Ecosystm research shows that cybersecurity is the most discussed technology at the Board and Management level, driven by the increasing sophistication of cyber threats and the rapid adoption of AI. While AI enhances security, it also introduces new vulnerabilities. As organisations face an evolving threat landscape, they are adopting a more holistic approach to cybersecurity, covering prevention, detection, response, and recovery.
In 2025, cybersecurity leaders will continue to navigate a complex mix of technological advancements, regulatory pressures, and changing business needs. To stay ahead, organisations will prioritise robust security solutions, skilled professionals, and strategic partnerships.
Ecosystm analysts Darian Bird, Sash Mukherjee, and Simona Dimovski present the key cybersecurity trends for 2025.
Click here to download ‘Securing the AI Frontier: Top 5 Cyber Trends for 2025’ as a PDF
1. Cybersecurity Will Be a Critical Differentiator in Corporate Strategy
The convergence of geopolitical instability, cyber weaponisation, and an interconnected digital economy will make cybersecurity a cornerstone of corporate strategy. State-sponsored cyberattacks targeting critical infrastructure, supply chains, and sensitive data have turned cyber warfare into an operational reality, forcing businesses to prioritise security.
Regulatory pressures are driving this shift, mandating breach reporting, data sovereignty, and significant penalties, while international cybersecurity norms compel companies to align with evolving standards to remain competitive.
The stakes are high. Stakeholders now see cybersecurity as a proxy for trust and resilience, scrutinising both internal measures and ecosystem vulnerabilities.
2. Zero Trust Architectures Will Anchor AI-Driven Environments
The future of cybersecurity lies in never trusting, always verifying – especially where AI is involved.
In 2025, the rise of AI-driven systems will make Zero Trust architectures vital for cybersecurity. Unlike traditional networks with implicit trust, AI environments demand stricter scrutiny due to their reliance on sensitive data, autonomous decisions, and interconnected systems. The growing threat of adversarial attacks – data poisoning, model inversion, and algorithmic manipulation – highlights the urgency of continuous verification.
Global forces are driving this shift. Regulatory mandates like the EU’s DORA, the US Cybersecurity Executive Order, and the NIST Zero Trust framework call for robust safeguards for critical systems. These measures align with the growing reliance on AI in high-stakes sectors like Finance, Healthcare, and National Security.
3. Organisations Will Proactively Focus on AI Governance & Data Privacy
Organisations are caught between excitement and uncertainty regarding AI. While the benefits are immense, businesses struggle with the complexities of governing AI. The EU AI Act looms large, pushing global organisations to brace for stricter regulations, while a rise in shadow IT sees business units bypassing traditional IT to deploy AI independently.
In this environment of regulatory ambiguity and organisational flux, CISOs and CIOs will prioritise data privacy and governance, proactively securing organisations with strong data frameworks and advanced security solutions to stay ahead of emerging regulations.
Recognising that AI will be multi-modal, multi-vendor, and hybrid, organisations will invest in model orchestration and integration platforms to simplify management and ensure smoother compliance.
4. Network & Security Stacks Will Streamline Through Converged Platforms
This shift stems from the need for unified management, cost efficiency, and the recognition that standardisation enhances security posture.
Tech providers are racing to deliver comprehensive network and security platforms.
Recent M&A moves by HPE (Juniper), Palo Alto Networks (QRadar SaaS), Fortinet (Lacework), and LogRhythm (Exabeam) highlight this trend. Rising player Cato Networks is capitalising on mid-market demand for single-provider solutions, with many customers planning to consolidate vendors in their favour. Meanwhile, telecoms are expanding their SASE offerings to support organisations adapting to remote work and growing cloud adoption.
5. AI Will Be Widely Used to Combat AI-Powered Threats in Real-time
By 2025, the rise of AI-powered cyber threats will demand equally advanced AI-driven defences.
Threat actors are using AI to launch adaptive attacks like deepfake fraud, automated phishing, and adversarial machine learning, operating at a speed and scale beyond traditional defences.
