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Beyond badges: the case for gamification

Over the past several years, gamification, the concept of applying game mechanics and game design techniques to engage and motivate people, has found its way into the corporate lexicon, with companies scrambling to ‘gamify’ elements of their business, writes JASON RIED, MD of Fuzzy Logic.

As with many new technology platforms and tools, however, the rush to adopt gamification led to many poor use cases and a misinterpretation of what the tool can really offer. Clearly, this hasn’t hindered investment – according to M2Research, ‘the size of the gamification market, currently estimated at around $100million, will grow to more than $2.8billion by 2016.”

To date, the majority of companies have viewed gamification as a way to retain staff and hopefully motivate teams and departments – by simply bolting on a gaming element to existing systems and processes. Yet true gamification extends far beyond simply rewarding a user with a virtual badge or points – and then pitting users against each other in a race to accumulate these online rewards. Sometimes, the word ‘game’ also deters companies from applying the concept in more impactful ways.

Indeed, to leverage and explore the full potential of this tool, companies and developers need to work together to add meaningful layers to the gamified experience – which not only enhance the experience, but also result in tangible (and measurable) changes in behaviour. Essentially, this is the great promise of gamification: influence and ultimately modify human behaviour to drive favourable business outcomes. These outcomes can include more successful loyalty programmes, higher engagement with internal communications and e-learning tools, or wider adoption of internal systems and processes.

Gamification is certainly a way to not only engage employees, but consumers/clients as well. Indeed, as some prominent insurers have already proven, gamification can be used to influence consumer behaviour for better social – and business – outcomes.

Ultimately, the use cases are infinite – but the fundamental approach has to be sound.

Identifying the Core Loop 

We approach the development of gamification tools – and indeed, games and apps – using the same underlying principle as a Skinner box. Also known as an operant conditioning chamber, it is an enclosed apparatus that contains a bar or key that an animal can press or manipulate in order to obtain food or water as a type of reinforcement. This concept has enabled researchers to find out which schedule of reinforcement will lead to the highest response rates.

In the gaming world, we explore which levers or elements within the game design or app can potentially influence behaviour – so, it’s a process of discovering the Skinner box within the virtual universe or app we have created. Once these levers have been identified, you can then start modifying and adding layers to guide users in the discovery of ‘positive’ and ‘negative’ outcomes.

A critical part of this process lies in identifying the ‘core loop’, to borrow another term from the gaming sphere. The core loop is the single most important element of a video game – it’s how players will describe the game to their peers. As developers, we understand that making this core loop easy to comprehend and repeat goes a long way towards engaging and retaining players and users. So when developing a gamified tool or app, the key is to link this core loop with the key behaviours or outcomes you are seeking. This inevitably requires a deep understanding of the psychological drivers behind behavioural patterns. As developers, we integrate this type of understanding and insight into what we do – making it integral to our offering.

Planned Unpredictability

An interesting insight that we have gained is that planned unpredictability can increase users’ engagement. Again, game loops are the key tool here – in that the first loop is the basic task/reward, the next loop is what you do with that in the medium term, and then the next loop is what you do with that in the longer term. Each loop needs to ‘surprise’ the user in that they are excited to see something new open up – either as a task or a reward. This in turn creates further engagement in the first loop as now there is a bigger picture to the task. Revealing the much bigger loop then surprises people again, giving them an incentive to perform the medium loop, which in turn drives the first loop.

This ‘holistic’ view can really drive and impact behaviour, and while it can appear random, truly well designed systems are anything but random. For example, we like to add unexpected elements into the game/system which can then obscure the task loops by introducing surprising elements that even disappear at times. These layers are what make games addictive, as you’re always finding new things (people are explorers at heart!).

For example, a major financial institution was looking to develop a tool that blended both gamification and augmented reality in order to improve the on-boarding/training process with new employees. We developed an app in which new staff members start with an image, and this becomes a seedling plant. The adjudicator from Training Room Online asks questions and awards points throughout the training period and these points are used to grow a virtual tree. As the days – and training – progress, the seasons will move from autumn, summer, winter and spring, each with corresponding visual designs, allowing the players to see their progression in relation to other trainees. Teams doing well might have a luscious tree going into winter, while others might have a sapling and have a lot to catch up on!

A Constant Feedback Loop

In the spirit of much of today’s software development, we adopt the agile approach to developing gamification solutions, and constantly user test to make incremental adjustments This approach applies to any development project – not just gamified solutions, particularly as data becomes more readily available. The data can lead to critical insights into both employees and customers. As a result, we view these projects as an ongoing feedback loop, using data to guide our decisions and ultimately add value to the business or platform in question. So while virtual badges and titles remain useful and compelling tools, there is undoubtedly a far more layered and nuanced approach behind the most successful gamification strategies.

