�RISK MANAGEMENT SUMMIT 2022�

SUMMIT THEME:

“THE 4TH INDUSTRIAL REVOLUTION, MANAGING UNCERTAINTIES AND MAKING RISK BASED OPTIMAL DECISION”.

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TODAY’S DISCUSSION

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CHALLENGES AND OPPORTUNITIES

THAT

DISRUPTIVE TECHNOLOGY POSES

FOR

ORGANIZATIONS IN TANZANIA

AND

THE WAY FORWARD

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PRESENTER

DR. M. J. MKANDAWILE

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OUR MAIN FOCUS

• HOW NEW TECHNOLOGIES CHANGE THE WAY PEOPLE LIVE, WORK, COMMUNICATE, DO BUSINESS, SERVICES ARE OFFERED ETC
• CHALLENGES
• OPPORTUNITIES

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SUNFISH ROBOT

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SUNFISH ROBOT

• In 2013, the Government of Japan established the International Research Institute for Nuclear Decommissioning, including the mandate of developing robots.
• One of the biggest challenges had been how to determine what happened to the fuel inside the core of the reactor.
• Various robots had tried to penetrate the core but without success.
• Finally, in 2017, a small robot dubbed Little Sunfish equipped with five propellers, video cameras, an array of sensors and designed to operate underwater under severe radiation exposure succeeded in locating the missing fuel inside the reactor core .

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ORGANIZATION OF THE PRESENTATION

• Background to Disruptive technologies
• Disruptive technologies and their use in disaster risk reduction and management
• Challenges
• Opportunities
• Role of the Government
• Conclusion

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Vihatarishi vya utekelezaji wa Mpango wa Maendeleo wa Mwaka 2022/2023:

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Hotuba ya Waziri wa Fedha, Kuhusu Taarifa ya hali ya Uchumi wa Taifa na Mpango wa Maendeleo ya Taifa 2022/2023:

Vihatarishi vya utekelezaji wa Mpango wa maendeleo wa Mwaka 2022/2023:

“Utekelezaji wa Mpango wa maendeleo Unaweza Kuathiriwa na vihatarishi vya ndani na nje.

Vihatarishi vya ndani ni pamoja na:

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�Vihatarishi vya utekelezaji wa Mpango wa Maendeleo wa Mwaka 2022/2023:�

1. Ufinyu wa rasilimali fedha kuweza kugharamia utekelezaji wa miradi ya maendeleo

(b) Ushiriki mdogo wa sekta binafsi katika mipango wa maendeleo.

(c) Uhalibifu wa mazingira na mabadiliko ya tabia nchi

(d) Uhalifu wa kimtandao

(e) Rushwa

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�Vihatarishi vya utekelezaji wa Mpango wa maendeleo wa Mwaka 2022/2023:�

Vihatarishi vya nje ni pamoja na:

1. Majanga ya asali na magonjwa ya mlipuko
2. Mitikisiko ya kiuchumi duniani
3. Kubadilika kwa teknolojia
4. Kutopatikana kwa misaada na mikopo kwa wakati

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�BACKGROUND TO DISRUPTIVE TECHNOLOGY �

• The term Technological disruption or ‘disruptive technology’ was popularized by Christensen (1997).
• The key element to Christensen’s model is that disruptors ‘sneak’ into an existing market, and compete directly with incumbents once a foothold has been established.
• Regardless, Christensen’s model ignores the reality that the introduction of higher quality products can also be disruptive .

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Example: Apple ‘s iPhone

• Apple’s iPhone was, from its introduction, a superior and more expensive alternative to the smartphones and mobile phones produced by market leaders such as Nokia, Motorola and Blackberry.
• The iPhone, together with smartphones based on the Android operating system, proceeded to disrupt the market.
• Between 2009 and 2014, Apple’s share of the global mobile phone market grew from 2 to 10 per cent, while Nokia’s contracted from 36 to 10 per cent (Statista 2016).
• In a similar vein, ridesharing company Uber offers quality improvements over taxi services (at least in the eyes of some consumers), but at a lower cost.

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DISRUPTIVE INNOVATION

• The Christensen model of disruptive technology, later renamed ‘disruptive innovation’, defines a process through which ‘disruption’ takes place.
• A firm enters a market by providing cheaper (but typically more technologically advanced) products at lower-value to consumers.