Real-time AI solutions will be essential for detection and response.
Nation-state-backed advanced persistent threat (APT) groups and GenAI misuse are intensifying these challenges, exploiting vulnerabilities in critical infrastructure and supply chains. Mandatory reporting and threat intelligence sharing will strengthen AI defences, enabling real-time adaptation to emerging threats.
At a recently held Ecosystm roundtable, in partnership with Qlik and 121Connects, Ecosystm Principal Advisor Manoj Chugh, moderated a conversation where Indian tech and data leaders discussed building trust in data strategies. They explored ways to automate data pipelines and improve governance to drive better decisions and business outcomes. Here are the key takeaways from the session.
Data isn’t just a byproduct anymore; it’s the lifeblood of modern businesses, fuelling informed decisions and strategic growth. But with vast amounts of data, the challenge isn’t just managing it; it’s building trust. AI, once a beacon of hope, is now at risk without a reliable data foundation. Ecosystm research reveals that a staggering 66% of Indian tech leaders doubt their organisation’s data quality, and the problem of data silos is exacerbating this trust crisis.
At the Leaders Roundtable in Mumbai, I had the opportunity to moderate a discussion among data and digital leaders on the critical components of building trust in data and leveraging it to drive business value. The consensus was that building trust requires a comprehensive strategy that addresses the complexities of data management and positions the organisation for future success. Here are the key strategies that are essential for achieving these goals.
1. Adopting a Unified Data Approach
Organisations are facing a growing wave of complex workloads and business initiatives. To manage this expansion, IT teams are turning to multi-cloud, SaaS, and hybrid environments. However, this diverse landscape introduces new challenges, such as data silos, security vulnerabilities, and difficulties in ensuring interoperability between systems.
A unified data strategy is crucial to overcome these challenges. By ensuring platform consistency, robust security, and seamless data integration, organisations can simplify data management, enhance security, and align with business goals – driving informed decisions, innovation, and long-term success.
Real-time data integration is essential for timely data availability, enabling organisations to make data-driven decisions quickly and effectively. By integrating data from various sources in real-time, businesses can gain valuable insights into their operations, identify trends, and respond to changing market conditions.
Organisations that are able to integrate their IT and operational technology (OT) systems find their data accuracy increasing. By combining IT’s digital data management expertise with OT’s real-time operational insights, organisations can ensure more accurate, timely, and actionable data. This integration enables continuous monitoring and analysis of operational data, leading to faster identification of errors, more precise decision-making, and optimised processes.
2. Enhancing Data Quality with Automation and Collaboration
As the volume and complexity of data continue to grow, ensuring high data quality is essential for organisations to make accurate decisions and to drive trust in data-driven solutions. Automated data quality tools are useful for cleansing and standardising data to eliminate errors and inconsistencies.
As mentioned earlier, integrating IT and OT systems can help organisations improve operational efficiency and resilience. By leveraging data-driven insights, businesses can identify bottlenecks, optimise workflows, and proactively address potential issues before they escalate. This can lead to cost savings, increased productivity, and improved customer satisfaction.
However, while automation technologies can help, organisations must also invest in training employees in data management, data visualisation, and data governance.
3. Modernising Data Infrastructure for Agility and Innovation
In today’s fast-paced business landscape, agility is paramount. Modernising data infrastructure is essential to remain competitive – the right digital infrastructure focuses on optimising costs, boosting capacity and agility, and maximising data leverage, all while safeguarding the organisation from cyber threats. This involves migrating data lakes and warehouses to cloud platforms and adopting advanced analytics tools. However, modernisation efforts must be aligned with specific business goals, such as enhancing customer experiences, optimising operations, or driving innovation. A well-modernised data environment not only improves agility but also lays the foundation for future innovations.
Technology leaders must assess whether their data architecture supports the organisation’s evolving data requirements, considering factors such as data flows, necessary management systems, processing operations, and AI applications. The ideal data architecture should be tailored to the organisation’s specific needs, considering current and future data demands, available skills, costs, and scalability.
