Is Conversational AI Right for Your Business?

Alex is desperate to get his vehicle tire replaced ahead of the long drive early the next day. The local tire store is closed for the day and opens late morning. Looking for options, he logs into the tire company website and is greeted by a new avatar.

Emma: Hello, I am Emma, here to help you. What are you looking for today?

Alex: Hi – I need replacement tires for my SUV.

Emma: Sure, I will be glad to help with that. Please select your vehicle make from the options provided ……

A few minutes later, Alex signs off, thanking Emma. He just placed an order for express delivery of two spare tires for his SUV to be delivered by 5:30 am. He is relieved. He can now catch a good sleep before he sets out on his long drive the next morning. 

In today’s world, while humanity sleeps, smart AI bots stay awake and keep your business ticking, answering routine questions, taking queries, recording service requests, or even helping complete a sale. With little or no human interaction from the business. An incredible upgrade to customer experience –unimaginable just a few years ago. That is the power of Conversational AI. 

As businesses transform digitally, they find that conversational AI for customer experience (CX) are viable options that must be considered.  

Read more: 5 Points to Evaluate Before Adopting AI in Your Organization

Key elements of Conversational AI

Conversational AI is an interesting blend of tech Natural Language Processing (NLP), machine learning (ML), deep learning, contextual awareness, and Art. It allows bots to converse like human beings by recognizing speech text, understanding context, understanding multiple languages, and creating as close to human interaction as possible. Art brings a human touch, elements of empathy, mannerism, and contextual personalization, making it seem very real.

Conversational AI comes in two flavors: AI chatbots that are predominantly text-driven and AI voice bots that serve voice responses. The realism in voice (personal language preferences, dialects, and tonality) brings a new stickiness level to the CX instance. 

Unlike older generation chatbots, Conversational AI bots are not script-based, rule-driven programs. The AI element (especially in voice bots) aims to ‘understand’ unspoken emotions and customize the menu’s responses to ensure a more meaningful experience. 

Technology components

Conversational AI technology includes NLP, machine learning, dialog management – which might need NLU (Natural Language Understanding) – where unstructured customer inputs are mapped based on context and keywords to something that can be interpreted. It then gets presented using NLG (Natural Language Generation). 

The process uses orchestrated responses for conversation flow, using the client’s history, context, and past responses as required. For Conversational AI voice bots, ASR (Automatic Speech Recognition) and TTS (Text to Speech) routines need to be integrated with necessary customization and training. This throws up unique challenges as language, and human interactions have cultural elements that vary widely across geographies. 

Successful deployments of Conversational AI are driving a change in the business rationale for its consideration. Initially, the business case for AI Chatbots evolved around reducing the human resource costs required to provide 24×7 Customer Service. However, when implemented correctly, it reduces people costs, saves on training, unlocks efficiency, and increases sales – a priceless return on investment!

Examples of successful conversational AI deployments

  • Financial Services: PwC, a globally reputed Big 4 Accounting firm, deployed AI-powered NLP-based chatbot to answer queries on HR, finance, and business process policies. Available to 10,000 employees in one region, PwC frees up the HR team to focus on more strategic issues and less on routine matters. 
  • Airline Sector: Lufthansa’s Elisa, Nelly, and Maria are the airline’s trained AI chatbots that go beyond answering routine questions from passengers. These chatbots will even connect a passenger to a live speaking human agent when required. The passenger doesn’t need to wait in line, and the agent has a complete context to address the query quickly.
  • Services & Utilities: Bluehost is a leading hosting services company with a chatbot well equipped to handle most client queries. This includes billing details, pending bills, current plans, renewals, and some others.
  • Insurance Industry: Geico, a leading insurance company, has its now popular and friendly AI virtual assistant, Gabby. Gabby is also in constant learning mode as it trains on customer inputs and queries to improve further. 
  • Fashion Industry: Sephora is a popular fashion brand that is an early adopter of conversational AI chatbot tech. The bot throws up a quiz for a new visitor, which it uses to customize recommendations and create a user profile. The bot allows you to apply various products to your selfies to visualize how you look in them. The popular bot is now rolled out across multiple platforms, including FB Messenger.

Not a panacea

Understanding customer process, system maturity, and knowing which customer touchpoints to automate are critical. If AI is introduced in a hurry before all customer interface contexts, workflows, and escalation paths are clearly defined or understood, it can create chaos and do more damage than good. 

Make sure you ask for the latest version of the AI model from the vendor, understand the road map, and consider the best for your deployment. The AI models need to be trained for your specific business context, product categories, and business logic. Also, test internally or do a pilot for early adapters and measure feedback to see the response before full-fledged large-scale deployment. Remember, AI is a tech solution and will not fix a broken business or client process.

