The PDF brief highlights five unique AI trends impacting enterprise leaders in 2021 - including the prominence of AI platforms, the importance of anomaly detection, and more.

Learn the 7 steps to developing an AI strategy - with hard-won lessons learned from our interviews with AI leaders, and Emerj's hands-on enterprise AI strategy work over the last three years. While there is no single AI strategy process that applies to all enterprise firms or all situations, there are common factors involved in AI strategy (such as project prioritization, assessment of AI maturity), and this outline provides a common order of phases for a given AI process.

A succinct summary of AI use-cases for pandemic response, covering: Drug discovery, pandemic prediction, and diagnostic technologies. Each short section includes a breakdown of the AI capability, and an example vendor. Included at the end of the PDF is a short list of podcast interviews relating to the themes from this white paper.

Emerj AI Cheat Sheets serve as important executive guides to AI applications and trends in critical industry areas. The purpose of this AI in Banking Executive Cheat Sheet is to help banking professionals quickly grasp key concepts and apply this knowledge to their career immediately.

Emerj AI Cheat Sheets serve as important executive guides to AI applications and trends in critical industry areas. The purpose of this AI in Retail Executive Cheat Sheet is to help retail professionals quickly grasp key concepts and apply this knowledge to their career immediately.

Emerj AI Cheat Sheets serve as important executive guides to AI applications and trends in critical industry areas. The purpose of this AI in Insurance Executive Cheat Sheet is to help insurance professionals quickly grasp key concepts and apply this knowledge to their career immediately.

Leaders at financial institutions can use the the AI in Financial Services Cheat Sheet to quickly get up to speed on critical AI use-cases driving revenue, cost savings, and risk reduction in their industries.

Getting started with AI means picking the right technology providers and partners – but with so much hype and noise in the market, it is extremely challenging to find vendors who can actually get the job done. We’ve created this short guide to help leaders and consultants avoid mistakes and find the right partners quickly.

At Emerj.com, we work with some of the world’s best-known AI brands to help them generate leads and build pipeline. Over the course of hundreds of B2B campaigns for our clients, we’ve developed a complete blueprint for attracting and qualifying executive leads who are looking to apply AI.

Most “how-to” guides to AI are written for engineers and developers. At Emerj, our work focuses on simple explanations and actionable insights for nontechnical businesspeople. This guide is designed to help business people understand the steps and phases of getting started with AI in any business or sector.

Natural language processing (NLP) will be one of the most prevalent AI approaches in business in the decade ahead. A fundamental understanding of what NLP can, and cannot do, can be a help for literally any nontechnical professional who wants to spot AI opportunities in their own role or industry. We’ve created this guide to give business leaders that precise edge.

Here at Emerj, most of our AI Capability Map services work about finding trends in quantitative data – which requires hundreds of hours of expert research, and established frameworks for interpreting and categorizing data for insight. While this robust research approach is often the right step for companies who are on their way to building a complete AI strategy – it’s easiest to begin with simple secondary research online. In this PDF, we reveal some of our preferred methodologies for hunting down AI opportunities and trends quickly.

This report was created as a direct response to our subscribers and Emerj Plus members who are concerned about the resiliency of their global supply chains. From transportation to inventory management and beyond - discover critical AI trends impacting the future of logistics and supply chain in this PDF white paper.

The USA is advancing its formal plans to maintain a global leadership position in AI. In 2019 the Trump administration founded the American AI Initiative and supported its initial investment with additional funding commitments. This guide has been written as a pragmatic, insight-dense report on the US AI Initiative, focused on actionable insights and opportunities for organization leaders.

With our finger on the pulse of academia, Fortune 500 leadership, and the global artificial intelligence startup ecosystem, organizations regularly call upon Emerj's research capabilities for insight into their most important strategic decisions related to AI. This guide is intended to help internal innovation leaders or outside AI consultants determine the right ROI measures for an AI project, predict or forecast ROI with as much accuracy as one could reasonably expect, and convey that ROI case to leadership.

The COVID-19 pandemic has been a massively disruptive force across industries, leaving many isolated in their homes and grinding some sectors to a halt while begging many questions: What AI applications will be most important for businesses to survive the COVID crisis? How are some companies using AI to pull ahead of the competition during the pandemic and its economic fallout? How do leaders need to reconsider AI investments in the face of the pandemic? This guide provides representative use cases and strategic considerations for leaders who are rebuilding their technology and business strategy to fit a successful AI project in the post-pandemic environment.