4. Strengthening Data Governance with a Structured Approach
Data governance is crucial for establishing trust in data, and providing a framework to manage its quality, integrity, and security throughout its lifecycle. By setting clear policies and processes, organisations can build confidence in their data, support informed decision-making, and foster stakeholder trust.
A key component of data governance is data lineage – the ability to trace the history and transformation of data from its source to its final use. Understanding this journey helps organisations verify data accuracy and integrity, ensure compliance with regulatory requirements and internal policies, improve data quality by proactively addressing issues, and enhance decision-making through context and transparency.
A tiered data governance structure, with strategic oversight at the executive level and operational tasks managed by dedicated data governance councils, ensures that data governance aligns with broader organisational goals and is implemented effectively.
Are You Ready for the Future of AI?
The ultimate goal of your data management and discovery mechanisms is to ensure that you are advancing at pace with the industry. The analytics landscape is undergoing a profound transformation, promising to revolutionise how organisations interact with data. A key innovation, the data fabric, is enabling organisations to analyse unstructured data, where the true value often lies, resulting in cleaner and more reliable data models.
GenAI has emerged as another game-changer, empowering employees across the organisation to become citizen data scientists. This democratisation of data analytics allows for a broader range of insights and fosters a more data-driven culture. Organisations can leverage GenAI to automate tasks, generate new ideas, and uncover hidden patterns in their data.
The shift from traditional dashboards to real-time conversational tools is also reshaping how data insights are delivered and acted upon. These tools enable users to ask questions in natural language, receiving immediate and relevant answers based on the underlying data. This conversational approach makes data more accessible and actionable, empowering employees to make data-driven decisions at all levels of the organisation.
To fully capitalise on these advancements, organisations need to reassess their AI/ML strategies. By ensuring that their tech initiatives align with their broader business objectives and deliver tangible returns on investment, organisations can unlock the full potential of data-driven insights and gain a competitive edge. It is equally important to build trust in AI initiatives, through a strong data foundation. This involves ensuring data quality, accuracy, and consistency, as well as implementing robust data governance practices. A solid data foundation provides the necessary groundwork for AI and GenAI models to deliver reliable and valuable insights.
“AI Guardrails” are often used as a method to not only get AI programs on track, but also as a way to accelerate AI investments. Projects and programs that fall within the guardrails should be easy to approve, govern, and manage – whereas those outside of the guardrails require further review by a governance team or approval body. The concept of guardrails is familiar to many tech businesses and are often applied in areas such as cybersecurity, digital initiatives, data analytics, governance, and management.
While guidance on implementing guardrails is common, organisations often leave the task of defining their specifics, including their components and functionalities, to their AI and data teams. To assist with this, Ecosystm has surveyed some leading AI users among our customers to get their insights on the guardrails that can provide added value.
Data Security, Governance, and Bias
- Data Assurance. Has the organisation implemented robust data collection and processing procedures to ensure data accuracy, completeness, and relevance for the purpose of the AI model? This includes addressing issues like missing values, inconsistencies, and outliers.
- Bias Analysis. Does the organisation analyse training data for potential biases – demographic, cultural and so on – that could lead to unfair or discriminatory outputs?
- Bias Mitigation. Is the organisation implementing techniques like debiasing algorithms and diverse data augmentation to mitigate bias in model training?
- Data Security. Does the organisation use strong data security measures to protect sensitive information used in training and running AI models?
- Privacy Compliance. Is the AI opportunity compliant with relevant data privacy regulations (country and industry-specific as well as international standards) when collecting, storing, and utilising data?
Model Development and Explainability
- Explainable AI. Does the model use explainable AI (XAI) techniques to understand and explain how AI models reach their decisions, fostering trust and transparency?
- Fair Algorithms. Are algorithms and models designed with fairness in mind, considering factors like equal opportunity and non-discrimination?
- Rigorous Testing. Does the organisation conduct thorough testing and validation of AI models before deployment, ensuring they perform as intended, are robust to unexpected inputs, and avoid generating harmful outputs?
