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How To Drive Successful AI Adoption At Your Enterprise

In this playbook, we’ll walk you through how AI and machine learning are fundamentally changing enterprises. We’ll define the promise of these technologies and explain how organizations can solve business challenges that were previously unsolvable. We will also share the lessons we learned along the way in helping organizations build and scale AI capabilities across the enterprise.

How To Drive Successful AI Adoption At Your Enterprise

Content Summary

Where do you see your business in five years?
The Big Idea
Is this a big deal for my business?
Real-world results: AI case studies and success stories
Where do I start?
What capabilities do I need for AI success?
How do I get going? The four key stages of the AI journey
Let’s put AI to work for you!

Where do you see your business in five years?

Artificial intelligence (AI) has been a topic of great interest in recent years, with many arguing that it has the potential to revolutionise industries in the same way that the Internet has over the past three decades. PwC estimates that AI will drive GDP growth of $15.7 trillion by 2030 through productivity gains such as automated or autonomous processes and hyper-personalized products and services.

According to a recent MIT Sloan Management Review study, 91 percent of respondents expect to derive new business value from AI implementations over the next five years. The study found that even 81 percent of passive adopters — businesses that currently experience indirect benefits from AI-based products — expect to attain direct value from AI in the next few years.

Given the above, business leaders are asking: What impact will AI have on my organization, and does AI have the potential to disrupt or transform my business?

In the business world, the question is no longer whether you should adopt AI technologies. Instead, it’s a question of when and how. Key decision makers are contemplating where to invest in AI and which capabilities they will need in order to transform their businesses. Above all, they are exploring how to build AI in an unbiased, responsible and transparent way in order to maintain the trust of customers and stakeholders.

As an award-winning company that has built transformative AI solutions for a variety of industries, we have spent several years helping organizations tackle these key concerns. As a result, we’ve been fortunate enough to address many of the challenges surrounding AI, and we’ve witnessed and helped shape the evolution of AI applications first-hand. Now, we’re ready to share key insights and best practices that will help your organization pursue its own AI initiatives with success.

91% of business executives expect to derive new business value from AI implementations over the next five years. Source: MIT Sloan Management Review

The Big Idea

Why AI?

AI can solve cognitive problems and automate complex processes that have traditionally required considerable human input.

AI represents a revolutionary new paradigm for computing. For the better part of a century, computers have required structured data and discrete instructions written by humans.

AI and ML do things quite differently. They enable computers to learn on their own. Using a combination of structured and unstructured data, AI and ML can decipher our world in surprisingly human ways: through sight, sound, speech and pattern recognition.

Over the past few decades, advances in machine-learning algorithms, ever-increasing data sets and hyper-scale cloud computing have enabled machines to gain considerable cognitive capabilities. For quite some time, AI and ML were the exclusive domain of deep-pocketed enterprises. Now, due to a combination of open-source software, pre-trained APIs and the ability to rent hyperscale cloud computing, AI and ML “superpowers” are accessible to a wider range of businesses and budgets.

AI-Assisted Healthcare

  • Unlocking Deeper Patient Insights: Healthcare providers have millions of records in their EHR systems, representing a hard-to-manage mix of structured and unstructured data. ML models can help make sense of it all, tagging patient interactions, assessing before and after factors for each interaction and predicting the probability of events like unwanted patient readmissions.
  • Medical Image Segmentation: Deep learning and computer vision are helping medical professionals make critical diagnoses more quickly and with greater accuracy. ML models are capable of analyzing many input files at once to identify regions of interest in CT scans, reducing decision-making time from days to seconds.
  • Remote Patient Monitoring: AI can identify patients with a high risk of interventional care, target care-management needs and reduce the cost of care while improving health outcomes.

AI-Assisted Contact Centers

  • Improved Customer and Employee Experience: Through chatbots and virtual agents, AI transforms the experience for customers and contact-center representatives. These virtual agents handle humanlike conversations via phone, web chat, social media and a host of other channels.
  • Seamless Integration: AI solutions integrate with telephony providers to give virtual agents the capacity to seamlessly transfer conversations to live agents when tasks become too complex.
  • Versatility: AI leverages custom solutions for realtime speech analytics, workforce capacity planning, call scripting and compliance monitoring to improve experience while dramatically reducing costs.

What Can AI Do That Wasn’t Possible Before?

In other words, what “superpowers” are we talking about here? AI and ML let you solve problems in ways that weren’t possible with computers until recently.

There are five key traits that give AI and ML applications these unique problem-solving powers.

  • The ability to understand visuals
  • The ability to hear speech and transcribe it into text
  • The ability to understand the semantic meaning of words
  • The ability to anticipate what is likely to happen next based on historical data patterns
  • The ability to optimize large, complex and dynamic systems

While the earliest applications of AI and ML were in diagnostic areas, viable use-cases are growing in complexity as the technologies evolve. Increasingly, AI and ML are being used as assistive mechanisms with humans in the loop to provide dramatic improvements in efficiency. In some rare cases, AI and ML provide autonomous capabilities that can automate processes end-to-end.