Watch our video to learn more on improving your capabilities for AI/ML testing

Conversational AI is here to stay. Apart from all the apparent benefits stated in this article, the bot is a goldmine of data that can help conceptualize targeted campaigns and build memorable engagements if mined correctly and integrated into CRM. As other related technologies improve (such as gesture tech), expect AI chatbots to become a ‘must have’ for most businesses.

AI deployments are soaring in the business world at multiple touchpoints – from websites and social media platforms to apps and HR systems catering to customers and employees. 

Start a conversation with Trigent today

Conversational AI ensures your customer experience goes up a notch. Find out at what customer touchpoints in your business you should consider deploying conversation AI and how best to implement the solution. Ask our retail gurus at Trigent. We have developed AI solutions for our customers in the healthcare, banking and finance, transportation, and logistics industries. Benefit from our experience. We are optimistic this chat will help drive an entirely new conversation with your customers.

Over 70% of customer interactions will rely on chatbots in 2022. Delight your customers with Conversational AI. Let’s talk

AI in Education – A Realistic Look at the Effectiveness of AI in the Education Sector

A realistic view of the current adoption rate of AI in education, and pointers on how to ensure that it works, amidst the digital-learning hype.

When the kids in Montour school district (PA, USA) turned up to school that day in the fall of 2018, they were in for a surprise. They were told they would begin a brand-new course on Artificial Intelligence (AI). What on earth was AI? And what could it mean to kids in classes 5 and 6?

But this was a serious matter. MIT Media Lab and Media, Arts and Science Department at MIT, had come together and proposed to ‘catch them young’. The idea was to make an early introduction to concepts and practical AI lessons for middle school kids. All students from classes 5 to 8 would go through the AI Ethics program to identify use cases of gender / racial biases, privacy, and fairness. By the end of the 3-day course, they would know if such biases were embedded into the programs they would work on.

Welcome to generation AI. This makes millennium kids look antiquated. This new breed is sensitized to the good of AI and is aware of where it could go wrong. 

That is not all. Montour School district STEM teacher has co-developed a six-week program with Carnegie Mellon Dept of Computer Science called AI in Autonomous Robotics for 7 and 8-grade students. The implementation rigor here is quality stuff as kids are asked to solve real-world problems. 

Amper Music, the world’s first AI music composer and producer, has worked with music faculty at the school to develop a 10-day AI Music program for class 7 and 8 students. This school district is certainly leading the AI drive firing on all cylinders.

A host of universities, AI software firms, educators, and AI experts are coming together like never before to create early engagement for school kids into the AI world. And unlike what most of us would have thought: It is not only about STEM. In fact, the philosophy is to move from STEM to STEAM (with a liberal dose of Art – music, media, entertainment) thrown in for good measure. And this is happening in several pockets across the US.

AI in education sector – AI is here to stay, and the US campuses are already doing it

Across the United States, AI penetration within the education sector is tangible but may not be visible to the untrained eye. While varying in level of experimentation, schools and higher education institutes have embraced the tech and decided to learn how to harness its powers. 

Pittsburg-based Carnegie Learning1 offers AI-based personalized math, applied sciences, and language programs for post-high school students to rediscover learning. The entire program is personalized and self-paced, giving a new approach to STEM learners post-K12 schooling. The results demonstrated in some school districts in Washington and Texas prove the program creates a positive impact.

Duolingo2 is an amazingly popular AI-based customized language learning tool that allows anyone to learn a language. This is based on machine-driven instructions optimized for students based on millions of similar learning sessions held earlier. And most of the learning is for free.

California-based Content Technologies3 is a pioneer in AI and has developed several advanced AI systems for education. The Cram 101 is an AI tool that converts any textbook fed to it into chapter-wise byte-sized summaries, true or false type questions, learning concepts in record time. The company has developed similar tools for different disciplines such as nursing education, high school, and so on.

Some of the interesting outcomes of the approach of starting them young came from a US scientist, Ms. Druga, who built Cognimates, an AI platform for building games and programming robots and training AI models. Cognimates was incubated in MIT Media Labs. 

In a three-year study, where kids were taught to program bots to play games such as Rock and Scissors and build gaming applications using AI. One of the most profound observations came from Druga: When the kids came out after a session and said – “the computer is smart, but I am smarter”.

This was a powerful endorsement of how a young student comes away with a high level of confidence in the programmability of the computer to do what she wants it to do. This clearly establishes the argument about why AI perhaps should be started early on in school.

Next steps in playing this right – How can AI be used in education?

In general, schools and Universities must do the following to stay abreast of the AI curve and help imbue its benefits within the communities.