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FORMAL DEFINITION

• Disruptive technology or digital disruption is a transformation that is caused by emerging digital technologies and business models.
• These innovative new technologies and models can impact the value of existing products and services offered in the industry.
• The emergence of these new digital products/services/businesses disrupts the current market and causes the need for re-evaluation of the existing ones.
• eg Blockchain, risks? challenges? opportunities?

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DISRUPTIVE TECHNOLOGY

• Disruptive technologies enter an established market but radically change the way business is handled.
• These technologies completely replace their predecessors by offering revolutionary benefits that are notably superior.
• When a disruptive technology enters an existing market, it can make current items or processes obsolete.

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Disruptive technologies and their use in disaster risk reduction and management

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�Disruptive technologies and their use in disaster risk reduction and management�

• Technological advancement and innovation have created new opportunities for enhancing disaster resiliency and risk reduction.
• Developments in disruptive technologies such as artificial intelligence (AI), the Internet of Things (IoT), and Big Data – and innovations in such areas as robotics and drone technology are transforming many fields, including disaster risk reduction and management.
• The rapid spread of supporting digital infrastructure and devices – such as wireless broadband networks, smartphones and cloud computing – has created the foundation for the application of disruptive technologies for disaster management.

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Disruptive technologies in disaster risk reduction and management

• IoT, Big Data and AI are key drivers behind the ongoing digital transformation, and will play an increasingly important role in all phases of disaster management and resiliency development.
• Examples include the use of AI to analyze data to make detection models about earthquakes, and the use of Big Data to identify communication patterns during disasters, through the analysis of social media.

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Disaster management phases�

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���SOME CASE STUDIES: Natural disaster subgroup��

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CASE BY CASE

HOW DISRUPTIVE TECHNOLOGIES HAS BEEN/MAY BE USED IN DISASTER RISK REDUCTION AND MANAGEMENT�

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�Disruptive technologies for 1. Drones (a) Air�

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UNMANNED AIR VEHICLES (UAVS

• Unmanned air vehicles (UAVs) were initially developed for military use. They have since made their way into other uses, such as aerial photography and package delivery.
• UAVs are suitable, since they can fly places manned aircraft cannot. They can also fly at low altitudes, overcoming lack of visibility when there is cloud cover, and thus images from drones have higher resolution than satellite.
• The first documented use of drones was after Hurricane Katrina in the United States of America in 2005 (Meier, 2015). Because roads were blocked by trees, small drones were deployed to search for survivors and assess river levels

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��Examples of UAV use for different disaster phases include:� �

• Preparedness such as filming volcanic activity in order to determine when warnings should be issued (Husain, 2018).
• Response such as delivering equipment to locations where networks have been affected by a disaster:
• Drones are already used to deliver blood in several countries, other medical supplies and equipment needed during a disaster (Smyth, 2017).
• Another example is the use of drones to assist Australian firefighters at night.

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(b) Underwater drone�

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��Underwater��

• Unmanned underwater vehicles (UUV) measure storm intensity and direction.
• One key difference between UUVs and airborne drones is that GPS does not work underwater, so UUVs are tethered, limiting their range (Meier, 2018b).
• underwater drones carry sensors to measure ocean heat, salinity and density (Niiler, 2018).
• They were used during Hurricane Florence in 2018 where Sensors measured the ocean heat fuelling the hurricane, transmitting the data to the weather stations.

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2. 5G and disruptive technologies�

• The 5G wireless technology is viewed as a key enabler of several disruptive technologies applicable for disaster situations.
• IoT sensors generate vast amounts of data that need to be communicated rapidly.
• Drones are more effective if high definition images can be transmitted in real time, instead of having to wait until they return to their base.
• The 5G technology has higher capacity, is faster and has lower latency compared with previous generations, and thus can support disruptive technologies reaching their full functionality.

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�3. Mobile phones� �

• As mobile phones have evolved in functionality, their impact for disaster relief has grown. From voice calls to text messages – and now location -based services, cameras and Internet access – mobile phones have a diverse set of features being leveraged by the public and disaster community in times of crisis.
• The wide spread of mobile phones – often with a higher penetration than television or radio in developing nations – today makes them the most universal communication device in the world.
•  SMS provides information quickly to those touched by disasters. Several studies have examined the impact of text messaging during disasters.

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�Mobile phones�

• The advent of smartphones has created new opportunities for the public (i.e. crowd) to assist – knowingly or unknowingly – in helping to respond to disasters. Four roles have been identified.

