AI has already had a significant impact on the tech industry, rapidly evolving software development, data analysis, and automation. However, its potential extends into all industries – from the precision of agriculture to the intricacies of life sciences research, and the enhanced customer experiences across multiple sectors.
While we have seen the widespread adoption of AI-powered productivity tools, 2025 promises a bigger transformation. Organisations across industries will shift focus from mere innovation to quantifiable value. In sectors where AI has already shown early success, businesses will aim to scale these applications to directly impact their revenue and profitability. In others, it will accelerate research, leading to groundbreaking discoveries and innovations in the years to come. Regardless of the specific industry, one thing is certain: AI will be a driving force, reshaping business models and competitive landscapes.
Ecosystm analysts Alan Hesketh, Clay Miller, Peter Carr, Sash Mukherjee, and Steve Shipley present the top trends shaping key industries in 2025.
Click here to download ‘AI’s Impact on Industry in 2025’ as a PDF
1. GenAI Virtual Agents Will Reshape Public Sector Efficiency
Operating within highly structured, compliance-driven environments, public sector organisations are well-positioned to benefit from GenAI Agents.
These agents excel when powered LLMs tailored to sector-specific needs, informed by documented legislation, regulations, and policies. The result will be significant improvements in how governments manage rising service demands and enhance citizen interactions. From automating routine enquiries to supporting complex administrative processes, GenAI Virtual Agents will enable public sector to streamline operations without compromising compliance. Crucially, these innovations will also address jurisdictional labour and regulatory requirements, ensuring ethical and legal adherence. As GenAI technology matures, it will reshape public service delivery by combining scalability, precision, and responsiveness.
2. Healthcare Will Lead in Innovation; Lag in Adoption
In 2025, healthcare will undergo transformative innovations driven by advancements in AI, remote medicine, and biotechnology. Innovations will include personalised healthcare driven by real-time data for tailored wellness plans and preventive care, predictive AI tackling global challenges like aging populations and pandemics, virtual healthcare tools like VR therapy and chatbots enhancing accessibility, and breakthroughs in nanomedicine, digital therapeutics, and next-generation genomic sequencing.
Startups and innovators will often lead the way, driven by a desire to make an impact.
However, governments will lack the will to embrace these technologies. After significant spending on crisis management, healthcare ministries will likely hesitate to commit to fresh large-scale investments.
3. Agentic AI Will Move from Bank Credit Recommendation to Approval
Through 2024, we have seen a significant upturn in Agentic AI making credit approval recommendations, providing human credit managers with the ability to approve more loans more quickly. Yet, it was still the mantra that ‘AI recommends—humans approve.’ That will change in 2025.
AI will ‘approve’ much more and much larger credit requests.
The impact will be multi-faceted: banks will greatly enhance client access to credit, offering 24/7 availability and reducing the credit approval and origination cycle to mere seconds. This will drive increased consumer lending for high-value purchases, such as major appliances, electronics, and household goods.
4. AI-Powered Demand Forecasting Will Transform Retail
There will be a significant shift away from math-based tools to predictive AI using an organisation’s own data. This technology will empower businesses to analyse massive datasets, including sales history, market trends, and social media, to generate highly accurate demand predictions. Adding external influencing factors such as weather and events will be simplified.
The forecasts will enable companies to optimise inventory levels, minimise stockouts and overstock situations, reduce waste, and increase profitability. Early adopters are already leveraging AI to anticipate fashion trends and adjust production accordingly.
No more worrying about capturing “Demand Influencing Factors” – it will all be derived from the organisation’s data.
5. AI-Powered Custom-Tailored Insurance Will Be the New Norm
Insurers will harness real-time customer data, including behavioural patterns, lifestyle choices, and life stage indicators, to create dynamic policies that adapt to individual needs. Machine learning will process vast datasets to refine risk predictions and deliver highly personalised coverage. This will produce insurance products with unparalleled relevance and flexibility, closely aligning with each policyholder’s changing circumstances. Consumers will enjoy transparent pricing and tailored options that reflect their unique risk profiles, often resulting in cost savings. At the same time, insurers will benefit from enhanced risk assessment, reduced fraud, and increased customer satisfaction and loyalty.