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Now IBM’s Watson joins IoT revolution in agriculture

Global expansion of the Watson Decision Platform taps into AI, weather and IoT data to boost production

IBM has announced the global expansion of Watson Decision Platform for Agriculture, with AI technology tailored for new crops and specific regions to help feed a growing population. For the first time, IBM is providing a global agriculture solution that combines predictive technology with data from The Weather Company, an IBM Business, and IoT data to help give farmers around the world greater insights about planning, ploughing, planting, spraying and harvesting.

By 2050, the world will need to feed two billion more people without an increase in arable land [1]. IBM is combining power weather data – including historical, current and forecast data and weather prediction models from The Weather Company – with crop models to help improve yield forecast accuracy, generate value, and increase both farm production and profitability.

Roric Paulman, owner/operator of Paulman Farms in Southwest Nebraska, said: “As a farmer, the wild card is always weather. IBM overlays weather details with my own data and historical information to help me apply, verify, and make decisions. For example, our farm is in a highly restricted water basin, so the ability to better anticipate rain not only saves me money but also helps me save precious natural resources.”

New crop models include corn, wheat, soy, cotton, sorghum, barley, sugar cane and potato, with more coming soon. These models will now be available in the Africa, U.S. Canada, Mexico, and Brazil, as well as new markets across Europe and Australia.

Kristen Lauria, general manager of Watson Media and Weather Solutions at IBM, said: “These days farmers don’t just farm food, they also cultivate data – from drones flying over fields to smart irrigation systems, and IoT sensors affixed to combines, seeders, sprayers and other equipment. Most of the time, this data is left on the vine — never analysed or used to derive insights. Watson Decision Platform for Agriculture aims to change that by offering tools and solutions to help growers make more informed decisions about their crops.” 

The average farm generates an estimated 500,000 data points per day, which will grow to 4 million data points by 2036 [2]. Applying AI and analysis to aggregated field, machine and environmental data can help improve shared insights between growers and enterprises across the agriculture ecosystem. With a better view of the fields, growers can see what’s working on certain farms and share best practices with other farmers. The platform assesses data in an electronic field record to identify and communicate crop management patterns and insights. Enterprise businesses such as food companies, grain processors, or produce distributors can then work with farmers to leverage those insights. It helps track crop yield as well as the environmental, weather and plant biologic conditions that go into a good or bad yield, such as irrigation management, pest and disease risk analysis and cohort analysis for comparing similar subsets of fields.

The result isn’t just more productive farmers. Watson Decision Platform for Agriculture could help a livestock company eliminate a certain mold or fungus from feed supply grains or help identify the best crop irrigation practices for farmers to use in drought-stricken areas like California. It could help deliver the perfect French fry for a fast food chain that needs longer – not fatter – potatoes from its network of growers. Or it could help a beer distributor produce a more affordable premium beer by growing higher quality barley that meets the standard required to become malting barley.

Watson Decision Platform for Agriculture is built on IBM PAIRS Geoscope from IBM Research, which quickly processes massive, complex geospatial and time-based datasets collected by satellites, drones, aerial flights, millions of IoT sensors and weather models. It crunches large, complex data and creates insights quickly and easily so farmers and food companies can focus on growing crops for global communities.

IBM and The Weather Company help the agriculture industry find value in weather insights. IBM Research collaborates with start up Hello Tractor to integrate The Weather Company data, remote sensing data (e.g., satellite), and IoT data from tractors. IBM also works with crop nutrition leader Yara to include hyperlocal weather forecasts in its digital platform for real-time recommendations, tailored to specific fields or crops. IBM acquired The Weather Company in 2016 and has since been helping clients better understand and mitigate the cost of weather on their businesses. The global expansion of Watson Decision Platform for Agriculture is the latest innovation in IBM’s efforts to make weather a more predictable business consideration. Also just announced, Weather Signals is a new AI-based tool that merges The Weather Company data with a company’s own operations data to reveal how minor fluctuations in weather affects business.

The combination of rich weather forecast data from The Weather Company and IBM’s AI and Cloud technologies is designed to provide a unique capability, which is being leveraged by agriculture, energy and utility companies, airlines, retailers and many others to make informed business decisions.

[1] The UN Department of Economic and Social Affairs, “World Population Prospects: The 2017 Revision”

[2] Business Insider Intelligence, 2016 report: https://www.businessinsider.com/internet-of-things-smart-agriculture-2016-10


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What if Amazon used AI to take on factories?