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�Mobile phones�

• Crowd as sensors: Mobile phones are continuously generating data from their internal sensors, including GPS. The data are collected (opportunistic crowdsourcing) with little, if any, data processing by the user.
• Crowd as social computers: Users generate data by using apps such as those for social media. These data are collected by platforms (Big Data). Like the crowd as sensor, there is no direct effort to share the data by the user.
• Crowd as reporters: Users offer their own information on events (e.g. taking a photo of damage, tweeting about weather conditions, etc.). This user-generated content can include supplementary information (e.g. hashtags).
• Crowd as microtaskers: Users create content such as adding roads or buildings to satellite images. Here, users are active participants and often have specific skills.

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�4. Big Data�

• Growing digitalization is creating an bulk of data generated by sensors, closed circuit television, mobile phones, financial transactions and Internet activities, to name just a few.
• While the huge amount of data being generated is being mined by businesses for commercial purposes, Big Data analytics also hold enormous potential for disaster management.
• Examples include the analysis of social media communications during a disaster to understand the types of information and content creators, in order to have more impact and reduce false information.
• Another example is the use of financial transactions to monitor economic activity during and after a disaster, in order to improve targeting of support efforts
• Cellphone data have been used to monitor the movement of the population during flooding .
• Big Data analytics are also used for analysing information generated by sensors in IoT implementations as well as data from drones and robots.

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�Big Data�

• Big Data and crisis analytics is a term used to refer to the analysis of large data sets for disasters.
• Due to advances in ICT and processing of large data sets, the ability to respond to disasters is at an inflection point (Qadir et al., 2016).
• Big data tools can today process large amounts of crisis-related data (e.g. user-generated, sensors) to support more effective disaster response.

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5. Internet of Things�

• Developments in cloud computing, broadband wireless networks, the sensors themselves and data analysis have led to the emergence of powerful, integrated and real-time systems referred to as the Internet of Things (IoT).
• Disaster management is an ideal case for IoT applications, since sensors can send alerts about a number of potentially dangerous situations.
• Tree sensors can detect if a fire has broken out by testing temperature, moisture and carbon dioxide levels.
• Ground sensors can detect earth movements that might signal earthquakes.
• River levels can be monitored by sensors for possible flooding

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Example

• After serious landslides in April 2010 that killed more than 50 people in Rio de Janeiro, Brazil, and left thousands homeless, a City Hall Operations Centre was built in collaboration with IBM (Centre for Public Impact, 2016).
• The centre operates non-stop monitoring of various data streams generated in the city, such as security cameras, rain gauges, traffic signal data, the electricity grid, traffic controls, GPS-equipped public transit vehicles and social media feeds.
• IBM weather forecasting software uses the data and can predict emergencies up to two days in advance

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�6. Artificial intelligence� 

• Software algorithms are increasingly generating valuable insights about a variety of phenomena.
• This allows computers to imitate human intelligence, hence the term artificial intelligence (AI).
• Examples of AI are already operational, such as voice and facial recognition, and commercialized by products such as the IBM Watson computer system, which integrates AI into the analysis of Big Data
• Watson has been applied to disaster scenarios by having it analyse weather and census data to help organizations prepare for a crisis and optimally allocate resources (IBM, 2012).

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Computational chemistry

• uses methods and programs to solve specific chemistry problems, saving time and resources.
• Instead of carrying out multiple experiments at the laboratory, appropriate parameters are entered into the computer to predict what formula would be the most optimal

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Artificial intelligence and machine learning in emergency situations

• AI could have tremendous impact for disaster management, from potentially predicting earthquakes to quickening recovery and response times.
• Artificial intelligence (AI) and machine learning have advanced to the state where they are highly proficient in making predictions and in identification and classification.

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�AI during emergency situations�

Processing information: AI is used for image recognition of satellite photos to identify damaged buildings, flooding, impassable roads, etc. Multiple data streams can be combined with unreliable data removed and heat maps generated.

Following the Nepal earthquakes in 2015, humanitarian and relief groups used pre- and post-disaster imagery and utilized crowdsourced data analysis and machine learning to identify locations affected by the quakes that had not yet been assessed or received aid.

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AI during emergency situations

• Social media analysis: Real-time information from Facebook, Twitter, Instagram and YouTube can be analysed and validated by AI to filter and classify information and make predictive analysis.
• Artificial Intelligence for Disaster Response (AIDR) was created to process the large number of tweets generated during a crisis.
• AIDR uses machine learning to automatically process tweets in real time. The software collects tweets based on hashtags and keywords, and then uses AI to further classify them by topic.
• The open software is free for those who work in crisis response

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AI during emergency situations

• Predictive analytics: AI is being used to analyse past data to predict what is likely to happen in the event of a disaster.
• Optima Predict software processes information from emergency response systems to optimize ambulance routes (Young, 2017).
• The data can be integrated with online dashboards so that emergency personnel can respond in real time.