1.   Create a qualified AI resource team within the institution so they can track AI developments in peer institutes, vendor implementations and research the use cases.

2. Understand own deployments, migration of data systems into the AI realm, define implementation road map and create necessary stakeholder education of the new systems that will come.

3.   Educational institutions should also work with boards, government agencies, and accreditation bodies to define a structured AI curriculum for higher courses. This may require an industry interface also. This combination will create a Special Interest Group -university-industry – regulator group that will work together in ensuring the best interests of all concerned.

4. Faculty training, student and parent education, and awareness programs in terms of how the implementation could affect them need to be made available. Privacy and security rights of all stakeholders are paramount and need to be protected. How the schools intend to ensure data protection as machines become more powerful and open to sharing, receiving data from remote tutors, servers dynamically need to be shared transparently.

The Association for the Advancement of Artificial Intelligence (AAAI) and the Computer Science Teachers Association (CSTA) launched the AI for K-12 Working Group (AI4K12) to define for artificial intelligence what students should know and be able to do.

There are several such movements developing effective programs to deploy at various levels. These can help institutes understand better where AI is headed and how to ride this new technology wave to harness its full benefits.

Start your AI journey with Trigent

AI could well be the elephant in the classroom but if it’s a friendly elephant that can help enrich your life, you wouldn’t complain, would you?

At Trigent, we provide intuitive and easy to use AI solutions that ensuring seamless adoption of the latest technology. With the AI-powered tools from Trigent, you will be able to accelerate your digital transformation initiative in your organization successfully.

Want to know more? Get in touch with us for a quick consultation.

 References

  1. https://www.carnegielearning.com/why-cl/success-stories/
  2. https://www.duolingo.com/info
  3. http://contenttechnologiesinc.com/

AI in Media: Redefining Customer Experience with Immersive Stories

Artificial intelligence has become an important milestone in the digital transformation journey of all sectors, including media and entertainment. With the buzz it has created, it is no surprise that the adoption of AI in media and entertainment is a game-changer for the pioneering and the digitally inclined. It plays an immense role in the way content and experiences are curated and delivered at scale today. 

The next era of the Media industry is defined by customers’ increased demand for immersive, live, and shareable experiences. Consumers now wish to get more engaged, better connected, and closer with the stories they love – both in the digital and physical worlds. Companies have started empowering these experiences through emerging technologies. Big data and artificial intelligence will create the most dramatic change, redefining how the industry can connect with all stakeholders and drive growth.

Modern enterprises are now deploying AI tools and technologies to ensure effective decision-making and agile responsiveness to market changes. While over-the-top players like Netflix have already adopted a data-first approach, many others are still trying to attain AI success. The road to full-fledged AI adoption is not devoid of challenges. AI can be only as good as the data you have. Every effort must be made to efficiently manage different data types, including audience, operational, and content data.

As workflows and processes continue to become AI-enabled, we analyze the media and entertainment landscape to understand the impact of AI adoption.

Customization to optimization – the role of AI in media & entertainment sector

AI plays an important role in enhancing the user experience across all the six segments of the Media and Entertainment (M&E) industry: Films & TV, social media, journalism, gaming, music, and sports.  

Customer-focused experience with content personalization 

AI powers recommendation engines to predict what content should be promoted and when based on customer viewing data, search history, ratings, and even the device customers use. A classic case in point is Netflix’s landing cards1 helping the streaming website customize what you watch through personalized targeting. Images of lead characters are seen while scrolling to understand popular choices based on the cards people click. 

Machine classification algorithms for improved search optimization

AI also plays a significant role in search optimization thanks to machine classification algorithms that help in improving the categorization of movies. Users can search based on categories instead of individual titles to enable quick searches and smooth navigation. Streaming websites have enhanced streaming quality with AI since it helps them predict future demands and position their assets strategically to help users enjoy high-quality streaming even during peak hours.

Music streaming companies like Spotify and Apple Music rely on machine learning algorithms to segment users and songs to offer personalized recommendations and playlists. Natural Processing (NLP) gives them an edge by providing information about songs and artists from the web. AI has also been helping musicians generate lyrics and compose songs.

Enhanced news reporting with robot journalists

AI has a coveted place in social media and journalism too. While social media platforms like Facebook, Instagram, and Snapchat are using it to offer personalized products and services, Forbes and Bloomberg have been using robot journalists Bertie and Cyborg respectively to create storylines based on their parameters and data.

The Washington Post, too, gave us a taste of the future of journalism with its Heliograf2 that covered the Olympics. However, the Chinese news aggregation service Toutiao took it to the next level by creating an AI-enabled reporter Xiaomingbot that churned out a whopping 450 articles during the Rio Olympics in just 15 days.  