AI Deployment and Monitoring
- Oversight Accountability. Has the organisation established clear roles and responsibilities for human oversight throughout the AI lifecycle, ensuring human control over critical decisions and mitigation of potential harm?
- Continuous Monitoring. Are there mechanisms to continuously monitor AI systems for performance, bias drift, and unintended consequences, addressing any issues promptly?
- Robust Safety. Can the organisation ensure AI systems are robust and safe, able to handle errors or unexpected situations without causing harm? This includes thorough testing and validation of AI models under diverse conditions before deployment.
- Transparency Disclosure. Is the organisation transparent with stakeholders about AI use, including its limitations, potential risks, and how decisions made by the system are reached?
Other AI Considerations
- Ethical Guidelines. Has the organisation developed and adhered to ethical principles for AI development and use, considering areas like privacy, fairness, accountability, and transparency?
- Legal Compliance. Has the organisation created mechanisms to stay updated on and compliant with relevant legal and regulatory frameworks governing AI development and deployment?
- Public Engagement. What mechanisms are there in place to encourage open discussion and engage with the public regarding the use of AI, addressing concerns and building trust?
- Social Responsibility. Has the organisation considered the environmental and social impact of AI systems, including energy consumption, ecological footprint, and potential societal consequences?
Implementing these guardrails requires a comprehensive approach that includes policy formulation, technical measures, and ongoing oversight. It might take a little longer to set up this capability, but in the mid to longer term, it will allow organisations to accelerate AI implementations and drive a culture of responsible AI use and deployment.
2024 has started cautiously for organisations, with many choosing to continue with tech projects that have already initiated, while waiting for clearer market conditions before starting newer transformation projects. This means that tech providers must continue to refine their market messaging and enhance their service/product offerings to strengthen their market presence in the latter part of the year. Ecosystm analysts present five key considerations for tech providers as they navigate evolving market and customer trends, this year.
Navigating Market Dynamics
Continuing Economic Uncertainties. Organisations will focus on ongoing projects and consider expanding initiatives in the latter part of the year. This means that tech providers should maintain visibility and trust with existing clients. They also need to help their customers meet multiple KPIs.
Popularity of Generative AI. For organisations, this will be the time to go beyond the novelty factor and assess practical business outcomes, allied costs, and change management. Tech providers need to include ROI discussions for short-term and mid-term perspectives as organisations move beyond pilots.
Infrastructure Market Disruption. Tech leaders will keep an eye out for advancements and disruptions in the market (likely to originate from the semiconductor sector). The disruptions might require tech vendors to re-assess the infrastructure partner ecosystem.
Need for New Tech Skills. Tech leaders will evaluate Generative AI’s impact on AIOps and IT Architecture; invest in upskilling for talent retention. Tech providers must prioritise creating user-friendly experiences to make technology accessible to business users. Training and partner enablement will also need a higher focus.
Increased Focus on Governance. Tech leaders will consult tech vendors on how to implement safeguards for data usage, sharing, and cybersecurity. This opens up opportunities in offering governance-related services.
5 Key Considerations for Tech Vendors
#1 Get Ready for the Year of the AI Startup
While many AI companies have been around for years, this will be the year that many of them make a significant play into enterprises in Asia Pacific. This comes at a time when many organisations are attempting to reduce tech debt and simplify their tech architecture.
For these AI startups to succeed, they will need to create watertight business cases, and do a lot of the hard work in pre-integrating their solutions with the larger platforms to reduce the time to value and simplify the systems integration work.
To respond to these emerging threats, existing tech providers will need to not only accelerate their own use of AI in their platforms, but also ramp up the education and promotion of these capabilities.
#2 Lead With Data, Not AI Capabilities
Organisations recognise the need for AI to enhance their workforce, improve customer experience, and automate processes. However, the initial challenge lies in improving data quality, as trust in early AI models hinges on high-quality training data for long-term success.
