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When it comes to artificial intelligence (AI), the path from ideation to implementation can be a long and technical one. Hiring the right talent. Wrangling data. Setting up the right infrastructure. Experimenting with models. Navigating the IP landscape. Waiting for results. More experimenting. More waiting. Enterprise AI implementation can be one of the most complex initiatives for IT and business teams today. And that’s before acknowledging the time pressure created by fast-moving markets and competitors.

As an AI software and automation company, Entefy works with private and public companies both as an advanced technology provider and an innovation advisor. This has given our team a unique perspective on what it takes to successfully bring AI to life. Our experience has taught us how to avoid major missteps organizations often make when developing or adopting AI and machine learning (ML) capabilities. These 5 missteps can derail productivity and growth at any organization:

Unrealistic expectations

“I hear that AI is the future. So let’s build some algorithms, point them at our data, and we should be awash in new insights by the end of the week.” Success with AI begins with adopting a more nuanced understanding of what artificial intelligence can and can’t do. AI and machine learning can indeed create remarkable new insights and capabilities. But you need to align your expectations with reality. This isn’t a new lesson. Enterprise-scale software deployments demonstrate this idea time and again. Software itself isn’t magic. The magic emerges from forward thinking application design and the effective integration of new capabilities into business processes. Think about your AI program in the same way, and you’ll be on the path to long-term success.

Expecting AI to instantly deliver transformative capabilities across your entire organization is unreasonable. Over the short term, narrowly defined projects can indeed be quickly deployed to deliver impressive impact. But to expect spontaneous intelligence to spring to life immediately can lead to missed expectations. The best way to view AI today is to focus on the “learning” in machine learning, not the “intelligence” in artificial intelligence. 

Development of advanced AI/ML systems is experimental in nature. Algorithmic tuning (iterative improvement) can be time-intensive and can cause unanticipated delays along the way. Equally time intensive is the process of data curation, a key pre-requisite step to prepare for training. Because of this, precise cost and ROI projections are difficult to ascertain upfront.

Short-term tactics without long-term planning 

It is easy to fall in love with AI. So it’s a common mistake to prioritize technology over goals and outcomes. Symptoms include approaching every problem with a popular technique or over-relying on one tool or framework. Instead, invest time in identifying needs and priorities before moving on to AI vendor selection or development planning.

Approach an AI project with a defined end goal. What problem are we solving? Ensure you have a clear understanding of the potential benefits as well as the impact to the existing business process. Turn to technology considerations only after you have a clear understanding of the problem you want to solve.

To better understand the limitations of AI, start by looking at information silos. The type of silos that result from teams, departments, and divisions storing information in isolation from one another. This limits access to critical knowledge and creates issues around data availability and integrity. The root cause is prioritizing short-term needs over long-term interoperability. With AI, this happens when companies develop multiple narrowly-scoped expert systems that can’t be leveraged to solve other business problems. Working with AI providers who offer diverse intelligence capabilities can go a long way to avoiding AI silos and, over time, increasing ROI.

Long-term planning should also consider compliance and legislation, especially as they pertain to data privacy. Without guidelines for sourcing, training, and using data, organizations risk violating privacy rules and regulations. The global reach of the EU General Data Protection Regulation (GDPR) law, combined with the growing trend toward data privacy legislation in the U.S., makes the treatment of data more complex and important than ever. Don’t let short-term considerations impede your long-term compliance obligations under these laws. 

Model mania 

The first rule of AI modeling is to resist the urge of jumping straight into code and algorithms until the identification of goals and intended results. After that, you can begin evaluating how to leverage specific models and frameworks with purpose. For example, starting with the idea that deep learning is going to “work magic” is the proverbial cart before the horse. You need a destination before any effective decisions can be made.

 There is a lot of misinformation around which AI methods work best for specific use cases or industries. Deep learning doesn’t always outperform classical machine learning. Or, industry-specific AI will not necessarily give you the best results. So try your best to be results driven, not method driven. And don’t let trends influence that decision. For example, neural networks have been going in and out of style for decades, ever since researchers first proposed them in 1944.

When making decisions about model selection, it’s necessary to consider 3 key factors—time, compute, and performance. A sophisticated deep learning approach may yield high probability results, but it does so relying on often costly CPU/GPU horsepower. Different algorithms have different costs and benefits in these areas.

The lesson is simple: A specific machine learning technique is either effective for achieving your specific goal, or it is not. When a particular approach works in the context of one problem, it’s natural to prioritize that approach when tackling the next problem. Random decision forests, for instance, are powerful and flexible algorithms that can be broadly applied to many problems. But resist settling into a comfort zone and know that AI success comes from frequent and ongoing experimentation.

Data considerations

Data matters. Good data can’t fix a bad model, but bad data can ruin a good model. It is a myth that AI/ML success requires massive datasets. In practice, data quality is often more likely to determine the success of your project. The challenge is two-fold. First, it’s necessary to understand how the structure of your data relates to your overarching goal. Second, the value of proper modeling can’t be understated, no matter how large the dataset.

Be sure to consider the 4 Vs of data to ensure success in advanced AI initiatives. The 4Vs include data volume, variety, velocity, and veracity. The road from data to insights can be patchy and long, requiring many types of expertise. Dealing with the 4 Vs early in the exploration process can help accelerate discovery and unlock otherwise hidden value. 

The successful preparation and processing of data is a highly complex exercise in multi-dimensional chess—every consideration is connected to multiple other considerations. Common issues entail: Ineffective pre-processing of data; trying to simplify the data with too strong a dimensionality reduction; excessive data wrangling; poorly annotated datasets. And there’s no single best practice. Data curation at its core is problem-specific.

Underestimating the human element

Large-scale rollouts often fail due to a range of human factors. Users aren’t properly trained. Features don’t enhance existing workflows. UI/UX is confusing. The company culture isn’t AI forward. 

Full realization of the benefits of AI starts with an empowered, educated workforce. Best practices for this include training strategies centered around continuous improvement in organization-wide technical training as well as leadership development for key champions of the project.