Gaming and customer-specific advertising

As the supply of mobile games continues to exceed demand, companies are now using AI to estimate customer lifetime value (CLV) to bid efficiently in advertising for users, focusing only on those who would enthusiastically engage with their products. AI is also helping animators bring exciting characters to life for a multitude of virtual reality games and movies.

 Improved entertainment quotient in sports broadcasting

The perennial popularization of sports brings new fans, players, and subscribers into the sports and gaming fold. AI satiates them with entertaining shots and angles during live telecasts and enhances the experience by broadcasting exclusive footage captured by drones.

Laying deeper data foundations for successful adoption of AI in media

AI has forayed into virtually all functions and areas to add value in a highly competitive market. As competitive pressures intensify, it has become more critical than ever to fast-track your AI initiatives and reap their benefits. But as with every other digitalization endeavor, AI adoption too brings along unique challenges.

Here’s what you can do to overcome them and lay deeper data foundations for successful AI adoption. 

Assess AI maturity 

M&E businesses are now shifting from B2B to B2C business models due to the direct-to-consumer delivery and consumption trends and hence are currently operating on massive amounts of data. In order to make complete sense of this data and drive decisions, data silos need to be removed first. A fragmented approach is not going to work and should be replaced with a data-first approach.

Organizations often get caught up in a quandary, wondering if they should modernize the data architecture first for their AI models to rest upon or build a model and modernize only that part of the required data. However, the right approach would be to invest in a sound strategy for your target data architecture that relies on proven models to avoid pitfalls and rework. Data management should be a top concern for organizations to interpret and get actionable insights.

Focus on people and processes 

Data sources will continue to increase, causing greater challenges for data management and project management. So while building your technology stack, it is equally important to invest in people and processes that would be at the helm of things while progressing up the AI maturity curve.

AI leaders believe in including technologists and data scientists in business teams to give them the visibility to understand business challenges. It is essential that business leaders, values, people, and culture are aligned to enable successful automation and AI adoption. Only then would human employees be able to work alongside robots and AI-powered machines to build capabilities and deliver value.

Adopt a continuous improvement approach

AI is not a one-time endeavor but will continue to evolve with time. To achieve enterprise-wide AI, it needs to be perceived as a transformational initiative that must be implemented across all front-end and back-end processes.

A comprehensive picture of ROI based on revenue and costs for different functions and processes can give organizations the clarity to track value and identify areas that need to improve. M&E companies are integrating established AI processes into finance, HR, and other functions to garner cost and operational efficiencies.

The future of entertainment looks AI-centric

AI is undeniably transforming the media and entertainment sector, empowering them to make informed decisions based on critical data analysis. It will navigate disruption and drive growth in all spheres by addressing data gaps and helping M&E companies become more agile. Clearly, AI is impacting everyday entertainment in a big way, and it’s time organizations harnessed its power to fine-tune their forward-thinking strategies and explore new avenues.

Discover the power of AI with Trigent

The technology experts at Trigent have been offering robust AI-enabled solutions to M&E companies based on data from diverse sources and powerful algorithms to enable a superlative user experience while giving them insights into customer behavior. 

We help build excellent AI capabilities and advanced features to deliver content in the most effective manner. We can help you build high-quality datasets to get the best results in diverse settings and drive impact at scale. 

Call us now for a business consultation

References

  1. https://www.wired.co.uk/article/netflix-data-personalisation-watching
  2. https://futurism.com/the-future-of-writing-chinas-ai-reporter-published-450-articles-during-rio-olympics 

Quick Wins in Enterprise Digital Transformation (yet often ignored) – Intelligent Automation

The modern workplace is seeing widespread usage of machines and automation. Enterprise digital transformation, Artificial intelligence (AI), and automation are changing the tide for businesses globally. This means a significant change in the work culture as employees will have to acquire new skills and adapt to the advanced capabilities of machines. 

As per a recent study1 involving over 600 business leaders from 13 countries, more than 50 percent of respondents confessed to having already invested over $10 Million in intelligent automation projects. The AI market globally is presently growing at a CAGR of 40%, all set to touch $26.4 Billion by 2023.

AI,  along with robotic process automation (RPA), voice recognition, natural language processing (NLP), and machine learning (ML), is allowing businesses to blend automation with human capabilities successfully to create intelligent working environments. 

Automation is driving agility for businesses giving them the much-needed competitive edge over others with quick decision-making powers. Clearly, decision velocity powered by AI-driven insights gives you data supremacy to lead in a highly volatile market.

Making a case for Intelligent Process Automation (IPA)

When automation meets artificial intelligence, you get intelligent process automation to scale up your business. While it allows you to off-load routine, repetitive tasks, it empowers better guardrails for all your automation initiatives. It takes the uncertainty out of the picture and enables more personalized execution and processes.