Tech vendors that can help with data source discovery, metadata analysis, and seamless data pipeline creation will emerge as trusted AI partners. Transformation tools that automate deduplication and quality assurance tasks empower data scientists to focus on high-value work. Automation models like Segment Anything enhance unstructured data labeling, particularly for images. Finally synthetic data will gain importance as quality sources become scarce.
Tech vendors will be tempted to capitalise on the Generative AI hype but for sake of positive early experiences, they should begin with data quality.
#3 Prepare Thoroughly for AI-driven Business Demand
Besides pureplay AI opportunities, AI will drive a renewed and increased interest in data and data management. Tech and service providers can capitalise on this by understanding the larger picture around their clients’ data maturity and governance. Initial conversations around AI can be door openers to bigger, transformational engagements.
Tech vendors should avoid the pitfall of downplaying AI risks. Instead, they should make all efforts to own and drive the conversation with their clients. They need to be forthcoming about their in-house responsible AI guidelines and understand what is happening in AI legislation world-wide (hint: a lot!)
Tech providers must establish strong client partnerships for AI initiatives to succeed. They must address risk and benefit equally to reap the benefits of larger AI-driven transformation engagements.
#4 Converge Network & Security Capabilities
Networking and security vendors will need to develop converged offerings as these two technologies increasingly overlap in the hybrid working era. Organisations are now entering a new phase of maturity as they evolve their remote working policies and invest in tools to regain control. They will require simplified management, increased visibility, and to provide a consistent user experience, wherever employees are located.
There has already been a widespread adoption of SD-WAN and now organisations are starting to explore next generation SSE technologies. Procuring these capabilities from a single provider will help to remove complexity from networks as the number of endpoints continue to grow.
Tech providers should take a land and expand approach, getting a foothold with SASE modules that offer rapid ROI. They should focus on SWG and ZTNA deals with an eye to expanding in CASB and FWaaS, as customers gain experience.
#5 Double Down on Your Partner Ecosystem
The IT services market, particularly in Asia Pacific, is poised for significant growth. Factors, including the imperative to cut IT operational costs, the growing complexity of cloud migrations and transformations, change management for Generative AI capabilities, and rising security and data governance needs, will drive increased spending on IT services.
Tech services providers – consultants, SIs, managed services providers, and VARs – will help drive organisations’ tech spend and strategy. This is a good time to review partners, evaluating whether they can take the business forward, or whether there is a need to expand or change the partner mix.
Partner reviews should start with an evaluation of processes and incentives to ensure they foster desired behaviour from customers and partners. Tech vendors should develop a 21st century partner program to improve chances of success.
2024 will be another crucial year for tech leaders – through the continuing economic uncertainties, they will have to embrace transformative technologies and keep an eye on market disruptors such as infrastructure providers and AI startups. Ecosystm analysts outline the key considerations for leaders shaping their organisations’ tech landscape in 2024.
Navigating Market Dynamics
Continuing Economic Uncertainties. Organisations will focus on ongoing projects and consider expanding initiatives in the latter part of the year.
Popularity of Generative AI. This will be the time to go beyond the novelty factor and assess practical business outcomes, allied costs, and change management.
Infrastructure Market Disruption. Keeping an eye out for advancements and disruptions in the market (likely to originate from the semiconductor sector) will define vendor conversations.
Need for New Tech Skills. Generative AI will influence multiple tech roles, including AIOps and IT Architecture. Retaining talent will depend on upskilling and reskilling.
Increased Focus on Governance. Tech vendors are guide tech leaders on how to implement safeguards for data usage, sharing, and cybersecurity.
5 Key Considerations for Tech Leaders
#1 Accelerate and Adapt: Streamline IT with a DevOps Culture
Over the next 12-18 months, advancements in AI, machine learning, automation, and cloud-native technologies will be vital in leveraging scalability and efficiency. Modernisation is imperative to boost responsiveness, efficiency, and competitiveness in today’s dynamic business landscape.
The continued pace of disruption demands that organisations modernise their applications portfolios with agility and purpose. Legacy systems constrained by technical debt drag down velocity, impairing the ability to deliver new innovative offerings and experiences customers have grown to expect.
