AI sometimes seems to be the domain of mad data scientists and highly specialized, secretly initiated experts. But actually – through simple APIs and webservices – every application can benefit from touches of smart, without any of the black magic involved. Go through the applications portfolio, step-by-step, to find the application moments that would profit the most from added AI capabilities such as image recognition, natural language understanding, automated decisions, predictive analytics and recommendations. Use benefits logic to prioritize the cases and leverage a catalog of ready-to-implement AI services. Application users will love all that extra intelligence.
- Many AI and cognitive capabilities can be easily accessed through web services and APIs, including; image and voice recognition, intelligent automation, natural language processing and understanding, conversational systems (bots), plus predictive and prescriptive analytics.
- Often, these capabilities come with pre-trained models, eradicating the need to acquire training data and build algorithmic models.
- Instead of building AI and analytical solutions from scratch to leverage these capabilities, existing applications (whether ‘classic’ or mobile) can be augmented with them; this relates to both the applications that are already in use, as well as applications that still need to be delivered.
- Applications become ‘smarter’, creating more value for users with enhanced performance and speed.
- To effectively incorporate AI, the new and existing applications portfolio needs to be systematically reviewed to find the best opportunities for added value, whilst considering the benefits.
- Metrics-based portfolio management tools such as eAPM can enable creating this “Apps❤️AI” shortlist.
- Google added Smart Compose to its Gmail applications, using natural language processing capabilities to assist in effectively writing e-mails.
- Microsoft’s Anomaly Detector embeds anomaly detection into apps, to quickly identify potential problems, select the best-fitting detection model and ensure accuracy.
- Big Fish Games adds Microsoft’s Content Moderator capabilities to its games, ensuring proper profile and dialogue content to provide a positive player experience.
- IBM Watson Tone Analyzer can be added to customer service applications, responding to customers appropriately and at scale, detecting if they are satisfied or frustrated.
- The restaurant chain, Subway uses Amazon Personalize to deliver personalized recommendations for ingredients and flavors to guests using the Subway app.
- Google’s Cloud Vision Product Search can be added to any commercial website, allowing users to upload an image of what they want, for it to match products in their catalog.
- Tesco and French retailer, Monoprix are leveraging conversational commerce systems such as Alexa and Google Home together with machine learning capabilities. (Capgemini Research Institute)
- An insurance company added a cognitive application to ‘understand’ information from thousands of news feeds to reduce time in analytics, resulting in a reduction in risk assessment time by 70%, whilst reducing cost to serve individual claims.
- Extend the life span of existing applications by adding high-value functionality.
- Increase effectiveness and productivity of applications and automate manual activities that originally required cognitive capabilities previously considered unique to humans.
- Equip the larger population of software developers with a toolset to build powerful cognitive capabilities, without the need for a deep background in data science and analytics.
- Create a more compelling, personalized user experience in both business and consumer-oriented applications and mobile apps.
- Toolkits and platforms: Microsoft Cognitive Services, IBM Watson APIs, AWS AI Services, Pega Real-Time AI, Rainbird, Google Cloud AI Building Blocks
- Bots: Azure Bot Service, Siri, Cortana, Google Assistant, Alexa, ELSA Speak, Socratic, Fyle, DataBot, Hound, Youper, Robin