Intelligent automation enhances the overall customer experience. The speed of response has often been a critical consideration while evaluating the customer experience. Intelligent automation is helping organizations meet customer expectations with personalization. Through customized offers, services, and content, businesses are acquiring and retaining customers.

What do the right IPA endeavors ensure?

  • Agile services due to a significant reduction in processing time
  • Greater flexibility and scalability for being able to operate round the clock with capabilities to scale up and down as required
  • Improved quality control due to greater traceability of events and instances and checks at different levels
  • Increased savings and productivity due to a high level of automation
  • Clear, actionable insights to predict and improve drivers of performance

While there is unanimous agreement on the benefits of intelligent automation, not everyone has leveraged these benefits across the organization. What you need is an enterprise-wide approach that promotes a new way of working.

Adding intelligence to the digital mix

A highly automated world does not focus on reducing the headcount but increasing its potential to do more in an agile manner to solve the business challenges of tomorrow. It relies on structured and unstructured data the company collects from the public domain and other stakeholders rather than depending on traditional methods.

Intelligent automation compels you to rethink key business processes. The sales and marketing team gets deeper segmentation to target and sell through advanced analytics. Those working to strengthen the supply chain get to improve production and distribution by leveraging technologies like cloud and analytics across the value chain. Planning and development teams, on the other hand, rely on data-driven insights to integrate them into product performance and boost innovation.

Alibaba Group2 is a classic example of what you can achieve with intelligent automation.

After making significant strides in eCommerce and retail, it has further revolutionized its business processes with its ‘Industrial Vision AI‘ solution for manufacturing and production. It allows the company to inspect raw materials thoroughly to detect minute defects, resulting in a 5X increase in production efficiency. Its automated warehouse is managed entirely by robots taking precision and efficiency to a whole new level.

Regardless of your goal, you need to create a strategic roadmap to align it with your business priorities. This is not possible unless you assess your digital maturity.

What is the role of IT in successful IPA transformation?

Intelligent process automation (IPA) is a melting pot of technologies enabling significant gains for businesses worldwide. IPA should not be confused with robotic process automation (RPA) as unlike RPA that performs repetitive, automated tasks based on predefined rules and inputs, IPA can understand the context, learn, and iterate to support informed decision-making using unstructured and structured data.

Those who have been able to get the full value of IPA have been the ones who have put IT leaders at the helm of their IPA endeavors. CIOs need to strengthen their core with IPA programs to support automation.

Here’s what we recommend:

  1. Assess the high-level value potential

You may start with help-desk requests since that’s where a significant amount of incidents originate. While tickets with low difficulty levels are resolved immediately, those with more complexity are often escalated to specialized teams. Determine how many such requests were handled the previous year, and by multiplying them by the average handling time (AHT) required, you can evaluate the value of this whole exercise.

For instance, an organization with a significant number of requests for password reset or access can leverage RPA bots that work across multiple applications via the user interface to automate ticket resolution and free up employee capacity. Reducing resolution times and a drop in costs associated with outsourcing help-desk support will thus improve performance and profits.

The effort required for these activities often varies. Everything needs to be evaluated critically from backups and patching to security audits and upgrades to understand the effort involved and the value you can garner by planning activities for automation.

  1. Identify the use cases best suited for IPA

Let’s consider the same example mentioned above. In order to automate incidents, organizations need to first identify the ones ideal for automation. An organization may be effectively logging incidents in detail, but due to the large numbers and complexities, support teams may not respond quickly and effectively.

AI can make sense of the chaos and understand the reasons behind the alerts. It may be trained to make appropriate recommendations or even make better decisions to ensure suitable responses.

  1. Elevate customer experiences with better service

AI and automation are changing the customer service landscape for every industry, from retail to aviation. Boeing has a fleet of passenger service robots that operate via sensors installed in their bodies. They are doing their best to reduce the manual work of cabin crews. Though experts argue a human perspective is required for these robots to do what humans can.

The key is to understand the power of automation and integrate it seamlessly into processes and workflows to complement human efforts and endeavors perfectly, as we did in the case of one of our clients Surge Transportation.

The company links shippers and carriers and has an automated tracking and monitoring system to assign loads. But the pricing and quotation were being done manually. This drained their resources, led to a huge turnaround time, and left a long log of emails, calls, and paper trails.

Trigent critically evaluated the complexities in its pricing mechanism to bring down the turnaround time to less than a second. Apart from 100% pricing accuracy, the company improved profits by 25%, revenue by 40%, and reduced the load processing time by 91%. With seamless carrier integration, the company now processes 4000 more loads per day.