Prioritising modernisation initiatives that align with key value drivers is critical. Technology leaders should empower development teams to move beyond outdated constraints and swiftly deploy enhanced applications, microservices, and platforms.
#2 Empowering Tomorrow: Spring Clean Your Tech Legacy for New Leaders
Modernising legacy systems is a strategic and inter-generational shift that goes beyond simple technical upgrades. It requires transformation through the process of decomposing and replatforming systems – developed by previous generations – into contemporary services and signifies a fundamental realignment of your business with the evolving digital landscape of the 21st century.
The essence of this modernisation effort is multifaceted. It not only facilitates the integration of advanced technologies but also significantly enhances business agility and drives innovation. It is an approach that prepares your organisation for impending skill gaps, particularly as the older workforce begins to retire over the next decade. Additionally, it provides a valuable opportunity to thoroughly document, reevaluate, and improve business processes. This ensures that operations are not only efficient but also aligned with current market demands, contemporary regulatory standards, and the changing expectations of customers.
#3 Employee Retention: Consider the Strategic Role of Skills Acquisition
The agile, resilient organisation needs to be able to respond at pace to any threat or opportunity it faces. Some of this ability to respond will be related to technology platforms and architectures, but it will be the skills of employees that will dictate the pace of reform. While employee attrition rates will continue to decline in 2024 – but it will be driven by skills acquisition, not location of work.
Organisations who offer ongoing staff training – recognising that their business needs new skills to become a 21st century organisation – are the ones who will see increasing rates of employee retention and happier employees. They will also be the ones who offer better customer experiences, driven by motivated employees who are committed to their personal success, knowing that the organisation values their performance and achievements.
#4 Next-Gen IT Operations: Explore Gen AI for Incident Avoidance and Predictive Analysis
The integration of Generative AI in IT Operations signifies a transformative shift from the automation of basic tasks, to advanced functions like incident avoidance and predictive analysis. Initially automating routine tasks, Generative AI has evolved to proactively avoiding incidents by analysing historical data and current metrics. This shift from proactive to reactive management will be crucial for maintaining uninterrupted business operations and enhancing application reliability.
Predictive analysis provides insight into system performance and user interaction patterns, empowering IT teams to optimise applications pre-emptively, enhancing efficiency and user experience. This also helps organisations meet sustainability goals through accurate capacity planning and resource allocation, also ensuring effective scaling of business applications to meet demands.
#5 Expanding Possibilities: Incorporate AI Startups into Your Portfolio
While many of the AI startups have been around for over five years, this will be the year they come into your consciousness and emerge as legitimate solutions providers to your organisation. And it comes at a difficult time for you!
Most tech leaders are looking to reduce technical debt – looking to consolidate their suppliers and simplify their tech architecture. Considering AI startups will mean a shift back to more rather than fewer tech suppliers; a different sourcing strategy; more focus on integration and ongoing management of the solutions; and a more complex tech architecture.
To meet business requirements will mean that business cases will need to be watertight – often the value will need to be delivered before a contract has been signed.
In our previous Ecosystm Insights, Ecosystm Principal Advisor, Gerald Mackenzie, highlighted the key drivers for boosting ESG maturity and the need to transition from standalone ESG projects to integrating ESG goals into organisational strategy and operations.
This shift can be difficult, requiring an alignment of ESG objectives with broader strategic aims and using organisational capabilities effectively. The solution involves prioritising essential goals, knitting them into overall business strategy, quantifying success metrics, and establishing incentives and governance for effective execution.
The benefits are proven and significant. Stronger Customer and Employee Value Propositions, better bottom line, improved risk profile, and more attractive enterprise valuations for investors and lenders.
According to Gerald, here are 5 things to keep in mind when starting on an ESG journey.
Download ‘Embedding Sustainability in Corporate Strategy and Operations’ as a PDF
In an era marked by heightened global awareness of environmental, social, and governance (ESG) issues, organisations find themselves at a crossroad where profitability converges with responsibility. The imperative to take resolute action on ESG fronts is underscored by a compelling array of statistics and evidence that highlight the profound impact of these considerations on long-term success.