Artificial intelligence often requires data and data scientists in order to deliver value, but what most leave out is the fact that it also requires a process. In this insightful guide from Emerj CEO Daniel Faggella, IT and data science leaders are offered a definitive roadmap for successfully deploying early AI projects in enterprise environments. How to focus teams with various skills on what is most important and what questions are the right to ask at the right stage of the process are answered in-depth in this handy guide.

Artificial intelligence often requires data and data scientists in order to deliver value, but what most leave out is the fact that it also requires a process. In this insightful guide from Emerj CEO Daniel Faggella, business leaders are offered a definitive roadmap for deploying early AI projects in enterprise environments. How to focus teams with various skills on what is most important and what questions are the right to ask at the right stage of the process are answered in-depth in this handy guide.

Artificial intelligence adoption is challenging. When we see our clients applying AI in an existing business, whether for AI strategy development or vendor selection, we start looking at the beginning of the process. While there is no guarantee for AI ROI, most of the wasted money on failed AI projects can be traced back to misunderstanding AI's true capabilities and potential long-term role in their organization's mission. This report is intended to prepare leaders and teams to adopt AI successfully and to drive company strategy forward through these challenges and more.

Developing an AI product is difficult. Not only does one have to struggle with the issues of integrating and using AI, but you have to do so in a way that satisfies customers. Working with AI in business comes with its own challenges, such as building an accessible data stack and a reliable data science team. At Emerj, we’ve worked with dozens of AI startups and established companies working on AI projects. We have interviewed hundreds of AI companies about what they learned during their time in development. In this guide, we break down the critical frameworks that we use to help companies develop AI products that succeed in the market. You can reliably use these frameworks to develop a winning long-term marketing strategy along with near-term development goals to help you get there.

Artificial intelligence is transforming industries, and it is providing a competitive advantage to early adopters. But the headlines hide the fact that success isn’t the norm. While some companies are adopting artificial intelligence to great advantage - most companies are spinning their wheels with fruitless AI projects that go nowhere. This compact 25-page report is broken down into five key chapters: 1. Critical Executive Expectations in AI Adoption - Overcome the early hurdles to AI adoption by understanding how to assess costs and resources, and grounding your project in the right expectations of an AI project’s requirements. 2. Short-Term and Long-Term AI Investment - How to lock in the near-term benefits of AI by positioning your firm or department to win the long term, and handle unrealistic ROI-based objections or unrealistic ROI expectations. 3. AI Adoption Motives - Discover the three reasons for AI adoption that lead to successful outcomes and more likely ROI, and the two mistaken AI adoption motives that almost inevitably lead to wasted money, setbacks, and embarrassment. 4. Timing and Purchasing of AI - Learn when to adopt artificial intelligence, what to do directly before beginning an AI initiative, and a simple, powerful framework for build vs buy decision-making. 5. The Three Phases of AI Adoption - Discover the crucial importance of a post-pilot “incubation” phase for AI projects, and critical insights to turn a successful AI proof of concept (PoC) into a truly functional business solution - without making the costly errors that stop many PoCs cold.

The biggest problem facing insurance firms today is a one-word answer: fraud. Yet despite widespread data showing in detail the massive problem that is insurance fraud, many remain perhaps willingly ignorant as to the ubiquitousness of the problem. Insurance carriers don’t want to pay millions in fraudulent claims, but not at the risk of slowing down and upsetting already-on-edge claimants. In this white paper, we will take a closer look at two use cases from Shift Technology that demonstrate the promise of AI in the insurance space: detecting network fraud and integrating external data sources into fraud detection workflows.

Five years ago, almost all enterprise firms treated AI initiatives like IT projects, focused on near-term ROI without understanding the challenges of working with data or maintaining a deployed AI system. Today, many more enterprise leaders take AI maturity into account throughout their decision-making processes and see AI as a capability to build, not just a supporting factor in winning market strategy. In this service operations-focused white paper, we work with experts to develop a roadmap approach that industry enterprises can adopt to bring about the internal shifts necessary to bring AI to life at their organizations.

Technology and innovation — and all the wonders and disruptions they bring — surround us every day. Service operations leaders navigate digital and physical worlds increasingly filled with the solutions that will help them plot their course into an AI future. Smart, connected products have the potential to provide significant market advantages, but new possibilities require new strategies from leadership. A new report, co-created by Emerj and PTC, sets out a roadmap approach that industry enterprises can adopt to bring about the transformation that has to happen within organizations in order to bring AI to life.