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�7. Robots� �

• Although industrial robots have been around for some time, robots have become more sophisticated through integration with microprocessors and sensors.
• The growing dexterity of robots makes them suitable in disaster situations that are too dangerous for humans or rescue animals.
• Search-and-rescue robots were reportedly first used following the September 2011 terrorist attack in New York City to assess the wreckage of the demolished World Trade Center (Feuilherade, 2017).

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�8. Blockchain�

• One challenge during a rapidly evolving disaster is coordinating and verifying information among different stakeholders.
• For example, the United Nations found that, in the wake of the 2010 Haitian earthquake, assistance efforts were hampered by too many data sources among the some 20 relief groups (Rohr, 2017).
• The Blockchain distributed ledger system and chain of verified records could play a significant role in ameliorating information control.

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Blockchain�

• Another way blockchain technology is already indirectly used for disaster relief is for fundraising activities that accept cryptocurrencies (Harmes, 2018).
• Several organizations – including Direct Relief, Humanity Road and Save the Children – currently accept cryptocurrencies such as Bitcoin in their fundraising activities.

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CHALLENGES

• Disruptive technologies have the potential to significantly transform disaster preparation, response, recovery and mitigation.
• Some technologies, such as drones and IoT, are increasingly utilized in disaster situations, while others are still being piloted.
• Wider application depends both on overcoming a number of challenges and the relevance of sustainability in different contexts.
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Challenges

• The use of disruptive technologies in disaster settings faces a number of challenges limiting their impact:

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Skills

• While some of the technologies require few skills and their use can be learned quickly, a high level of competence is required to successfully deploy others.
• Drones and robots require skilled human technicians to deploy, operate and maintain.
• Big Data analytics require advanced software, powerful computers and data science expertise, and involve significant testing and modeling, and investment in research.
• Many of these skill sets and supporting resources are in short supply in developing countries including Tanzania.

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Data deluge

• Growing access to ICTs and the increasing application of sensors are generating massive volumes of data.
• Such Big Data has immense relevance for disaster management.
• However, the growing amount of data poses challenges for data management, analysis and verification.
• As one expert puts it: “The overflow of information generated during disasters can be as paralyzing to humanitarian response as the lack of information. This flash flood of information is often referred to as Big Data, or Big Crisis Data. Making sense of Big Crisis Data is proving to be an impossible challenge for traditional humanitarian organizations” (www​.digital​ -humanitarians​.com)

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False information

• While real-time information dissemination can save lives, the rapidity by which the data flow makes it difficult to verify, and the consequences of false information can be deadly.
• There are a number of levels on which information can be false, with negative consequences for disaster preparedness and response.
• First, the public tends to exaggerate under extreme stress (Qadir et al., 2016). A study on social media use relating to the Boston Marathon bombing in 2013 analyzed 8 million unique tweets, finding that only 20 per cent relayed accurate information; the remaining tweets either consisted of fake content or rumours (29 per cent) or general comments and opinions (51 per cent) (Meier, 2013).
• A second way that information is false is through bias, particularly with exclusive reliance on Big Data. The Google Flu Tracker overestimated the size of the 2013 influenza pandemic by half, forecasting twice the amount of flu-related doctor visits (Butler, 2013).
• Some of the factors distorting Big Data include intentional or unintentional false crowdsourced data and how well they represent the actual population.

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Legal ramifications

Disruptive technologies pose a number of regulatory and legal challenges.

• Drones often need to be registered and abide by civil air regulations, particularly in crowded urban areas.
• Some jurisdictions ban the use of drones, due to security concerns.
• Some relief organizations have developed a code of conduct related to legal and other issues concerning drone use in humanitarian work.
• The increasing use of Big Data for crisis analysis poses challenges for data protection and privacy, and even more so when the data are shared cross-border, raising issues for international research collaboration.

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Costs

Investment for implementing digital solutions for disaster management can be high.

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While costs of hardware such as UAVs and sensors are continuously declining, the cost of operating, integrating and analysing the information they generate are high.

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Similarly, while Big Data itself costs little to generate, its analysis requires specialized software and hardware and data science expertise.