Other use cases where AI and Automation are driving value

Cashier-less stores

Amazon is popularizing the concept of cashier-less stores with Amazon Go and Just Walk Out. Robotization of stores helps save operational expenses and gives shoppers a smart shopping experience.

Automated medical appointment scheduling

No-shows have been the cause of losses of over $150 billion a year for the U.S. healthcare system with every unused time slot costing individual physicians $200 on an average. No-shows also impact the health of patients since continuity of care is interrupted. IPA challenges traditional scheduling methods by ensuring error-free appointment scheduling based on the nature of the illness, the convenience of patients, and the availability of doctors and healthcare facilities. While patients get to choose a date and time for different health issues, follow-up appointments can be scheduled automatically along with reminders.

Automated supply chains

The ideal supply chain is where there is neither wastage nor out-of-stock scenarios. In tandem with machine learning, AI predicts demand based on location, weather, trends, promotions, and other factors. Revenue losses of up to $4Trillion have been caused due to supply chain disruptions following the pandemic with 33% attributed to commodity pricing fluctuations as per a report.

The automobile giant Toyota is using AI in its manufacturing environment to address waste control with its ability to predict when excess parts, products, and practices threaten to impede work.

Intelligent Automation is clearly on a winning streak!

The potential value of AI and automation is immense for different sectors and will vary depending on the type of industry, availability of abundant and complex data, use cases, and other factors. To get the most out of your automation initiatives, it is however important to tide over organizational challenges with the right mindset and approach.

Create impact and value with Trigent

Trigent with its team of technology experts empowers you to stay relevant and competitive. It is equipped with insights and intelligent solutions to dramatically boost your bottom line and improve customer engagement.

Allow us to help you grow your business and increase revenue with strategies and solutions that are perfect for you.

Call us today for a business consultation

References
1. https://www.analyticsinsight.net/intelligent-automation-accelerating-speed-and-accuracy-in-business-operations/
2. https://datacentremagazine.com/technology-and-ai/alibaba-group-adopts-ai-and-automation-singles-day

AI Implementation Checklist – 5 Points to Evaluate Before Adopting AI in Your Organization

Artificial intelligence is now all around us in household gadgets as well as business workflows. AI adoption is rampant across sectors; the global artificial intelligence market is expected to reach $ 266.92 billion by 20271 at a CAGR of 33.2% during 2019-2027. Nearly half of the respondents who had participated in a survey confessed to being interested in AI implementation and machine learning to improve data quality.

No doubt, artificial intelligence is the proverbial genie that does everything we want it to do without even rubbing the magic lamp. But the lack of nuance and failure to spell out caveats can result in AI systems that will make us think twice before we wish for anything.

Believe it or not, misaligned AI can be a nightmare.

A classic case is YouTube2, with its AI-based content recommendation algorithms that led to users accusing it of radicalization. Its constant upping-the-ante approach led users to extreme content in a bid to maximize viewing time. So videos on vegetarianism led to veganism, and jogging searches resulted in ultramarathons. This unintentional polarizing and radicalizing highlights one significant challenge: we have yet to define the goals accurately for our AI systems!

The sad truth is that we don’t even know what we want, at least not from our autonomous systems and gadgets and other possessions. For instance, a self-driving car may be too slow and brake too often just the way it was designed to prevent itself from colliding with nearby objects. But the object could be as insignificant as a paper bag that was blown away by the wind.

What we need is goal-oriented AI born with a solid sense of purpose with excellent human-machine coordination. But only after you have answered the question- Do I really need AI?

Here’s is your ultimate AI implementation checklist

AI has ample scope in many sectors. AI can interact on your behalf with customers, as in the case of chatbots, or help healthcare providers diagnose cancer and other ailments. If leveraged well, it can help you turn a new leaf in critical interactions with your customers. Understanding the potential of AI and applying it to enhance critical business values can make a world of difference to your business. The key is to know where you stand and whether AI can help you attain your business goals.

Identify the purpose

Organizations with successful AI implementations are usually the ones that have assessed its financial impact or conducted a thorough risk analysis for AI projects. Having the right metrics in place gives you a sneak peek into the risks and benefits of AI implementation and how it would perform in those chosen areas. While it may not guarantee a positive ROI, it gives you a fair idea about what to expect. 

Accuracy, for instance, is an important metric, but it’s not enough to understand how well your AI systems are performing. You need to correlate  AI metrics to business outcomes to ensure you get the most out of your investments. 

The smart pricing tool created by Airbnb to eliminate pricing disparities between black hosts and white hosts presents a classic example. While the AI-based system performed the assigned tasks with precision, the business results fell short – widening the gap further by 20%. 