When it comes to hiring AI/ML talent, the situation on the ground is sobering for any organization with ambitions to rapidly scale internal AI capabilities. Stories of newly minted machine learning graduates fetching steep salaries are real, as practically every large company on the planet drives up demand for a limited pool of qualified candidates. Then there’s the reality of ML resume inflation, where some job seekers add machine learning credentials without the necessary skills or experience to deliver real value.

Traditional software system development follows the plan-design-build-test-deploy framework. AI development follows a slightly different path due to its experimental nature. Much time and effort is needed to identify and curate the right datasets, train models, and optimize model performance. Ensure your technical and business teams align on these differences and that they have the required skills to remain productive in this new environment.

Conclusion

There are countless parallels between the early adoption of enterprise software decades ago and the rollout of AI and machine learning today. In both cases, organizations have faced pressure to leverage the power of new capabilities quickly and effectively. The path to success is complex and fraught with pitfalls, covering everything from personnel training to thoughtfully scripted rollouts.

The lesson of that earlier time was this: After all of the strategic and tactical wrinkles of software implementation were addressed, the new solutions did indeed make a significant impact on people and their organizations. The AI story is no different. Deploying intelligence capabilities can be challenging but the competitive advantages they confer are transformational.

The many disruptions in 2020 effectively illustrated the fragility of life, work, and society at large. People and organizations alike scrambled to solve problems in ways not considered before the COVID-19 pandemic. Many businesses suffered hefty losses while others survived, and even thrived in some cases, with increased agility and a move toward modern technologies.

In reflecting upon last year’s events and the use of advanced technologies, including artificial intelligence (AI) and machine learning, we observed promising activity in several key sectors such as healthcare, manufacturing, retail, finance, and education.

In healthcare for instance, while COVID-19 drastically shifted life for everyone, many essential healthcare workers were on the frontlines to help overcome the global pandemic with assistance from machine learning. One particularly promising example came from MIT researchers who developed an AI model to help them diagnose asymptomatic COVID-19 patients through the sound of their coughs. The difference between a healthy cough and an asymptomatic cough cannot be heard by the human ear, but when “the researchers trained the model on tens of thousands of samples of coughs,” the AI system discerned asymptomatic coughs with 100% accuracy. As we continue to keep physical distance from each other, a widely available test like this for asymptomatic patients could help the world flatten the curve.

Across the pond in the UK, a research project is being funded to track side effects related to COVID-19 vaccines as they are distributed. With several companies vying to deliver their vaccines to the market as quickly as possible, this tool is used to track adverse side effects. The awarded government contract for this purpose indicates “that the AI tool will ‘process the expected high volume of Covid-19 vaccine adverse drug reaction (ADRs) and ensure that no details . . . are missed.’” Other use cases that leverage AI to combat the pandemic, can be found on our previous blog, “How machine learning will help us outsmart the coronavirus.”

Companies outside of healthcare also took advantage of machine intelligence to showcase new capabilities or streamline their operations. For example, in select major cities across the U.S., driverless cars performed additional road testing. Case in point, during last quarter, Cruise introduced its very first driverless car hitting the asphalt in San Francisco. While there was a human in the passenger seat to experience the ride, this was the company’s first step toward securing permits to launch a commercial service using its autonomous vehicles.

For many who had never heard of Zoom prior to the pandemic, virtual video communication technology became nearly ubiquitous for those who could no longer communicate or collaborate in person—at home, at work, in education. This means more people relied on these types of technologies to perform functions they would normally handle face-to-face. People began to think of video communication as the virtual water cooler for happy hours, birthday celebrations, and other meet ups. Even visits to Santa went viral. AI models are taking virtual communication to the next level with chatbots, improved personalization, smart replies, and more.

While much was accomplished with AI last year, 2021 promises to do even more. Here are some of the trends we expect to unfold this year:

AI spend will break through previous records

To adapt to the major disruptions caused by the pandemic and the ensuing social and economic shifts, businesses and governments worldwide have begun increasing technology spends while lowering budgets in other departments such as HR and marketing. According to Gartner, 67% of board of directors (BoDs) surveyed foresee expansion to the technology budget and part of that budget belongs to advanced technologies with AI and analytics “expected to emerge stronger as game-changer technologies.”

Competition in the coming years will require organizations to adopt AI at a faster rate. Machine learning will help augment human power by unearthing new insights otherwise hidden in data and by automating a series of workflows, tasks, and processes that consume too much human time and effort. This can be consequential in many areas of operations including finance, sales, product development and delivery, security, and IT. These needs will push technology spending to new heights. Over the next four years alone, global AI spending is forecasted to double from “50.1 billion in 2020 to more than $110 billion in 2024.”

CIOs will help lead the productivity revolution

More enterprises will implement AI strategies by leaning on their CIOs to achieve real business results. This year, experimentations with machine learning will accelerate but that alone will not be sufficient. Enterprise CIOs will be under increasing pressure to explore, select, and implement suitable technologies that can power the intelligent enterprise. Their focus will remain on maximizing productivity by streamlining the many facets of internal operations.

As of 2019, “only 8% of firms engage in core practices that support widespread adoption. Most firms have run only ad hoc pilots or are applying AI in just a single business process.” Focusing on AI core practices as opposed to ad hoc implementations will not only enable stronger adoption within these organizations, but will also foster additional cross-team collaboration for better results. CIOs encouraging adoption of these new technologies will empower employees to explore and test AI projects so that they are used as efficiently as possible. This will help drive business success as in-person workflows remain disrupted by the pandemic, with an accelerated secular push toward remote work.

AI will become more widespread  

A natural byproduct of increased C-suite adoption of AI deployments within the enterprise is efficiency via automation, speed, and scale. Widespread adoption of intelligent applications and process automation simply translates into cost reductions and time savings. According to Gartner, “organizations want to reach the next level by delivering AI value to more people.” More internal stakeholders being exposed to a company’s AI initiatives will eventually bleed into other areas of business, internally and externally. “In the enterprise, the target for democratization of AI may include customers, business partners, business executives, salespeople, assembly line workers, application developers and IT operations professionals.” With more people realizing the benefits of machine learning in particular, we can expect potential for more AI-related learning, problem-solving, and even jobs.

