictive policing system might inadvertently target specific neighbourhoods based on historical arrest data. When these systems lack transparency or accountability, public trust erodes, and social unrest can follow.
How Can We Mitigate the Known and Unknown Consequences of AI?
While some of the unintended consequences of AI and automation won’t be known until systems are deployed and processes are in practice, there are some basic hygiene approaches that technology leaders and their organisational peers can take to minimise these impacts.
- Human-Centric Design. Incorporate user feedback into AI system development. Tools should be designed to complement human skills, not overshadow them.
- Comprehensive Training. Provide ongoing education for employees expected to handle advanced AI or edge-case scenarios, ensuring they remain engaged and confident when high-stakes decisions arise.
- Robust Governance. Develop clear policies and frameworks that address bias, privacy, and security. Assign accountability to leaders who understand both technology and organisational ethics.
- Transparent Communication. Maintain clear channels of communication regarding what AI can and cannot do. Openness fosters trust, both internally and externally.
- Increase your organisational AIQ (AI Quotient). Most employees are not fully aware of the potential of AI and its opportunity to improve – or change – their roles. Conduct regular upskilling and knowledge sharing activities to improve the AIQ of your employees so they start to understand how people, plus data and technology, will drive their organisation forward.
Let me know your thoughts on the Automation Paradox, and stay tuned for my next blog on redefining employee skill pathways to tackle its challenges.
At the Nutanix .NEXT 2024 event in Barcelona, it became clear that the discourse around cloud computing has evolved significantly. The debate that once polarised organisations over whether on-prem/co-located data centres or public cloud was better has been decisively settled. Both cloud providers and on-prem equipment providers are thriving, as evident from their earnings reports.
Hybrid cloud has emerged as the clear victor, offering the flexibility and control that organisations demand. This shift is particularly relevant for tech buyers in the Asia Pacific region, where diverse market maturities and unique business challenges require a more adaptable approach to IT infrastructure.
The Hybrid Cloud Advantage
Hybrid cloud architecture combines the best of both worlds. It provides the scalability and agility of public cloud services while retaining the control and security of on-prem systems. For Asia Pacific organisations, that often operate across various regulatory environments and face unique data sovereignty issues, this dual capability is invaluable. The ability to seamlessly move workloads between on-prem, private cloud, and public cloud environments enables enterprises to optimise their IT strategies, balancing cost, performance, and compliance.
Market Maturity and Adoption in Asia Pacific
The region shows a wide spectrum of technological maturity among its markets. Countries like Australia, Japan, and Singapore lead with advanced cloud adoption and robust IT infrastructures, while emerging markets such as Vietnam, Indonesia, and the Philippines are still in the nascent stages of cloud integration.
However, regardless of their current maturity levels, organisations in Asia Pacific are recognising the benefits of a hybrid cloud approach. Mature markets are leveraging hybrid cloud to refine their IT strategies, focusing on enhancing business agility and driving innovation.
Ecosystm research shows that 75% of organisations in Australia have a hybrid, multi-cloud strategy. Over 30% of organisations have repatriated workloads from the public cloud, and only 22% employ a “cloud first” strategy when deploying new services.
Meanwhile, emerging markets see hybrid cloud as a pathway to accelerate their digital transformation journeys without the need for extensive upfront investments in on-prem infrastructure. Again, Ecosystm data shows that when it comes to training large AI models and applications, organisations across Southeast Asia use a mix of public, private, hybrid, and multi-cloud environments.
Strategic Flexibility Without Compromise
One of the most compelling messages from the Nutanix .NEXT 2024 event is that hybrid cloud eliminates the need for compromise when deciding where to place workloads – and that is what the data above represents. The location of the workload is no longer a limiting factor. Being “cloud first” locks organisations into a tech provider, whereas agility was once exclusively in favour of public cloud providers. Whether it’s for performance optimisation, cost efficiency, or regulatory compliance, tech leaders can now choose the best environment for every workload without being constrained by location.
For example, an organisation might keep sensitive customer data within a private cloud to comply with local data protection laws while leveraging public cloud resources for less sensitive applications to take advantage of its scalability and cost benefits. I recently spoke to an organisation in the gaming space that had 5 different regulatory bodies to appease – which required data to be stored in 5 different locations! This strategic flexibility ensures that IT investments are fully aligned with business objectives, enhancing overall operational efficiency.