This evolution will redefine the customer-insurer relationship, making insurance a more dynamic and responsive service that adjusts to life’s changes in real-time.
Over the past year, Ecosystm has conducted extensive research, including surveys and in-depth conversations with industry leaders, to uncover the most pressing topics and trends. And unsurprisingly, AI emerged as the dominant theme. Here are some insights from our research on the Retail industry.
Click here to download ‘AI in Retail: Success Stories & Insights’ as a PDF
From personalised product recommendations to predictive analytics, AI is helping retailers deliver exceptional customer experiences and optimise their operations. However, many retailers are still grappling with the complexities of AI implementation. Those who can successfully navigate this challenge and harness the power of AI will emerge as industry leaders, driving innovation and shaping the future of retail.
Despite the challenges, Retail organisations are witnessing early AI success in these 3 areas:
- 1. Customer Experience & Engagement
- 2. Supply Chain Optimisation
- 3. Fraud & Risk Analysis
Customer Experience & Engagement
- Conversational AI. Providing real-time customer support and answering queries
- Personalisation. Offering tailored product suggestions based on customer preferences and behaviour
- Virtual Try-On. Allowing customers to visualise products in different settings using AR
“AI has helped us to refine our customer chatbots to allow for more self-service. We’ve experienced faster customer order processing and quicker resolution of issues, putting control directly in the hands of our customers.” – CX LEADER
Supply Chain Optimisation
- Inventory Management. Automating inventory management processes to ensure optimal stock levels
- Supply Chain Visibility. Monitoring and optimising supply chain operations, including logistics and distribution
- Demand Forecasting. Predicting sales and demand trends to optimise inventory and production planning
“We use AI to optimise the supply chain, saving operational costs. Digital supply chains and cloud-based tracking systems streamline operations and enhance efficiency.” – CFO
Fraud & Risk Analysis
- Fraud Detection. Identify and prevent fraudulent activities, such as online fraud and chargebacks
- Risk Assessment. Assessing risk factors associated with customer transactions and preventing losses
- Customer & Market Insights. Understanding customer behaviour, market trends, and growth opportunities
“With eCommerce as a key market force, understanding customer habits is crucial to ensuring we have the right products in stock and optimising our pricing strategy.” – COO
Over the past year, Ecosystm has conducted extensive research, including surveys and in-depth conversations with industry leaders, to uncover the most pressing topics and trends. And unsurprisingly, AI emerged as the dominant theme. Here are some insights from our research on the Manufacturing industry.
Click here to download “AI in Manufacturing: Success Stories & Insights” as a PDF
AI is revolutionising production lines, supply chains, and product development in the manufacturing sector. Yet, many manufacturers find themselves stuck between ambition and execution. Those who bridge this gap will gain a competitive edge, driving innovation and leading the industry forward.
Despite the challenges, Manufacturing organisations are witnessing early AI success in these 3 areas:
- 1. Quality Control & Assurance
- 2. Supply Chain Management & Optimisation
- 3. Process Automation & Efficiency
Quality Control & Assurance
- Defect Detection. Identifying defects in products and improving quality
- Product Inspection. Implementing AI-powered vision systems to inspect products and ensure they meet quality standards
- Data Analysis. Analysing operational data and customer feedback to identify operations and product issues
“AI is the future of design. It streamlines the design process, leading to faster time-to-market and superior products.” – OPERATIONS LEADER
Supply Chain Management & Optimisation
- Inventory Management. Optimising inventory levels and reducing costs
- Supply Chain Visibility. Gaining real-time visibility into supply chain operations
- Demand Forecasting. Predicting demand for products to improve production planning and inventory management
“By leveraging AI, we’re not just optimising our supply chain; we’re pioneering sustainable practices to reduce our carbon footprint.” – CIO
Process Automation and Efficiency
- Process Optimisation. Identifying areas for improvement and potential operational bottlenecks
- Predictive Maintenance. Predicting equipment failures and preventing downtime
- Customer Feedback Analysis. Analysing customer feedback to improve design processes, products, and services
“Our goal is to build intelligent manufacturing plants. By proactively monitoring equipment health, we minimise downtime and maximise productivity – we have set a new internal standard for operational efficiency in the last two years.” – HEAD OF PRODUCTION
Governments worldwide struggle with intricate social, economic, and environmental challenges. Tight budgets often leave them with limited resources to address these issues head-on. However, innovation offers a powerful path forward.