Cybersecurity will enhance the remote workforce

Last year, many organizations were forced into a decentralized workforce in a matter of days. This unanticipated shift pushed these organizations toward new technology implementations that ensured information security in a very short time. More than ever, safety protections are essential for physical employees as well as remote operations. McKinsey notes that “as employees became comfortable working from home, companies began standardizing procedures for remote work environments and explored technologies to reduce long-term risk.” This year, enterprises will further strengthen their cybersecurity efforts in response to the increased vulnerabilities exposed via use of non-secure networks and devices by the growing size of the virtual workforce.

Ethical and responsible AI gain attention

As machine learning becomes more prevalent in day-to-day business, the conversation around data privacy and ethical uses of AI gains momentum. The topic of AI ethics is no longer a subject of discussion for only major universities or nonprofit organizations. Enterprises are becoming fast aware of the issues pertaining to mass aggregation and analysis of personal and sensitive data. The benefits of unlocking data to make smarter business decisions or reduce errors in operations comes with the added responsibility of protecting data in ways that do not cause reputational, moral, or regulatory harm. Major companies have already had to face backlash for not providing a clear outline of their data collection and processing standards. “Companies need a plan for mitigating risk — how to use data and develop AI products without falling into ethical pitfalls along the way.”

At Entefy, we are bullish about AI and how it will transform the way we work and live. 2021 promises to be an important year in our collective journey toward the intelligent enterprise. Be sure to read our previous blogs on enterprise AI and the “18 important skills” you need to bring it to life. 

For any student of history, economics, or innovation, there are a couple of truly astounding facts. One is the dawn of stone tool use about 3.3 million years ago, deep in our ancestral tree. And that was about it for the next three million plus years. 

Eventually the pace changed and accelerated about ten thousand years ago. First came agriculture, metal work, then towns and cities, and then coffee shops. The sudden lift during the Enlightenment Era, not more than 250 years ago, is the second astounding fact. Out of nowhere, people unlocked unprecedented levels of productivity and human well-being. At its core, the Enlightenment Era was a belief in humankind’s ability to craft a new and better future based on ideas – ideas debated openly, tested scientifically, applied universally, and for all to ultimately benefit.

The story of slow technological change is reflected in slow economic development. For most of our history, we have been stuck in a cycle where a few steps toward plenty has led to overpopulation and starvation. The graph below illustrates the episodic nature of our technological (and economic) leaps forwards.

What changed during the Age of Enlightenment? The world mind changed. Between 1600 and 1800, a new way of thinking about human existence emerged. It began in Europe, but the ideas were universal and soon spread to every continent where they were further adapted and evolved.

What were those ideas? Steven Pinker outlines them in his book, “Enlightenment Now”:

Provoked by challenges to conventional wisdom from science and exploration, mindful of the bloodshed of recent wars of religion, and abetted by the easy movement of ideas and people, the thinkers of the Enlightenment sought a new understanding of the human condition. The era was a cornucopia of ideas, some of them contradictory, but four themes tie them together: reason, science, humanism, and progress.

He then elaborates, identifying the behaviors which enabled and supported reason, science, humanism, and progress:

Among those norms are free speech, nonviolence, cooperation, cosmopolitanism, human rights, and an acknowledgment of human fallibility, and among the institutions are science, education, media, democratic government, international organizations, and markets. Not coincidentally, these were the major brainchildren of the Enlightenment.

Several of the clearest examples of Enlightenment thinking and behavior emerged in the coffeehouses of London in the 17th and 18th centuries. London was a global trading center during that time where people from many regions, from many classes, from many belief systems, connected and conversed, building relationships and wisdom in the warmth and welcome of coffee shops. New ideas were tested, new businesses launched, new interpretations of the world discussed among people from many walks of life.

That tradition has continued, although we now exchange ideas well beyond just the coffee shop – in online forums, in conventions, in think tanks, in research institutions, in corporate R&D labs, in papers books, TV, and of course social media. Although the Age of Enlightenment also exists in those places, it continues to percolate in the neighborhood coffee shop. Places where people meet as equals, with shared interests, ideas, complaints, suggestions, and daring thoughts. Where a conversation can drift on camaraderie and then turn sharply at an inspired thought. Where laughter is bonding and where thoughtful silences can be comfortable. Where the human person and human relationships are still at the heart of all that is important. 

It is that spirit which inspires Entefy. A spontaneous conversation in a coffee shop led to the launch of a venture. A venture which in turn could only exist on the basis of Age of Enlightenment ideas, norms, and institutions. The idea of advanced technology and smart machines helping all people communicate universally and gain global access to information in order to build their own understanding of the world, create new ideas and innovation, dramatically improving productivity and human well-being. For everyone. 

For it to work, the animating energy of the Age of Enlightenment had to go beyond mere ideas and include the human element. Conversations transform ideas into progress where there is shared respect for open dialog, nonviolence, cooperation, cosmopolitanism, human rights, reciprocity, and certainly an acknowledgment of human fallibility and the need for the grace of forgiveness.

Entefy was birthed in a coffee shop. We hope to cultivate the ethos of the Age of Enlightenment and foster those norms which make progress and improvement a continuing opportunity. 

AI is transforming the way we operate business around the world. Everything from the ads we see online to how we choose shows to watch while sheltering in place is now influenced by AI.

As popular as it has become, getting started with AI within a company can seem like a monumental task, especially while competitors are moving at a quicker pace with their AI initiatives. But AI doesn’t have to be complicated and you don’t need to reinvent the wheel to introduce it to your operations.