Moving Forward: Actionable Insights for Asia Pacific Tech Leaders
To fully capitalise on the hybrid cloud revolution, APAC tech leaders should:
- Assess Workload Requirements. Evaluate the specific needs of each workload to determine the optimal environment, considering factors like latency, security, and compliance.
- Invest in Integration Tools. Ensure seamless interoperability between on-premises and cloud environments by investing in advanced integration and management tools.
- Focus on Skill Development. Equip IT teams with the necessary skills to manage hybrid cloud infrastructures, emphasising continuous learning and certification.
- Embrace a Multi-Cloud Strategy. Consider a multi-cloud approach within the hybrid model to avoid vendor lock-in and enhance resilience.
Conclusion
The hybrid cloud has definitively won the battle for enterprise IT infrastructure, particularly in the diverse Asia Pacific region. By enabling organisations to place their workloads wherever they make the most sense without compromising on performance, security, or compliance, hybrid cloud empowers tech leaders to drive their digital transformation agendas forward with confidence. Based on everything we know today*, the future of cloud is hybrid. Reform your sourcing practices to put business needs, not cloud service providers or data centres, at the centre of your data decisions.
*In this fast-changing world, it seems naïve to make sweeping statements about the future of technology!
In my earlier post this week, I referred to the need for a grown-up conversation on AI. Here, I will focus on what conversations we need to have and what the solutions to AI disruption might be.
The Impact of AI on Individuals
AI is likely to impact people a lot! You might lose your job to AI. Even if it is not that extreme, it’s likely AI will do a lot of your job. And it might not be the “boring bits” – and sometimes the boring bits make a job manageable! IT helpdesk professionals, for instance, are already reporting that AIOps means they only deal with the difficult challenges. While that might be fun to start with, some personality types find this draining, knowing that every problem that ends up in the queue might take hours or days to resolve.
Your job will change. You will need new skills. Many organisations don’t invest in their employees, so you’ll need to upskill yourself in your own time and at your own cost. Look for employers who put new skill acquisition at the core of their employee offering. They are likelier to be more successful in the medium-to-long term and will also be the better employers with a happier workforce.
The Impact of AI on Organisations
Again – the impact on organisations will be huge. It will change the shape and size of organisations. We have already seen the impact in many industries. The legal sector is a major example where AI can do much of the job of a paralegal. Even in the IT helpdesk example shared earlier, where organisations with a mature tech environment will employ higher skilled professionals in most roles. These sectors need to think where their next generation of senior employees will come from, if junior roles go to AI. Software developers and coders are seeing greater demand for their skills now, even as AI tools increasingly augment their work. However, these skills are at an inflection point, as solutions like TuringBots have already started performing developer roles and are likely to take over the job of many developers and even designers in the near future.
Some industries will find that AI helps junior roles act more like senior employees, while others will use AI to perform the junior roles. AI will also create new roles (such as “prompt engineers”), but even those jobs will be done by AI in the future (and we are starting to see that).
HR teams, senior leadership, and investors need to work together to understand what the future might look like for their organisations. They need to start planning today for that future. Hint: invest in skills development and acquisition – that’s what will help you to succeed in the future.
The Impact of AI on the Economy
Assuming the individual and organisational impacts play out as described, the economic impacts of widespread AI adoption will be significant, similar to the “Great Depression”. If organisations lay off 30% of their employees, that means 30% of the economy is impacted, potentially leading to drying up of some government and an increase in government spend on welfare etc. – basically leading to major societal disruption.
The “AI won’t displace workers” narrative strikes me as the technological equivalent of climate change denial. Just like ignoring environmental warnings, dismissing the potential for AI to significantly impact the workforce is a recipe for disaster. Let’s not fall into the same trap and be an “AI denier”.
What is the Solution?
The solutions revolve around two ideas, and these need to be adopted at an industry level and driven by governments, unions, and businesses:
- Pay a living salary (for all citizens). Some countries already do this, with the Nordic nations leading the charge. And it is no surprise that some of these countries have had the most consistent long-term economic growth. The challenge today is that many governments cannot afford this – and it will become even less affordable as unemployment grows. The solution? Changing tax structures, taxing organisational earnings in-country (to stop them recognising local earnings in low-tax locations), and taxing wealth (not incomes). Also, paying essential workers who will not be replaced by AI (nurses, police, teachers etc.) better salaries will also help keep economies afloat. Easier said than done, of course!