A 2020 McKinsey report revealed that executives and investors value companies with robust ESG performance around 10% higher in valuations than laggards. Equally pivotal, workplace diversity is now recognised as a strategic advantage; a study in the Harvard Business Review finds that companies with above-average total diversity had both 19% higher innovation revenues and 9% higher EBIT margins, on average. Against this backdrop, organisations must recognise that embracing ESG principles is not merely an ethical gesture but a strategic imperative that safeguards resilience, reputation, and enduring financial prosperity.
The data from the ongoing Ecosystm State of ESG Adoption study was used to evaluate the status and maturity of organisations’ ESG strategy and implementation progress. A diverse representation across industries such as Financial Services, Manufacturing, and Retail & eCommerce, as well as from roles across the organisation has helped us with insights and an understanding of where organisations stand in terms of the maturity of their ESG strategy and implementation efforts.
A Tailored Approach to Improve ESG Maturity
Ecosystm assists clients in driving greater impact through their ESG adoption. Our tools evaluate an organisation’s aspirations and roadmaps using a maturity model, along with a series of practical drivers that enhance ESG response maturity. The maturity of an organisation’s approach on ESG tends to progress from a reactive, or risk/compliance-based focus, to a customer, or opportunity driven approach, to a purpose led approach that is focused on embedding ESG into the core culture of the organisation. Our advisory, research and consulting offerings are customised to the transitions organisations are seeking to make in their maturity levels over time.
Within the maturity framework outlined above, Ecosystm has identified the key organisational drivers to improve maturity and adoption. The Ecosystm ESG Consulting offerings are configured to both support the development of ESG strategy and the delivery and ‘story telling’ around ESG programs based on the goals of the customer (maturity aspiration) and the gaps they need to close to deliver the aspiration.
Key Findings of the Ecosystm State of ESG Study
89% of respondents self-reported that their organisation had an ESG strategy; however, a notable 60% also identified that a lack of alignment of sustainability goals to enterprise strategy was a key issue in implementation. This reflects many of the client discussions we’ve had, where customers share that ESG goals that have not been fully tested against other organisational priorities can create tensions and make it difficult to solve trade-offs across the organisation during implementation.
People & Leadership/Execution & Governance
Capabilities are still emerging. 40% of respondents mentioned that a lack of a governance framework for ESG was a barrier to adoption, and 56% mentioned that immature metrics and reporting maturity slowed adoption. 64% of respondents also mentioned that a lack of specialised resources as a key barrier to ESG adoption.
In our discussions with customers, we understand that there is good support for ESG across organisations, but there needs to be a simple narrative compelling them to action on a few clearly articulated priorities, a clear mandate from senior leadership and credible resourcing and governance to ensure follow through.
Data and Technology Enablement
There is a strong opportunity for improvement. “We can’t manage what we cannot measure” has been the common refrain from the clients we have spoken to and the survey reflected this. Only 47% of respondents say that preparing data, analytics, reporting, and metrics for internal consumption is a priority for their tech teams.
ESG is rapidly emerging as a key priority for customers, investors, talent, and other stakeholders who seek a comprehensive and genuine commitment from the organisations they interact with. Successfully determining the right priorities and effectively mobilising your organisation and external collaborators for implementation are pivotal. It’s crucial to acknowledge the intricacy and extent of effort needed for this endeavour.
With our timely research findings complementing our ESG maturity and implementation frameworks, analyst insights and consulting support, Ecosystm is well-positioned to help you to navigate your journey to ESG maturity.
As an industry, the tech sector tends to jump on keywords and terms – and sometimes reshapes their meaning and intention. “Sustainable” is one of those terms. Technology vendors are selling (allegedly!) “sustainable software/hardware/services/solutions” – in fact, the focus on “green” or “zero carbon” or “recycled” or “circular economy” is increasing exponentially at the moment. And that is good news – as I mentioned in my previous post, we need to significantly reduce greenhouse gas emissions if we want a future for our kids. But there is a significant disconnect between the way tech vendors use the word “sustainable” and the way it is used in boardrooms and senior management teams of their clients.