The key to getting started is to focus on the business problems you would like to solve, especially the business problems that can most benefit from advanced analysis of data. Look for areas where meaningful human effort is needed to complete routine tasks or make better decisions while large volumes of data sit idle or are underutilized. Here are three areas where AI can help optimize:

1. Decisions and insights

How can we gain data-driven insights to help make better decisions? The type of decisions that can unlock areas of improvement at your organization by:

• Lowering costs
• Improving customer experience and engagement
• Reducing customer churn
• Detecting fraud

It is estimated that only 4% of businesses effectively capture value from their data. This is partly due to the fact that 90% of digital data generated is dark or unstructured. Advanced analysis of both structured and unstructured data can reveal hidden, and often surprising, insights. Companies worldwide are already using such insights for countless applications including those that create better customer experiences, more efficient manufacturing of products and supply chain management, personalized shopping, AI-generated podcasts and entertainment content, and protection against cybersecurity threats.

2. Processes and workflows

How can we leverage the power of process automation to help free up time and resources? Throughout the workday, employees are often engaged in time consuming rote tasks and workflows that could be performed exponentially faster by intelligent machines. These types of tasks are often repetitive, create bottlenecks, and don’t require the human touch. By implementing automation in this area, your team can take back the precious time needed to focus on high value work that grows the business.

In this regard, something even simpler than advanced AI and machine learning, such as robotic process automation (RPA), can be used to send out invoices or process credit card payments. Automating tasks like these not only saves time but can reduce human errors and costs. According to Gartner, the COVID-19 “pandemic and ensuing recession increased interest in RPA for many enterprises” due to increased pressures on businesses to better manage operations and costs.

3. Teamwork and communication

How can we make team collaboration more efficient to raise productivity? Here’s where next-generation communication tools and knowledge management systems play important roles.

These days, information is the new gold. By making it easily accessible, searchable, and sharable, information can help each of us perform better at our jobs. At enterprises, information is typically stored in and retrieved out of complex knowledge management systems that sit at the core of decisionmaking. Employees depend on these systems to access the organization’s knowledge base and improve communication and collaboration across teams.

Powering communication and knowledge management systems with AI can usher in an entirely new level of productivity. For instance, machine learning can be used to map diverse data types housed in disparate storage repositories and enable universal search capabilities across everything from PDFs to spreadsheets, images, text, audio packets, and even videos. Natural language processing (NLP) can be used to summarize documents or help communication tools improve grammar or tone.

AI is also used in detecting sentiment and emotion contained in digital conversations and social media chatter. Companies use sentiment analysis to identify key emotional triggers for a variety of use cases, not only to improve employee relations but also those with customers—de-escalating situations where frustration or threats are observed, understanding brand loyalty, or identifying early signs of happiness or dissatisfaction within teams or customers.

Additional questions to help kick off your AI initiative

After landing on the key areas where AI can add value, consider the following questions:  

• Which use cases can quickly prove value? 
• Is there sufficient managerial or executive support for the intended use cases?
• Are ROI expectations realistic among the stakeholders?
• Are changes required to the compute infrastructure for this purpose? This involves hardware and IT capacity planning.
• Do we have access to the right data for the intended use case? In the exploration phase, considering the 4 Vs of data can help accelerate discovery and unlock hidden value.
• What are the common missteps to avoid in implementing AI?
• Can we find the right skills in-house or via external vendors to make this this a reality? It takes 18 separate skills to bring an AI solution to life from ideation to production level implementation.

Get informed and learn from the experts

AI is a vast and complex field. So, it would be helpful to get started with key AI terms and concepts. You may also enjoy learning how machine learning differs from traditional data analytics.

Of course, if time is at a premium, you can form a partnership with an AI firm. The data in your business tells a story. Finding these stories is what AI professionals do best. And, perhaps more important, they can help you avoid costly mistakes. 

AI is a popular topic these days. It seems as though every day, someone announces a new product or service that has AI at its core. McKinsey reported that, as of 2019, 58% of companies surveyed had implemented AI in at least one business unit within their organization. This represents a 23% increase in AI adoption over prior year.

Across industries, business are beginning to learn how best to leverage AI and machine learning to improve performance and generate positive return on investment (ROI). Think next generation process automation, better customer service, virtual agents, and physical robots that can out run, out muscle, or out navigate any of us.

Outside of the more common use cases, however, there’s a diverse and fascinating world where AI and machine intelligence is being used to enhance both life and business.

6 Creative uses of AI today 

1. Farming with robot bees – AI has been used in agriculture for some time. Until recently, the focus has been on optimizing core farming tasks—watering crops, determining correct time and dosage for pesticide use, and so on. However, with the recent decline in honeybee population threatening crop pollination for nearly a third of our food supply, multiple organizations are working on ways to use robo-bees (bee-sized robots) to help supplement the work bees do. These autonomous swarming drones can be trained to learn and follow pollination paths using AI and GPS. While this doesn’t solve the problem of colony collapse among bees, it can help ensure the future of our food supply.
   
2. Restoring touch and control – The medical field is the perfect playground for useful applications of machine learning, so it’s not surprising that researchers are employing AI to help restore prosthetic hand control for select amputees. For this, “the machine learning algorithm learns what muscular stimuli at the site of amputation correlate to specific hand motions.” There is still much work to be completed in this area, but the initial results show immense promise. 
   
3. Accelerating drug development – With the world in the middle of a global pandemic, there is an immediate need to find ways to speed up drug development. For illustration, a viable vaccine can take more than 10 years to fully develop as researchers and doctors work through the various stages. This includes everything from research and discovery to rigorous testing, regulatory approval of the drug, as well as manufacturing and distribution at scale. AI can help reduce the amount of time it takes by eliminating manual and time-consuming processes that make up the bulk of the 10-year process.
   
4. Producing fresher food – AI andother forms of automation have been a part of the manufacturing process for decades to streamline a number of processes. However, recently manufacturers have also found ways to help ensure food is fresh and crisp. For instance, potato chip manufacturer, Frito Lay, is using lasers and machine learning to test the crispness and crunchiness of their chips without having to touch them. The system fires lasers at the chips and listens to the noise they make. That sound is then correlated into texture that reveals the quality of the product.
   