Companies invest in something if they think it generates value, and AI development is no different in this regard. Myriad sources tell us that AI adoption is becoming more ubiquitous, which would seem to indicate that the people running these companies understand the business value and ROI. This new report, co-created by Emerj and Clickworker, will examine two critical elements of good data — quality and diversity — via two of Clickworker’s typical use cases: facial recognition and voice recognition.

Increasingly, AI is descending deeper and deeper into the bedrock of today’s digital products. The days have passed where AI lingered near the surface of a digital product’s vast pool of capabilities and innovations. This new report, co-created by Emerj and NLP Logix, explores the growth many companies experience as they mature in their approaches and applications of AI in their digital products, as well as use-cases that will serve as examples to organizations as they chart their own AI journeys.

Healthcare and Life Sciences are in a state of flux. With a wave of new investment in light of COVID-19, and burgeoning AI capabilities, many core workflows and processes are ripe for change. This new PDF report from Emerj and SambaNova Systems highlights both trends and use cases that are transforming Healthcare and life sciences.

Digitally aware companies are adopting artificial intelligence, but most firms are doing so without a plan or strategy. One of the first decisions they will make determines how they will acquire and stand up their solution. This new PDF report, co-created by Emerj and SambaNova Systems, explores the different AI adoption pathways that enterprise leaders can take — and provides actionable guidance to make adoption easier and faster.

How much paper-based mail does your organization receive every month? Do the inefficiencies of processing and distributing this mail to your employees challenge your compliance, privacy, and information security requirements? Has COVID complicated your paper-based mail processes even more? In this white paper, Emerj Research professionals take a closer look at Iron Mountain's metadata management use case in their Digital Mailroom service.

The manufacturing industry has long relied on telemetry data to measure, evaluate, and draw conclusions about the way they produce goods. AI and machine learning algorithms promise to introduce profound changes to what we can do with data collected from a large number of real-time telemetry sensors, video streams, and audio streams. This new PDF report, co-created by Emerj and MayaHTT, explores the different ways AI leverages the new capabilities in computer vision and deep learning, to take full advantage of rising digital technologies in continuous manufacturing processes.

Increasingly, technology and business leaders look to project managers to make the execution — and success — of their AI projects more predictable. Executives and decision-makers want AI projects to mature so they are more like the software development projects that have been with us for a generation. Yet any AI project manager hoping to deliver on those expectations knows that success in AI projects requires end-to-end thinking rarely found in leadership today. In turn, educating executives that there is no magical black box with AI becomes mission-critical so that the development and execution of the project can satisfy the right expectations. A new guide from Daitan and Emerj Research walks enterprise executives through these rigorous and demanding challenges of data iteration and data cleansing in ways that lead to successful project outcomes.

Until now, reporting has required hours of manual effort across finance departments and companies. Deloitte's Baromètre Digital Finance 2021 report identified the automation of reporting as a crucial solution in solving three of these principal challenges facing finance and accounting groups in the next 12-18 months. To confront these challenges thrust upon today's business leaders, corporate finance must accelerate its reporting, analysis, and decision-making processes, identify root causes in real time, and share those insights as they develop. The COVID-19 pandemic has only accelerated these trends and the need for new, faster, and better technologies that will get and keep you ahead of the competition. Download this guide now and find out how enterprise organizations have automated their financial reports and how Finance managers are measuring their ROI so that you, too, can accelerate your organization.

Pharma walks a hard road getting life-saving drugs to market. That’s why speed becomes so crucial in the new drug application approval process, where companies face many challenges, such as the race to market, competition and maintaining compliance with stringent internal and external regulatory requirements. The importance of speeding the drug application process became obvious during the COVID-19 pandemic as daily case counts and deaths surged worldwide. How does pharma respond to that challenge effectively and ethically? A new report from Yseop and Emerj Research shows how natural language generation technologies bring automated intelligence to the clinical study reporting process. With widespread integration, pharmaceutical companies can get drugs to regulators and the market faster. This helps both those who need the drugs and those who need the ROI to continue producing them.

Traditional data mining tools leverage keywords to extract information and are incapable of reading text or understanding language like humans. In the context of the deep scientific content of drug discovery, keywords can miss critical information just because it fails to line up with a user query. Whether the focus is on drug discovery, the design of clinical trials, or the tracking of adverse effects in the drug safety process, AI stands ready as a worthwhile partner to the industry’s subject matter experts. This new report offers direct insight into how AI technologies are helping life sciences companies scale key business processes, overcome operational challenges, and realize new opportunities.