Few disruptive digital solutions have achieved the scale necessary to achieve a dramatic reduction in costs.

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Ownership

This revolves around several areas.

• One is the ability for governments and other organizations involved in disaster relief to own relevant disruptive technology equipment
• As noted, though dropping in costs, the price of equipment such as drones remains costly for many developing countries. While drones are often deployed by experienced teams, the delay between them arriving on site and a country already having the equipment could be significant.

Another aspect relates to the data ownership.

• Data are generated by different disruptive technologies. Policies need to be in place regarding ownership of data generated during disasters, including appropriate data protection and privacy regulations.
• A third aspect of ownership relates to governments directing their own strategy for use of disruptive technology for disaster management or relying on others to do so.
• While a lack of resources may stipulate the latter option, in the long run, sustainability dictates that governments themselves are best placed to know how best to utilize disruptive technologies under different contextual environments.

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Readiness

• Countries vary in their capabilities to properly absorb digital technologies for disaster situations.
• Within countries, there are differences in access to ICTs as well as applications. This may make it problematic for some disruptive technologies to achieve wide impact.
• For example, an analysis of citizens impacted by 2014 floods in Malaysia found that mobile phones and SMS were used most often during the flood (Aisha et al., 2015).
• Facebook was the most popularly used social media application, compared to Instagram or Twitter.
• WhatsApp was the mobile messaging application used most often. There was also a significant age difference: younger users were more likely to use social media, and there was a notable inverse relationship between age and the use of social media.
• No analysis was done of differences in use by gender, although it is notable that the majority of surveys used for the study were completed by female flood victims (63 per cent of respondents).
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OPPORTUNITIES

• Firstly, technology is a key driver of within-sector productivity growth at both a firm and individual level.
• Adopting new technologies is essential in transforming sectors to higher productivity models: whether it be the use of fertilisers, irrigation or machinery in agriculture; in machinery or machine tools in manufacturing; or in information technologies in offices and businesses.
• In addition, rising productivity lowers production costs and stimulates demand, in some cases leading to job creation; alternatively the technology itself may create new job categories to manage its utilization.

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Creation of new markets

• Technological disruption usually leads to creation of new markets.
• The ideas stream into creation of new products and or new services which disrupts the market share of established products or services and thus leads to creation of new markets.
• The evolution of mobile phones, Uber, Bolt

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Value addition

• Value addition is generally in the form of an extra feature that has been added to the product or the service before it is offered to the final customer for consumption of usage.
• Disruptive innovations are associated with value addition as they are successful in either reducing the manufacturing cost or come up with extra features or satisfying any other lateral wants of the customer as in the aces of hybrid cars which can be electrically driven or by fuel.

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Survey of selected industries

Mining

• Tanzania has a significant mining sector with great future potential, which is only increased by the availability of disruptive technologies for extraction and exploration.
• Sectoral data from the Bank of Tanzania show that between 2009 and 2016, mining and quarrying experienced an average annual growth of 7.72 per cent, slightly above the average GDP growth of 6.36 per cent over the period.
• In 2009, the sector had a share of 3.32 per cent of Tanzanian output; by 2016, its output share was 3.51 per cent.
• The sector makes the largest contribution to exports, and it makes major contributions to public-sector revenue through tax and non-tax revenues

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Mining�

• Tanzania’s industrial strategy (URT 2016) places strong emphasis on manufacturing, including those manufacturing industries that add value to Tanzania’s agricultural products and mined raw materials.
• In fact, in 2016,mining accounted for US$3.5 billion in exports, about 54 per cent of merchandise exports (MIT 2018).
• This dwarfs manufacturing exports, and makes mining a very strategic part of the economy.
• Mining is a very capital intensive, and high-tech industry

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�Mining�

• However, if properly managed, mining can yield very significant public revenues, which can in turn stimulate demand and further growth.
• In some cases, significant employment and technical spillovers occur.
• For a long time, the application of engineering and new technology have offered mining the opportunity to realise massive productivity gains (Maloney and Lederman 2012).
• Disruptive technologies extend that potential further.

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Mining�

• Opportunities for gains from disruptive technology relate to a combination of automated data generation and high-intensity data processing to generate quality, yield and efficiency gains.
• This ranges from altering extraction technologies to better management of plants to avoid downtime.
• The sector’s potential extends to mining of other substance - other metals, precious stones, graphite, helium and uranium, for example - and to incorporating some of the most high-tech

What are the risks involved?