By ANTONY BOURNE, IFS Global Industry Director for Manufacturing

Amazon recently announced record profits of $3.03bn, breaking its own record for the third consecutive time. However, Amazon appears to be at a crossroads as to where it heads next. Beyond pouring additional energy into Amazon Prime, many have wondered whether the company may decide to enter an entirely new sector such as manufacturing to drive future growth, after all, it seems a logical step for the company with its finger in so many pies.

At this point, it is unclear whether Amazon would truly ‘get its hands dirty’ by manufacturing its own products on a grand scale. But what if it did? It’s worth exploring this reality. What if Amazon did decide to move into manufacturing, a sector dominated by traditional firms and one that is yet to see an explosive tech rival enter? After all, many similarly positioned tech giants have stuck to providing data analytics services or consulting to these firms rather than genuinely engaging with and analysing manufacturing techniques directly.

If Amazon did factories

If Amazon decided to take a step into manufacturing, it is likely that they could use the Echo range as a template of what AI can achieve. In recent years,Amazon gained expertise on the way to designing its Echo home speaker range that features Alexa, an artificial intelligence and IoT-based digital assistant.Amazon could replicate a similar form with the deployment of AI and Industrial IoT (IIoT) to create an autonomously-run smart manufacturing plant. Such a plant could feature IIoT sensors to enable the machinery to be run remotely and self-aware; managing external inputs and outputs such as supply deliveries and the shipping of finished goods. Just-in-time logistics would remove the need for warehousing while other machines could be placed in charge of maintenance using AI and remote access. Through this, Amazon could radically reduce the need for human labour and interaction in manufacturing as the use of AI, IIoT and data analytics will leave only the human role for monitoring and strategic evaluation. Amazon has been using autonomous robots in their logistics and distribution centres since 2017. As demonstrated with the Echo range, this technology is available now, with the full capabilities of Blockchain and 5G soon to be realised and allowing an exponentially-increased amount of data to be received, processed and communicated.

Manufacturing with knowledge

Theorising what Amazon’s manufacturing debut would look like provides a stark learning opportunity for traditional manufacturers. After all, wheneverAmazon has entered the fray in other traditional industries such as retail and logistics, the sector has never remained the same again. The key takeaway for manufacturers is that now is the time to start leveraging the sort of technologies and approaches to data management that Amazon is already doing in its current operations. When thinking about how to implement AI and new technologies in existing environments, specific end-business goals and targets must be considered, or else the end result will fail to live up to the most optimistic of expectations. As with any target and goal, the more targeted your objectives, the more competitive and transformative your results. Once specific targets and deliverables have been considered, the resources and methods of implementation must also be considered. As Amazon did with early automation of their distribution and logistics centres, manufacturers need to implement change gradually and be focused on achieving small and incremental results that will generate wider momentum and the appetite to lead more expansive changes.

In implementing newer technologies, manufacturers need to bear in mind two fundamental aspects of implementation: software and hardware solutions. Enterprise Resource Planning (ERP) software, which is increasingly bolstered by AI, will enable manufacturers to leverage the data from connected IoT devices, sensors, and automated systems from the factory floor and the wider business. ERP software will be the key to making strategic decisions and executing routine operational tasks more efficiently. This will allow manufacturers to keep on top of trends and deliver real-time forecasting and spot any potential problems before they impact the wider business.

As for the hardware, stock management drones and sensor-embedded hardware will be the eyes through which manufacturers view the impact emerging technologies bring to their operations. Unlike manual stock audits and counting, drones with AI capabilities can monitor stock intelligently around production so that operations are not disrupted or halted. Manufacturers will be able to see what is working, what is going wrong, and where there is potential for further improvement and change.

Knowledge for manufacturing

For many traditional manufacturers, they may see Amazon as a looming threat, and smart-factory technologies such as AI and Robotic Process Automation (RPA) as a far off utopia. However, 2019 presents a perfect opportunity for manufacturers themselves to really determine how the tech giants and emerging technologies will affect the industry. Technologies such as AI and IoT are available today; and the full benefits of these technologies will only deepen as they are implemented alongside the maturing of other emerging technologies such as 5G and Blockchain in the next 3-5 years. Manufacturers need to analyse the needs which these technologies can address and produce a proper plan on how to gradually implement these technologies to address specific targets and deliverables. AI-based software and hardware solutions will fundamentally revolutionise manufacturing, yet for 2019, manufacturers just have to be willing to make the first steps in modernisation.

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