By embracing new technologies, adapting to cultural shifts, and fostering new skills, structures, and communication methods, governments can find solutions within existing constraints.
Find out how public sector innovation is optimising internal operations, improving service accessibility, bridging the financial gap, transforming healthcare, and building a sustainable future.
Click here to download ‘Innovation in Government: Social, Economic, and Environmental Wins’ as a PDF
Optimising Operations: Tech-Driven Efficiency
Technology is transforming how governments operate, boosting efficiency and allowing employees to focus on core functions.
Here are some real-world examples.
Singapore Streamlines Public Buses. A cloud-based fleet management system by the Land Transport Authority (LTA) improves efficiency, real-time tracking, data analysis, and the transition to electric buses.
Dubai Optimises Utilities Through AI. The Dubai Electricity and Water Authority (DEWA) leverages AI for predictive maintenance, demand forecasting, and grid management. This enhances service reliability, operational efficiency, and resource allocation for power and water utilities.
Automation Boosts Hospital Efficiency. Singapore hospitals are using automation to save man-hours and boost efficiency. Tan Tock Seng Hospital automates bacteria sample processing, increasing productivity without extra staff, while Singapore General Hospital tracks surgical instruments digitally, saving thousands of man-hours.
Tech for Citizens
Digital tools and emerging technologies hold immense potential to improve service accessibility and delivery for citizens. Here’s how governments are leveraging tech to benefit their communities.
Faster Cross-Border Travel. Malaysia’s pilot QR code clearance system expedites travel for factory workers commuting to Singapore, reducing congestion at checkpoints.
Metaverse City Planning. South Korea’s “Metaverse 120 Center” allows residents to interact with virtual officials and access services in a digital environment, fostering innovative urban planning and infrastructure management.
Streamlined Benefits. UK’s HM Revenue and Customs (HMRC) launched an online child benefit claim system that reduces processing time from weeks to days, showcasing the efficiency gains possible through digital government services.
Bridging the Financial Gap
Nearly 1.7 billion adults or one-third globally, remain unbanked.
However, innovative programs are bridging this gap and promoting financial inclusion.
Thailand’s Digital Wallet. Aimed at stimulating the economy and empowering underserved citizens, Thailand disburses USD 275 via digital wallets to 50 million low-income adults, fostering financial participation.
Ghana’s Digital Success Story. The first African nation to achieve 100% financial inclusion through modernised platforms like Ghana.gov and GhanaPay, which facilitate payments and fee collection through various digital channels.
Philippines Embraces QR Payments. The City of Alaminos leverages the Paleng-QR Ph Plus program to promote QR code-based payments, aligning with the central bank’s goal of onboarding 70% of Filipinos into the formal financial system by 2024.
Building a Sustainable Future
Governments around the world are increasingly turning to technology to address environmental challenges and preserve natural capital.
Here are some inspiring examples.
World’s Largest Carbon Capture Plant. Singapore and UCLA joined forces to build Equatic-1, a groundbreaking facility that removes CO2 from the ocean and creates carbon-negative hydrogen.
Tech-Enhanced Disaster Preparedness. The UK’s Lincolnshire County Council uses cutting-edge geospatial technology like drones and digital twins. This empowers the Lincolnshire Resilience Forum with real-time data and insights to effectively manage risks like floods and power outages across their vast region.
Smart Cities for Sustainability. Bologna, Italy leverages the digital twins of its city to optimise urban mobility and combat climate change. By analysing sensor data and incorporating social factors, the city is strategically developing infrastructure for cyclists and trams.