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Gas

• Offshore natural gas fields in Southern Tanzania offer the potential to generate more exports than gold by using very expensive gas liquification plants.
• Gains from disruptive technology are also possible in terms of exploration.
• Exploration in this area has been conducted using remote-sensing, satellite technology, which was found to be about 30 times faster than the corresponding traditional exploration.

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Agriculture

• There are - and are going to be - opportunities to use exciting, disruptive technologies for high-value farming.
• These include collecting and using high-resolution data for fine-tuned responses, such as reducing wasted inputs, optimizing irrigation, increasing yields, and perfecting quality control.
• In Tanzania, where capital inputs are currently hard to justify, these new technologies present an opportunity to raise production and profits much more than is possible with current technology
• New technologies might help Tanzania compete in producing much higher-value export crops such as expensive coffee, horticultural products and flowers.

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What role does government play?

Governments have three main policy tools — they regulate, tax and spend.

Their formulation and implementation of these policy levers shapes how, and whether, markets develop, adopt and diffuse new technologies.

Governments play a major role in setting the frameworks within which markets operate, through broad regulation such as competition policy and consumer law, and through specific legislation that governs the conduct of particular activities, firms, industries or workers

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�These roles are as: �

• a regulator of the frameworks in which firms and markets operate, pertaining to issues such as market power and information provision to consumers
• an enabler of new technology development and adoption — establishing public infrastructure, setting standards to ensure interoperability between technologies, and investing in education and training to ensure the workforce is appropriately skilled
• a mitigator of risks — smoothing the structural adjustment process for workers and firms by ensuring the social safety net evolves with changing work practices, and safeguarding individuals’ privacy and security

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�Key points �

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Governments influence the development and pace of adoption of new technologies.

• Risk-based approaches are needed to ensure regulatory frameworks do not act as barriers while maintaining regulatory objectives. Similarly, a light touch to standards is required, with attention on improving interoperability of technologies.
• Governments can encourage innovation by: being adopters of innovative technology; delivering a business environment that is consistent, understood and not unnecessarily regulated; and removing regulatory and network impediments to innovation. Data — held in both the public and private sector — is a resource for future innovation, and governments can improve its accessibility.

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Key points

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Governments have a role in mitigating adverse economic impacts and risks to individuals, consumers, organizations and the environment that may arise from disruptive technology.

• Digital platforms can provide consumers with more information. This reduces the need for some regulation to protect consumers but can require frameworks to: ensure information integrity; uphold quality and safety standards; and address negative impacts on the broader community.
• New technologies can raise social concerns, such as with risk-based pricing of insurance and the ethics of some robot uses.
• Governments should ensure that restrictions on the use of new technologies are essential to safety or a similar public interest objective and actually reflect the risks involved.
• With more pervasive collection of data, different measures may be needed to ensure adequate privacy protections.

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Key points

• New technologies can make regulation and engagement with regulators less burdensome, more efficient, and improve compliance monitoring.
• Automation and integration of human service delivery could reduce costs, allow better targeting of services, and facilitate greater consumer choice.
• Use of remote monitoring, such as through remotely piloted aircraft and embedded sensors, could improve the provision of government services, including better planning, management and funding of public infrastructure.

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Key points

Changes are needed to enable digital technologies within government and our organizations, including through:

• procurement policies; development of skills, culture and coordination across agencies; and a shift to more open policy development processes.
• Governments and organizations need to use digital technologies to improve transparency and enhance confidence in policies and processes.

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Conclusions

• Disruptive technologies are affecting disaster management, although the pace, scope and impact vary among the technologies.
• While social media platforms such as Facebook and Twitter have been applied in a number of emergency events, Big Data, robots and AI remain largely experimental.
• Despite this, drones and IoT are increasing in application, as experience is gained and costs fall.
• Older technologies such as satellite imagery and seismometers are still the most important methods for detecting, monitoring and accessing disasters, and text messaging has the widest reach for communicating with the public.

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Conclusions

• While there is evidence that AI can accurately predict some types of disasters before they happen, the application of disruptive technologies today has a more incremental effect.
• These technologies are refining processes by spreading critical information more quickly, improving understanding of the causes of disasters, enhancing early warning systems, assessing damage quickly, and adding to the knowledge base of the social behaviours and economic impacts after a crisis strikes.
• Disruptive technologies are improving situational awareness by providing the crisis community with a clearer understanding of the extent of damage and where to prioritize resources.

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