- Move to a shorter work week (but pay full salaries). It is in the economic interest of every organisation that people stay gainfully employed. We have already discussed the ripple effect of job cuts. But if employees are given more flexibility, and working 3-day weeks, this not only spreads the work around more workers, but means that these workers have more time to spend money – ensuring continuing economic growth. Can every company do this? Probably not. But many can and they might have to. The concept of a 5-day work week isn’t that old (less than 100 years in fact – a 40-hour work week was only legislated in the US in the 1930s, and many companies had as little as 6-hour working days even in the 1950s). Just because we have worked this way for 80 years doesn’t mean that we will always have to. There is already a move towards 4-day work weeks. Tech.co surveyed over 1,000 US business leaders and found that 29% of companies with 4-day workweeks use AI extensively. In contrast, only 8% of organisations with a 5-day workweek use AI to the same degree.
AI Changes Everything
We are only at the beginning of the AI era. We have had a glimpse into the future, and it is both frightening and exciting. The opportunities for organisations to benefit from AI are already significant and will become even more as the technology improves and businesses learn to better adopt AI in areas where it can make an impact. But there will be consequences to this adoption. We already know what many of those consequences will be, so let’s start having those grown-up conversations today.
If you have seen me present recently – or even spoken to me for more than a few minutes, you’ve probably heard me go on about how the AI discussions need to change! At the moment, most senior executives, board rooms, governments, think tanks and tech evangelists are running around screaming with their hands on their ears when it comes to the impact of AI on jobs and society.
We are constantly being bombarded with the message that AI will help make knowledge workers more productive. AI won’t take people’s jobs – in fact it will help to create new jobs – you get the drift; you’ve been part of these conversations!
I was at an event recently where a leading cloud provider had a huge slide with the words: “Humans + AI Together” in large font across the screen. They then went on to demonstrate an opportunity for AI. In a live demo, they had the customer of a retailer call a store to check for stock of a dress. The call was handled by an AI solution, which engaged in a natural conversation with the customer. It verified their identity, checked dress stock at the store, processed the order, and even confirmed the customer’s intent to use their stored credit card.
So, in effect, on one slide, the tech provider emphasised that AI was not going to take our jobs, and two minutes later they showed how current AI capabilities could replace humans – today!
At an analyst event last week, representatives from three different tech providers told analysts how Microsoft Copilot is freeing up 10-15 hours a week. For a 40-hour work week, that’s a 25-38 time saving. In France (where the work week is 35 hours), that’s up to 43% of their time saved. So, by using a single AI platform, we can save 25-43% of our time – giving us the ability to work on other things.
What are the Real Benefits of AI?
The critical question is: What will we do with this saved time? Will it improve revenue or profit for businesses? AI might make us more agile, faster, more innovative but unless that translates to benefits on the bottom line, it is pointless. For example, adopting AI might mean we can create three times as many products. However, if we don’t make any more revenue and/or profit by having three times as many products, then any productivity benefit is worthless. UNLESS it is delivered through decreased costs.
We won’t need as many humans in our contact centres if AI is taking calls. Ideally, AI will lead to more personalised customer experiences – which will drive less calls to the contact centre in the first place! Even sales-related calls may disappear as personal AI bots will find deals and automatically sign us up. Of course, AI also costs money, particularly in terms of computing power. Some of the productivity uplift will be offset by the extra cost of the AI tools and platforms.
Many benefits that AI delivers will become table stakes. For example, if your competitor is updating their product four times a year and you are updating it annually, you might lose market share – so the benefits of AI might be just “keeping up with the competition”. But there are many areas where additional activity won’t deliver benefits. Organisations are unlikely to benefit from three times more promotional SMSs or EDMs and design work or brand redesigns.
I also believe that AI will create new roles. But you know what? AI will eventually do those jobs too. When automation came to agriculture, workers moved to factories. When automation came to factories, workers moved to offices. The (literally) trillion-dollar question is where workers go when automation comes to the office.
The Wider Impact of AI
The issue is that very few senior people in businesses or governments are planning for a future where maybe 30% of jobs done by knowledge workers go to AI. This could lead to the failure of economies. Government income will fall off a cliff. It will be unemployment on levels not seen since the great depression – or worse. And if we have not acknowledged these possible outcomes, how can we plan for it?
This is what I call the “grown up conversation about AI”. This is acknowledging the opportunity for AI and its impacts on companies, industries, governments and societies. Once we acknowledge these likely outcomes we can plan for it.
And that’s what I’ll discuss shortly – look out for my next Ecosystm Insight: The Three Possible Solutions for AI-driven Mass Unemployment.