5. Preventing poaching – Poaching is a problem that threatens wildlife populations around the world. Regardless of whether poachers are capable of reducing populations to critical numbers unless action is taken (the recent plight of the pangolin is a great example of this). To help prevent at-risk species from being wiped off the planet, countries have turned to Protection Assistant for Wildlife Security (PAWS), a predictive AI software. PAWS determines the most effective patrol routes to catch poachers based on data collected in the field. The data collected (evidence of poacher activity such as snares, footprints, and vehicle tracks) is fed into PAWS to “predict potential poaching hotspots.”
   
6. Assisting firefighters – Navigating through burning buildings and fire zones requires firefighters to pay attention to a number of hazards and complex coordination steps that, if not properly managed, can lead to injury or death. To assist firefighters on the ground, NASA developed AUDREY, “the Assistant for Understanding Data through Reasoning, Extraction, and synthesis.” AUDREY is a virtual agent that tracks teams and provides individual updates to each firefighter on the team based on their location. The system also recommends ways to improve collaboration among team members. As the “guardian angel in the cloud,” AUDREY can learn and make predictions about the resources firefighters need to battle fires.

What can you do with AI

When it comes to introducing AI into your business, one of the biggest challenges is figuring out what opportunities are real and present. A great way to kick start an AI project is to look for areas where too much human effort is required to make better decisions or complete routine tasks while large volumes of data (internal or external) sits idle or is underutilized. Bringing advanced data intelligence and automation to processes and workflows can significantly boost productivity and team morale.

Another important lesson with AI implementations is embracing change and not being afraid to think outside of standard applications. You don’t fire lasers at potato chips to measure their crunchiness without a little creativity and sense for adventure.

To brush up on key AI terminology, be sure to read Entefy’s 53 useful terms in the world of artificial intelligence.

When people think of AI, good customer service may not be the first thing that pops to mind. Most people are going to jump to robots that lack empathy and simply respond to queries based on their programming.

But today, AI is more Rosie the Robot. It’s here to help find answers and support customer service and sales teams. AI can provide instant insights that would take a person years or even a lifetime of experience to generate. Businesses that provide their customers with highly personalized experiences can benefit from increased sales and better ROI on marketing spend. Artificial intelligence used in this way can boost revenue by 58% while increasing engagement by 54%.

Here’s how AI is shaping the future of customer service:

Better personalization and better offers

We produce an ocean of data as we surf the Internet. Every time we visit a website, use an app, or interact with a company on social media, we leave behind bits of information that indicate our preferences or buying habits. It can sound a little unsettling to think we’re leaving all that information behind, but, done correctly, that information can be useful in producing highly personalized offers to customers while protecting their privacy.

AI is reaching the point where it no longer just recommends scary movies because you watched a couple of horror flicks a few years ago. It’s capable of analyzing larger, more complex datasets to create intuitive and useful experiences. This matters now more than ever as customer expectations are at an all-time high.

Say an online shopper has been browsing through a specific set of products. If they’ve made prior purchases from a particular merchant, then the merchant can notify the shopper when those products go on sale. Or better yet, predict related needs for that specific shopper before they even come up. In addition, the simple fact that the shopper is online doesn’t exclude offline conditions from making an impact on their purchasing. Advanced AI systems can now take into account external factors such as weather or economic conditions to hyper-personalize the online shopper’s experience.

Organizations are beginning to pay attention to customer sentiments too by analyzing customer support tickets and social media. Here, words really do matter and properly assessing customer experiences can be the difference between brand loyalty and brand fatigue. By anticipating brand fatigue and finding ways to avoid customer churn, businesses can improve profitability. “It is 6-7X more expensive for companies to attract new customers than to keep existing customers.”

The best part of all this is that AI can create these personalized experiences faster and faster. AI dramatically improves the way information is processed and thus an invaluable ally in delivering personalization. With proper implementation, personalization can even manifest in real-time, with a targeted message to the right customer at the right moment.

Localization

It’s a global market. Businesses are no longer stuck selling to people who live nearby. As a result, being able to provide a local experience on a global scale is something that matters to customers.

Providing customers with an experience that reflects their reality gives them a feeling of inclusivity. With localization, it’s sometimes the small things that make the difference. For example, AI and machine learning can help companies move beyond basic language translation to include actual localized content from a variety of sources.

Although true localization relies heavily on human-centric effort, AI can help deliver data-driven recommendations that can highlight differences in local behavior and culture. Everything from language patterns to naming conventions to local holidays can impact the customer’s experience with a company. Providing that localized experience can make customers feel right at home, regardless of where home is.

Provide answers faster

Eventually, everybody has questions when they engage with a business. It could be about a specific product feature or how something should be repaired. Regardless of the question, no one likes waiting around for an answer. Not at a physical location and definitely not online.

It wasn’t that long ago that emails and Polaroids felt instant. These days, instant has a whole new meaning when mere seconds can mean the difference between a happy customer and a negative review. Some things can still take time, but when it comes to customer service, customers are 7 times more likely to buy from a company that gets back to them within an hour. What is surprising is that 24% of companies take longer than 24 hours to respond and 23% never respond at all. Chatbots can help fill that gap by providing automated responses as soon as a question has been asked.

Successful implementation of AI-powered technologies such as chatbots can help reduce the customer query response time even further by providing answers in real-time, around the clock. Faster answers lead to happier customers.

Getting the best of both worlds

We’re just starting to experience how AI is changing the way we engage with our favorite brands, online and offline. Businesses across industries, from retailers to banks, airlines, and entertainment companies have all begun investing in AI technologies to improve customer service. When it comes to customer experience, the true promise of AI is not to replace the human element but rather augment it with better insights and recommendations in less time.

For a closer look at how companies are using AI for the many aspects of customer engagement, be sure to read our previous blog, “AI and the 5-star customer experience.”

Entefy expands its IP portfolio with a set of newly awarded patents by the USPTO 

PALO ALTO, Calif. May 31, 2020. Entefy Inc. continues to expand its intellectual property portfolio with new trade secrets and newly issued patents by the U.S. Patent and Trademark Office (USPTO). Entefy’s patents represent a range of novel software and intelligent systems that serve to strengthen the company’s core technology, protect its business, and better serve its customers.