Appoint mixed-role teams for all AI initiatives

Those who have implemented AI successfully will tell you how crucial it is to build mixed-role teams comprising project managers, strategists, application designers, AI researchers, and data scientists to ensure a diversity of thought and skillsets. As per a Garnet Research Circle survey3, skills are the first barrier to AI adoption, and 56 percent of respondents believed new skills are required for new and existing jobs.

AI needs experts for it to evolve to its best version. TayTweets, a promising chatbot by Microsoft, was nothing but fun, and people loved talking to her. Until, of course, she became the nastiest chatbot ever in less than 24 hours, responding with offensive tweets. It demonstrates how horribly things can go wrong when AI and ML go awry when left unchecked.

Diversity in technical acumen enhances the value of AI to customers since the people working with AI know-how and where it should be used to have the most significant impact. Whether you want to hire new people or train existing ones for newer roles and responsibilities is something you will have to decide based on the business initiatives you have in mind.

Make a business case for AI

Businesses need AI for different reasons ranging from data security and fraud detection to supply chain management and customer support. You need to identify the use cases and applications to determine how AI can be effectively used. Organizations depend on AI to analyze contextual interaction data in real-time and compare it with historical data to get insights and recommendations.

Data plays a pivotal role in every aspect of a business. While a lot of emphases is placed on coding, math, and algorithms, many organizations are not able to apply the data acquired effectively in a business context. You will have to understand who you are building these solutions for and what technology framework you will require to do so.

As Moutusi Sau, principal research analyst at Gartner4, points out, “Business cases for AI projects are complex to develop as the costs and benefits are harder to predict than for most other IT projects. Challenges particular to AI projects include additional layers of complexity, opaqueness, and unpredictability that just aren’t found in other standard technology.”

Assess your AI maturity

It is impossible to arrive at a strategy without evaluating where you stand against the AI maturity model. Once you know it, you can decide the next steps. Typically, the AI maturity model has five levels:

Ø Level 1 – There is awareness in the organization, and AI implementation is being considered, but no concrete steps have been taken in that direction.

Ø Level 2 – AI is actively present in proofs of concept and pilot projects.

Ø Level 3 – AI is operational, and at least one AI project has made its way to production with a dedicated team and budget. 

Ø Level 4 – AI is part of new digital projects, and AI-powered applications are now an essential part of the business ecosystem.

Ø Level 5 – This should be the ultimate goal where AI is now ingrained in the organizational DNA and plays a transformative role for your business. 

Look beyond the hype

AI can cause ‘cultural anxiety’ as a significant shift in thought and behavior is necessary for successful AI adoption. A compelling story to help employees understand how AI would be beneficial to all is necessary to ease the resistance they might feel towards the change.  CIOs should recognize their fears and anxiety of the possibility of being replaced by machines and encourage an open dialogue with team members. This will build trust and help determine if the organization is ready for AI.

The hype around AI itself can sometimes be the biggest problem as organizations hurry to hop onto the AI bandwagon with insufficient understanding of its impact. Explains Whit Andrews, distinguished vice president analyst at Gartner, “AI projects face unique obstacles due to their scope and popularity, misperceptions about their value, the nature of the data they touch, and cultural concerns. To surmount these hurdles, CIOs should set realistic expectations, identify suitable use cases and create new organizational structures.” 

 AI to Impact

The biggest mistake organizations make when they invest in AI is that they have too many expectations and little understanding of AI capabilities. Rather than getting caught in the hype, you have to be realistic and evaluate its role critically in furthering your business objectives.

AI is an expensive investment that will give you good returns if you know how to use it. A lot of tools are good, but not every AI tool is suitable for your business. What you need is the right AI implementation strategy created with professional help from those who know AI like the back of their hand.

Adopt AI with Trigent

Artificial intelligence is a defining technology that can be successfully integrated into business workflows and applications. We at Trigent have been helping organizations from diverse sectors, including healthcare, retail, BFSI, and logistics, create AI operating models that are optimized for faster and effective outcomes. 

We can help you, too, with everything from strategy and implementation to maintenance and support.

Call us today to book a business consultation

References

  1. https://www.fortunebusinessinsights.com/industry-reports/artificial-intelligence-market-100114
  2. https://firstmonday.org/ojs/index.php/fm/article/view/10419/9404
  3. https://www.gartner.com/smarterwithgartner/3-barriers-to-ai-adoption/
  4. https://www.gartner.com/smarterwithgartner/how-to-build-a-business-case-for-artificial-intelligence/

To Opt or Not? Can Traditional Industries Use Machine Learning to Garner Business Insights?