Defining Sustainability
For organisations, Sustainability is a broad business goal – in fact for many, it is the over-arching goal. A sustainable organisation operates in a way that balances economic, social, and environmental (ESG) considerations. Rather than focusing solely on profits, a sustainable organisation aims to meet the needs of the present without compromising the ability of future generations to meet their own needs.
This is what building a “Sustainable Organisation” typically involves:
Economic Sustainability. The organisation must be financially stable and operate in a manner that ensures long-term economic viability. It doesn’t just focus on short-term profits but invests in long-term growth and resilience.
Social Sustainability. This involves the organisation’s responsibility to its employees, stakeholders, and the wider community. A sustainable organisation will promote fair labour practices, invest in employee well-being, foster diversity and inclusion, and engage in ethical decision-making. It often involves community engagement and initiatives that support societal growth and well-being.
Environmental Sustainability. This facet includes the responsible use of natural resources and minimising negative impacts on the environment. A sustainable organisation seeks to reduce its carbon footprint, minimise waste, enhance energy efficiency, and often supports or initiates activities that promote environmental conservation.
Governance and Ethical Considerations. Sustainable organisations tend to have transparent and responsible governance. They follow ethical business practices, comply with laws and regulations, and foster a culture of integrity and accountability.
Security and Resilience. Sustainable organisations have the ability to thwart bad actors – and in the situation that they are breached, to recover from these breaches quickly and safely. Sustainable organisations can survive cybersecurity incidents and continue to operate when breaches occur, with the least impact.
Long-Term Focus. Sustainability often requires a long-term perspective. By looking beyond immediate gains and considering the long-term impact of decisions, a sustainable organisation can better align its strategies with broader societal goals.
Stakeholder Engagement. Understanding and addressing the needs and concerns of different stakeholders (including employees, customers, suppliers, communities, and shareholders) is key to sustainability. This includes open communication and collaboration with these groups to foster relationships based on trust and mutual benefit.
Adaptation and Innovation. The organisation is not static and recognises the need for continual improvement and adaptation. This might include innovation in products, services, or processes to meet evolving sustainability standards and societal expectations.
Alignment with the United Nations’ Sustainable Development Goals (UNSDGs). Many sustainable organisations align their strategies and operations with the UNSDGs which provide a global framework for addressing sustainability challenges.
Organisations Appreciate Precise Messaging
A sustainable organisation is one that integrates economic, social, and environmental considerations into all aspects of its operations. It goes beyond mere compliance with laws to actively pursue positive impacts on people and the planet, maintaining a balance that ensures long-term success and resilience.
These factors are all top of mind when business leaders, boards and government agencies use the word “sustainable”. Helping organisations meet their emission reduction targets is a good starting point – but it is a long way from all businesses need to become sustainable organisations.
Tech providers need to reconsider their use of the term “sustainable” – unless their solution or service is helping organisations meet all of the features outlined above. Using specific language would be favoured by most customers – telling them how the solution will help them reduce greenhouse gas emissions, meet compliance requirements for CO2 and/or waste reduction, and save money on electricity and/or management costs – these are all likely to get the sale over the line faster than a broad “sustainability” messaging will.
Technology is reshaping the Public Sector worldwide, optimising operations, improving citizen services, and fostering data-driven decision-making. Government agencies are also embracing innovation for effective governance in this digital era.
Public sector organisations worldwide recognise the need for swift and agile interventions. With citizen expectations resembling those of commercial customers, public sector organisations face mounting pressure to break down the barriers to provide seamless service experiences.
Read on to find out how public sector organisations in countries such as Australia, Vietnam, the Philippines, South Korea, and Singapore are innovating to stay ahead of the curve; and what Ecosystm VP Consulting, Peter Carr sees as the Future of Public Sector.
Click here to Download ‘The Future of the Public Sector’ as a PDF