“We recognize the value and need for innovation, especially as current economic, social, and health crises are ushering in a new normal,” said Entefy’s CEO, Alston Ghafourifar. “As a company and as a team, we’ve been focused on the type of smart technologies that can power our society at the complex intersection of people, data, and processes. Particularly the type of technologies essential to the remote workforce.”

Expanding on Entefy’s universal communication and collaboration technology, Patent No. 10,587,553 and Patent No. 10,606,871 offer improved methods to simultaneously manage conversations across multiple channels or formats. This set of Entefy capabilities is designed to utilize robust, multimodal machine intelligence to analyze conversations, communication patterns, and individual/group behavior in order to increase worker productivity, streamline knowledge management, and reduce “inbox overload.” For businesses, this technology can also provide managers with unparalleled insights and recommendations regarding organizational dynamics and productivity.

Patent No. 10,587,585 describes the “system and method of presenting dynamically-rendered content in structured documents” and Patent No. 10,606,870 describes the “system and method of dynamic, encrypted searching.” These patents contribute to Entefy’s overarching work in AI-powered search and knowledge management technologies that preserve data privacy while sharing assets.

Entefy was also awarded Patent No. 10,491,690, which describes the “distributed natural language message interpretation engine.” This engine offers specific technical advancements, including Entefy’s AI-powered Message Understanding Service, which can improve performance of natural language-based systems such as digital personal assistants, chatbots, or other conversational AI services.

Entefy has developed an exclusive set of intellectual property assets spanning a series of domains from digital communication to artificial intelligence (AI), dynamic encryption, enterprise search, and others. “As a company, we invest heavily in R&D to create new technologies that can address high value business and consumer needs,” said Ghafourifar. Today’s update is the latest in a series of patent announcements, including earlier Entefy patents that cover the Company’s universal interaction platform, intelligent search capabilities, and APC technology.

ABOUT ENTEFY

Entefy is an advanced AI and process automation company, introducing the world’s 1st end-to-end, multisensory AI software platform. Businesses use Entefy to optimize operations for every corner of their organization—from knowledge management to communication, search, process automation, cybersecurity, data privacy, IP protection, customer analytics, forecasting, and much more.

Entefy’s integrated intelligence platform encapsulates advanced capabilities in machine cognition, computer vision, natural language processing, audio analysis, and other data intelligence. Get started at www.entefy.com.

“COVID-19 is a new disease and we are still learning how it spreads…” At time of this writing, this is the message you’ll find when visiting the CDC (Centers for Disease Control and Prevention) website looking for information on how this novel coronavirus can spread.

What’s been so worrisome about COVID-19, the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is its accelerating rate of transmission. What emerged in Wuhan, China only 3 months ago, has rapidly infected people in nearly every country. According to the World Health Organization (WHO), “It took 67 days for 100,000 cases to be reported, but just 3 days to go from 400,000 to 500,000 cases.” This, despite unprecedented efforts by many countries large and small trying to contain this disease. And as the world finds itself underprepared in dealing with this type of crisis, countless battalions of experts in varying disciplines are contributing to containment and recovery efforts. One such set of experts includes data scientists, software engineers, and automation experts who are unleashing information and technology as our allies in this emergency.

Monitoring and Forecasting

Machine learning is already at work 24/7, assisting with improved tracking of COVID-19 data as well as predicting its spread on domestic and international scale. The breadth and depth of data produced on this pandemic makes it unfeasible for humans to review and analyze. This includes information from global news sources, health organizations, research teams, governments, the travel industry, as well as manufacturing and logistics data.

AI algorithms are being used by a number of experts to examine this mountainous, diverse set of data in order to better identify relevant information and pinpoint valuable correlations that exist between certain data points. For example, how to mitigate transmission risks, how the spread of the disease and its associate mortality rate maps from one area to another during a particular time interval, or how to forecast the efficacy of certain public health practices.

These findings have grown exponentially over recent months and have become the basis for a growing library of research papers now released as part of CORD-19 (the COVID-19 Open Research Dataset)—“the most extensive collection of scientific literature related to the ongoing pandemic.” CORD-19 came together as a result of a global partnership among leading research groups and the dataset is being offered as a free, open resource to researchers everywhere who can benefit from the currently 45,000 plus scholarly articles pertaining to the coronavirus family including COVID-19.

Both traditional data analytics and machine learning can prove essential in analyzing this rapidly expanding sea of data. While traditional data analytics is useful in descriptive ways, explaining current or historical events, machine learning shines in its endless predictive capabilities, learning from different types of structured and unstructured data. Good examples of unstructured data include news articles, images and videos, research reports, or communication threads between any number of people or groups. For this pandemic, AI learning systems can rapidly comb through and analyze massive amounts of data from hundreds of thousands of sources to expose pertinent patterns, correlations, and recommendations.

Diagnosis and Treatment

Modeling the spread of the virus is important and can help save lives by ensuring preparedness, optimized resource allocation, and efficient delivery of care. However, without rapid improvements in diagnosis and treatment, our collective abilities to contain the transmission and treat the virus will continue to be compromised. This is another area where machine learning can be of incredible value. And, in recognition, the U.S. Government has announced the COVID-19 High Performance Computing Consortium to provide researchers access to world-class supercomputers for advanced data science and artificial intelligence modeling.

AI has also silently emerged as a transformative technology for the healthcare industry, enabling incredible efficiencies in diagnosis, drug discovery, and drug development. In some cases, the medical community has already seen the benefits of AI and big data for managing the coronavirus outbreak. WHO and China teamed up for a joint mission, headed by Dr. Bruce Aylward of WHO and Dr. Wannian Liang of the People’s Republic of China, to understand this novel disease and inform next steps for readiness and preparedness of the rest of the global community. The 40-page report released last month describes how these new technologies were implemented “to strengthen contact tracing and the management of priority populations.” As the virus continues to spread, more data is being made available each day, broadening the scope of what can be accomplished with AI technologies.  