Machine learning is a scientific discipline that uses algorithms to learn from data instead of relying on rules-based programming. It works in three stages, i.e. data processing, model building & deployment, and monitoring, with machine learning binding the three together. The power of machine or deep learning cannot be underestimated and as Alexander Linden, Research Vice President of Gartner says, ‘Deep learning can give promising results when interpreting medical images in order to diagnose cancer early. It can also help improve the sight of visually impaired people, control self-driving vehicles, or recognize and understand a specific person’s speech’.

To Opt or Not

Traditional industries have many processes which are governed by rules-based software. This approach is limited in its ability to tackle complex processes. If the rules-based learning can be substituted with self-learning algorithms, then valuable patterns and solutions would emerge.

As a result of digital data and Internet of Things there is a proliferation of data. If you believe this data will help you make intelligent decisions based on patterns, add machine learning. There is no need to add it otherwise as it can make an existing business complicated. Starting with the smaller pieces of the puzzle is better than jumping into it head on. For example, one can collate information from regular reports, apply machine learning to forward-looking predictions.

Machine learning can be useful to detect anomalies, enhance customer services and recommend new products. Manufacturing companies, for example, can benefit from machine learning by self-examining videos where defects can be spotted and automatically rerouted.

Recent developments in machine learning suggest a future in which robots, machines, and devices will be able to operate more independently if they run on self-learning algorithms. This would have far reaching effect in terms of improved efficiency, and cost savings.

Related: Reshaping your business with AI

Machine learning works best on specific tasks where input and output can be clearly stated. If an organization has a sufficient amount of data, with enough variation, machine learning can produce meaningful approximations.

Finally, it is the technical barriers that become the biggest hurdle in the transition process. To address the actual challenges and the perceived ones, companies need to identify expert data analysts who are capable of developing the intricate algorithms that machine learning requires. It will also require a team of engineers who can provide strategic direction, manage quality, and train internal resources on the tool.

The Impact of Artificial Intelligence on the Healthcare Industry

Artificial Intelligence (AI) is predicted to play a game-changing role in patient care. Let’s take a small example of its help in medical diagnosis. Imagine a scenario where a patient walks into a doctor’s office with symptoms indicative of several possible illnesses.  The doctor, to be sure, consults a digital assistant which scans a global database and comes up with a solution based on deep data analysis. The doctor goes on to prescribe further tests to confirm the prediction,  and here too, machine learning helps with comparing the images to the database and confirms the most likely cause of illness.  The doctor has just hastened patient care and with the help of accumulated intelligence has diagnosed the case. Not stopping there, the doctor introduces the patient to a chat-bot that explains the disease and its treatment. It schedules follow-up visits as well as any further investigations, if required. AI has just proved how invaluable it can be in patient care, by shortening the diagnosis to treatment curve.  Where time is of the essence, AI has proved how invaluable it can be.

Machine learning has brought AI to the forefront of healthcare and it is likely that its impact on diagnosing and treating diseases will be unsurpassed.  Recognizing this trend, a 2016 study by Frost & Sullivan, projects AI in healthcare to reach $6.6 billion by 2021, a 40 percent growth rate.  The study further confirms that AI will enhance patient care delivery by strengthening the medical imaging diagnosis process.    As an industry disrupter, AI will create real value for patients by supporting prevention, diagnosis, treatment, management and drug creation.

Technology experts predict that in the next couple of decades AI will be a standard component of healthcare – augmenting and amplifying human effort.  Its role will be as impactful and as quiet as the common X-ray machine.  It will also automate several health care tasks that are time-consuming and which require tons of unstructured data to be converted into intelligence.

While some of the innovations that we are talking about are futuristic in nature, AI has already quietly infiltrated this industry. It is already being used by healthcare players to manage billing, appointment fixing, and logistics planning.  To move into core clinical areas requires an amassing of data and that too has already begun.  With quantifiable data, diagnostics will become accurate and as a result indispensable in medical treatment.  Does this mean that we will see robot doctors in the place of human medical professionals?  Let’s leave that to science fiction movies for now.  What is more likely to happen is AI-enabled medical professionals.

To summarize, we can only imagine AI’s impact on saving human lives, going forward. For example, just imagine people in remote areas with limited access to diagnostics.  AI has just helped the local medical professional to remotely prescribe treatment, deliver medicines through an automated delivery system and prescribe telemedicine.  In a way, it has just helped to shrink the world.

Technology companies focusing on the healthcare segment are investing in Centers of Excellence where AI empowered healthcare IoT will bring about some dynamic changes, not to mention better control over existing processes such as supply chain, inventory management, equipment management, invoicing and drug development and reduce latency, lower cost and deliver operational efficiency. At Trigent, while we solve the problem of productivity, we remain focused on helping healthcare organizations take care of more people with less resources.  We do this by tapping our knowledge, experience and expertise in data and machine learning.

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