Tech for a Healthier Tomorrow
Technology is transforming healthcare delivery, promoting improved health and fitness monitoring.
Here’s a glimpse into how innovation is impacting patient care worldwide.
Robotic Companions for Seniors. South Korea tackles elder care challenges with robots. Companion robots and safety devices provide companionship and support for seniors living alone.
VR Therapy for Mental Wellness. The UAE’s Emirates Health Services Corporation implements a Virtual Reality Lab for Mental Health, that creates interactive therapy sessions for individuals with various psychological challenges. VR allows for personalised treatment plans based on data collected during sessions.
The AI landscape is undergoing a significant transformation, moving from traditional predictive AI use cases towards Generative AI (GenAI). Currently, most GenAI use cases promise an improvement in employee productivity, without focusing on how to leverage this into new or additional revenue generating streams. This raises concerns about the long-term return on investment (ROI) if this is not adequately addressed.
The Rise of Generative AI Over Predictive AI
Traditionally, predictive AI has been integral to business strategies, leveraging data to forecast future outcomes with remarkable accuracy. Industries across the board have used predictive models for a range of applications, from demand forecasting in retail to fraud detection in finance. However, the tide is changing with the emergence of GenAI technologies. GenAI, capable of creating content, designing products, and even coding, holds the promise to revolutionise how businesses operate, innovate, and compete.
The appeal of GenAI lies in its versatility and creativity, offering solutions that go beyond the capabilities of predictive models. For example, in the area of content creation, GenAI can produce written content, images, and videos at scale, potentially transforming marketing, entertainment, and education sectors. However, the current enthusiasm for GenAI’s productivity enhancements overshadows a critical aspect of technology adoption: monetisation.
The Productivity Paradox
While the emphasis on productivity improvements through GenAI applications is undoubtedly beneficial, there is a notable gap in exploring use cases that directly contribute to creating new revenue streams. This productivity paradox – prioritising operational efficiency and cost reduction – may not guarantee the sustained growth and ROI necessary from AI investments.
True innovation in AI should not only aim at making existing processes more efficient but also at uncovering opportunities for monetisation. This involves leveraging GenAI to develop new products, services, or business models to access untapped markets or enhance customer value in ways that directly impact the bottom line.
The Imperative for Strategic Reorientation
Ignoring the monetisation aspect of GenAI applications poses a significant risk to the anticipated ROI from AI investments. As businesses allocate resources to AI adoption and integration, it’s also important to consider how these technologies can generate revenue, not just save costs. Without a clear path to monetisation, the investments in AI, particularly in the cutting-edge domain of GenAI, may not prove viable in the next financial year and beyond.
To mitigate this risk, companies need to adopt a dual approach. First, they must continue to explore and exploit the productivity gains offered by GenAI, which are crucial for maintaining a competitive edge and achieving operational excellence. At the same time, businesses must strategically explore and invest in GenAI-driven opportunities for monetisation. This could mean innovating in product design, personalised customer experiences, or entirely new business models that were previously unfeasible.
Conclusion
The excitement around GenAI’s potential to transform industries is well-founded, but it must be tempered with strategic planning to ensure long-term viability and ROI. Businesses that recognise and act on the opportunity to not only improve productivity but also to monetise GenAI innovations will lead the next wave of growth in their respective sectors. The challenge lies in balancing the drive for efficiency with the pursuit of new revenue streams, ensuring that investments in AI deliver sustainable returns. As the AI landscape evolves, the ability to innovate in monetisation as much as in technology will distinguish the leaders from the followers.
It is true that the Retail industry is being forced to evolve the experiences they deliver to their customers. However, if Retail organisations are only focused on creating digital experiences, they are not creating the differentiation that will be required to leap ahead of the competition.
It is time for Retail organisations to leverage data to empower multiple roles across the organisation to prepare for the different ways customers want to engage with their brands.
So what are the phases of customer engagement? How are companies such as Singapore Airlines and TikTok preparing for the future of Retail?