Other use cases include computer vision technology used on cameras in airports, railway stations, and other public areas to detect and flag individuals with fever. With this technology, a task which would otherwise require an army of people to administer can now be safely accomplished via machines at a rate of 300 people per minute. Computer vision can also help interpret CT scans and detect coronavirus in as little as 20 seconds versus the estimated 5-15 minutes it would take a human doctor to diagnose. Relying on humans to review and interpret millions of CT images per day is impracticable at best. With computer vision, machines can process those same millions of CT images at lightning speed and with accuracy on par with that of human doctors. 

Diagnosis is only part of the COVID-19 journey. As the world is currently experiencing in China, Italy, and the United States, healthcare systems are struggling to meet needs for treatment and patient care. Current estimates for availability of a COVID-19 vaccine are as high as 18 months or longer. That doesn’t count the time needed to manufacture and distribute the vaccine at the potential scale required. Even in ordinary times when the world is not facing a global pandemic, development of a single drug or vaccine requires incredible effort, resources, experimentation, testing, and time.

AI and machine learning have already proven successful at accelerating drug discovery by enabling massively more efficient chemical compound analysis, outcome estimation, and drug interaction modeling. These are tasks which can traditionally take billions of dollars and years of effort from armies of scientists before leading to positive results. AI can cut this time and cost significantly, allowing faster migration from discovery to development and ultimately release. Drug development focuses on transforming compounds into products that are safe for consumption, something for which machine learning technologies can be used to improve analysis and drug production yields. Today, the difference between efficiently producing a compound that works and one that doesn’t can mean the difference between making things better or much worse.

Manufacturing and Logistics 

AI has already proven transformative in manufacturing, logistics, delivery infrastructure and other aspects of supply chains. These are critical pillars in the global response to COVID-19 encompassing everything from personal protective equipment (PPE) to life-saving ventilators to everyday household supplies and food items. As demand for supplies and equipment continues to increase the world-over, optimizing these important pillars becomes more important than ever.  The modern supply chain is a vast network of producers, vendors, retailers, distributors, warehouses, and transportation companies connected to create and deliver goods to end customers. This network is complex and rich in data generated by people and the many smart sensors and devices along the entire chain. However, the entities participating in this process are mostly unprepared to fully harness the true power of predictive analytics, real-time insights, and intelligent automation needed to optimize costs, units, and operations. In fact, “94% of the Fortune 1000 are seeing coronavirus supply chain disruptions.” The current pandemic is accelerating work in a number of areas including advanced robotics for delivery and sterilization, as well as machine learning for demand forecasting, risk assessment, sourcing, cost, inventory, and logistics optimization. Delivery networks are also being stretched to the limit with numerous efforts in place to use machine learning to balance load and predict demand while also exploring new AI-powered machine delivery methods such as drone delivery where computer vision plays an important role. 

News and Education

At the Munich Security Conference in February, WHO Director-General Tedros Adhanom Ghebreyesus stated that “We’re not just fighting an epidemic; we’re fighting an infodemic.” Throughout news and social media, citizens are inundated with reports, tips, stats, and more, much of which is unclear, conflicting, and sometimes even inaccurate. This means that important messages can be lost in the noise and misinformation can permeate the knowledge sphere. This is another area where AI can prove valuable.

Similarly to how computer vision systems can rapidly scan images and video feeds at a scale unfeasible by humans, natural language processing (NLP) can be unleashed on the world’s news outlets and social media feeds to synthesize the sheer volume of information, remove redundancies, filter out old news, flag misinformation, and prioritize new or unique content. Misinformation and “fake” news can spread faster than any person can keep up, but AI systems with robust and diverse NLP capabilities can scale as far and wide as needed when powered by the right computing infrastructure.

Conclusion 

This novel coronavirus has inspired significant global collaboration with people around the world working day and night to contain, manage, support, and treat those negatively impacted. Over the past several weeks, it has become clear that the need for answers and solutions is growing, and innovation is vital in order to accelerate recovery. Ultimately, the role of machine intelligence is to save time and create efficiency, something which may be more important now than ever before.

Machine learning is a powerful weapon in the arsenal of defense as it can help monitor and forecast the spread of the virus plus provide faster, more precise diagnostics and treatments. But it can also optimize manufacturing and distribution of goods and help in educating the public about the disease and our individual responsibilities in the context of COVID-19’s broader impact on our economy, healthcare system, businesses, and society at large. Significant resources are being poured into solutions which can help healthcare professionals and others rise to this unique challenge. This may be just the beginning, yet we’re already seeing examples where machine learning is helping us outsmart the coronavirus.

Artificial intelligence is the umbrella term for computer systems that can interpret, analyze, and learn from data in ways similar to human cognition. The field of AI is vast, encapsulating numerous subfields and applications related to machine intelligence. With AI, computers can perform a wide range of tasks—from playing chess to diagnosing cancer and virtually everything in between. 

The term artificial intelligence was first introduced by American computer scientist John McCarthy in 1956 at a summer conference at Dartmouth College in New Hampshire. That conference is believed by many to have launched AI as a genuine field of research. In the ensuing decades, a number of inventions, discoveries, and experiments have led to the many ways AI turns data into insights, powering our society and influencing how we use computers every day.

With AI and machine learning, computers are programmed or “trained” to perform intelligent tasks. Tasks that are either “narrow” or “general.” Artificial narrow intelligence or weak AI pertains to specific, pre-defined tasks such as predicting the weather, recommending your favorite music, or even autonomous driving. Narrow AI can on its own transform the way we treat a particular process or task. Most of what we see today in terms of machine intelligence falls within this category and shouldn’t be taken for granted. Narrow AI is capable of analyzing massive volumes of data, thousands of times faster than people and typically with fewer errors. Narrow AI also relieves us of mundane tasks so that we can be more efficient with our time.   

Artificial general intelligence (AGI) or strong AI is related to more complex functionality that is expected to match human level capabilities across multiple domains. Think about the very advanced AI systems you see in sci-fi movies where the interactions between people and machines are seamless and feel conscious. An AGI system can draw valuable insights from diverse data sets (e.g. images, text, audio files, logs) and use cognitive computing to perform functions that are indistinguishable from those performed by a human.

As described in one of our prior blogs, traditional data analytics and machine learning differ in several key ways, including structure, purpose, and benefits. Without diving into too many details, in short, traditional data analysis is descriptive and quite useful in explaining current or historical data while machine learning is predictive and capable of learning from data in ways that provide valuable insights and recommendations.

AI/machine learning is a dynamic process, often requiring algorithmic model training, validation, testing, refinement, and integration with other software components to create real value. Unlike many other engineering functions such as traditional software engineering, where you can create a solution based on certain known requirements, quality machine learning requires deep model and data exploration to arrive at something useful. Simply put, experimentation and embracing the unknown is par for the course in advanced AI.

Building models and proper orchestration are also core to success here. Added complexity sets in when the intended use case is multimodal and the data requires multimodal AI processing, creative ensembling of multiple models, and intricate queuing and software orchestration. This is where combinatorial expertise in machine learning, compute infrastructure, and software engineering is needed but currently in rare supply.      

Then there are the 4 Vs of data which are important criteria for success in advanced AI initiatives. The 4 Vs include data volume, variety, velocity, and veracity. The road from data to insights can be patchy and long, requiring many types of expertise. Dealing with the 4 Vs early in the exploration process can help accelerate discovery and unlock otherwise hidden value.     

It is also important to note that high accuracy and precision in artificial intelligence is the byproduct of rigorous scientific, engineering, and design efforts. This is where advance science meets art to deliver results. And the journey from ideation to implementation for even a single AI application requires cooperation with other contributors including those fluent in business, operations, legal, and cybersecurity—18 skills in all. 

For a quick refresher on key AI terminology be sure to read the 53 useful terms in the world of artificial intelligence. 

If you’ve been paying attention to retail spending over the past few years, it won’t surprise you to learn that e-commerce in United States continues to gain traction at record speed. In fact, e-commerce is expected to “surpass 10% of total US retail sales for the first time in history.” By 2023, online spending by U.S. consumers is expected to grow to $970 billion (a 65% increase to this year’s volume) and global retail e-commerce sales are expected to balloon to $6.5 trillion in that same year. There are several key factors that contribute to this growth and consumers’ attraction to e-commerce. These factors include convenience of 24/7/365 accessibility, nearly limitless product selection, quick price comparisons, fast checkouts using one-touch purchase options, real-time updates of new product launches, exclusive promotions, same-day delivery, as well as enhanced personalization and customer service using artificial intelligence.

These days, retailers and e-tailers have access to tremendous amounts of data about their customers, competition, and the market at large. But, this collection of data isn’t easy to manage, growing in volume and complexity daily. Therefore, for online and brick-and-mortar merchants the challenge remains connecting and making sense of all of that data, making it actionable for either revenue growth or cost reduction. And that’s where machine learning steps in to power the future.

With AI and machine learning, companies can turn idle data into valuable insights. For example, using AI can automatically categorize products, make better recommendations, dynamically adjust pricing based on customer behavior and inventory levels, provide virtual assistance to support customer queries or concerns, and optimize supply chains like never before. Given the broad applicability of AI and its promise to level the playing field in an increasingly competitive industry, retailers worldwide are predicted to spend $7.3 billion on AI by 2022, up from $2 billion in 2018.

Depending on the data type, specific machine learning methods and models are used to get to the intended outcome. For instance, computer vision, a subfield of AI, is used to classify and contextualize the content of digital images and videos. Computer vision gives companies the ability to use machines to detect and label objects in images without having their personnel do the same. Companies can also unclutter their listings or filter out offensive images in this way. This is the same technology that gives consumers the ability to find their favorite products (or something similar) by simply using a picture of the item.

Other uses for computer vision include facial recognition, sentiment analysis, and logo detection. Merchants can use facial recognition and sentiment analysis to recognize repeat customers, personalize the customer experience, and in some cases, provide better security by identifying and monitoring high-risk individuals. Logo detection by machines are used to support marketing, identify counterfeits, and protect brands against pervasive infringement. Take things to the next level and you’ll get to fully automated, cashless stores where consumers can simply walk into a location, grab their favorite merchandise off the shelf, and walk out with those items without ever having to stop at the cashier to scan any item or pull out a credit card.

Natural language processing (NLP) is a subfield of AI focused on processing and analyzing natural human language or text data. Here, finding the right product becomes much easier because NLP can interpret the customer’s intent and the shopping context much better than traditional search systems that rely solely on exact “keyword” matching. This includes better performance even when the user requests involve typos or poor grammar. NLP can also help drastically improve customer service, both in terms of leveraging sentiment analysis capabilities to better support customer needs and using conversational chatbots to streamline the call center experience. Imagine, smarter systems where clunky “press 1,” “press 2” prompts are a way of the past, replaced by NLP-powered machines that can seamlessly answer questions and carry on natural conversations.

While AI is improving your shopping experience, it is also being employed to simplify the less visible aspects of the supply chain which are responsible for production and distribution of the products you can buy. Ultimately, better supply chain management means less waste, faster production cycles, and lower costs. Historically, data analysis in these areas has been performed using traditional data analytics but this is fast changing due the explosion of data volume and complexity. Companies are turning to the “predictive” power of advanced machine learning to optimize everything from manufacturing to warehousing to transportation and logistics. For example, studies indicate “that unplanned downtime costs manufacturers an estimated $50 billion annually, and that asset failure is the cause of 42 percent of this unplanned downtime.” Predictive maintenance powered by AI is now delivering the required ROI by reducing unplanned downtime and improving asset efficiency.

As U.S. and global consumer demand for retail products continues to rise, it’s clear that the world’s reliance on advance AI and machine learning capabilities will continue to rise in lockstep. These new capabilities present new opportunities for retailers and e-tailers to deliver more personalized customer experience at greater scale than ever before.

For a quick refresher on key AI terminology, refer to our previous article defining 53 useful terms in the world of artificial intelligence.