AI-Assistend Marketing

  • Customer Journey Mapping: Automated trendspotting, propensity modeling and customer segmentation will help you better understand the path to purchase. These solutions provide easy access to customer behavior patterns for more informed decision making.
  • Predicting Marketing Outcomes: Machine learning can forecast the lifetime value of customers and their likelihood of purchasing, identifying behaviors and attributes specific to high-value audiences.
  • Customer Experience: Through sentiment monitoring and data-driven segmentation, AI can provide personalized messaging and content. Clustering algorithms can categorize customer behavior, pinpoint how consumers feel about a certain brand or company and make the entire experience personalized and frictionless.

Is this a big deal for my business?

Where In My Business Can AI Make An Impact?

In working with several clients as an AI software and services provider, we’ve discovered four key areas that are priorities for most companies. Regardless of the industry or vertical, many organizations struggle in making their products smarter, in creating frictionless customer experiences and in automating key processes. At the same time, they are under pressure to uncover and address anomalies, threats and risks that will help make their business safer.

  • Smarter products
  • Frictionless customer experiences
  • Autonomous processes
  • Detecting and managing anomalies and risks

Four Business Functions Where AI Can Make An Impact

Recent advancements in AI and machine learning (ML) have made significant improvements possible in addressing and managing common business priorities. Custom applications can help identify parts of your business that can be optimized. Let’s look at four common business challenges through the lens of what’s now possible with AI and ML:

Smarter Products

Basic computing involves a microprocessor following instructions created by a human being. Where this process breaks down is with cognitive problems: situations in which a human cannot define programming instructions explicitly. Machine learning and AI provide viable mechanisms to address such cognitive problems.

In the media world, computer vision and deep learning help analyze hours of video quickly, identifying content attributes such as actors, objects, actions and brands that drive viewer engagement. In the realm of education, students are receiving custom college recommendations, automated grading of their statements of purpose and essays, and feedback on their video interviews. AI is automating a large portion of the application process, allowing students to apply to several schools simultaneously.

Frictionless Customer Experiences

Activating someone’s interest is only the first step in converting them into a paying customer. Traditionally, finding and retaining high-value customers involves a lot of guesswork. How many times have you had to vet an incoming lead before determining whether it was qualified? Instead of connecting with the ideal customers for your business, you’re wasting time manually sorting through leads that aren’t worth pursuing.

AI can do this initial vetting for you. By applying ML technologies such as natural language processing to text documents, the process of targeting, acquiring and retaining customers becomes much more precise. Most companies we’ve worked with have experienced significant growth in new customer acquisition and retention through targeted application of machine learning. (Source: Quantiphi)

Autonomous Process

Organizations engaged in the supply chain are always looking to do more with less. Before AI, operating efficiencies were possible, but not as deep or quick to define and implement.

By applying ML techniques such as recurrent neural networks for time series forecasting, organizations have seen unprecedented improvement in their ability to forecast demand, capacity and revenue. Additionally, through the application of reinforcement learning, enterprises have seen dramatic improvement in their ability to optimize large, complex and dynamic systems. Now, you can effectively manage and streamline your demand, procurement and production beyond what conventional algorithms have been able to solve.

Detecting And Managing Anomalies And Risks

Regardless of the industry, every enterprise has risks. Examples include business-continuity risks such as denial-of-service attacks and data breaches. Industries such as insurance and lending have their entire business models hinging on effective risk management. What if you could identify potential landmines before they turn into existential threats?

By training AI models to detect anomalies and risks, companies can anticipate what is likely to happen next and put safeguards in place to protect themselves against threats.

Whether you apply a recommendation engine to anticipate your customer’s next purchase or predict downtime of your equipment to perform preventative maintenance, AI is able to bring quantifiable business impact to a large variety of enterprise functions.

Real-world results: AI case studies and success stories

Increasing Customer Satisfaction With AI-Assisted Virtual Assistants

Challenge: Advisors from a large educational-services company were responsible for managing incoming queries regarding new enrollments, student communications and other questions. They manually pulled chat logs to identify prospects and lacked a way to respond at high volumes or outside of normal business hours, resulting in long wait times for students.

Solution: We developed an always-on virtual assistant trained on existing chat logs to address student queries. By automating the query-handling process, overall productivity was improved by reducing manual efforts. Our solution also enhanced the student experience.

Results: By using a virtual assistant to handle all incoming queries, the client was able to reduce resolution time by half while improving the overall user experience.

Quantiphi AI-assisted virtual assistants helped one company reduce its query resolution time by 50 percent.

ML Makes Every Second Count During A Health Emergency

Challenge: A nationwide healthcare system required medical-image segmentation to expedite diagnoses and prevent critical situations for patients requiring interventional care following head trauma. The healthcare system’s previous ML models were trained on a low volume of data due to compute and storage constraints, resulting in lower accuracy levels and high inference times.

Solution: Using state-of-the-art deep-learning architectures, we trained several models on a fully automated training pipeline. We were able to stack several modules, including DICOM classification, and combined skull-stripping models with image segmentation to compute the volume of artifacts on 3D CT scans with a high degree of accuracy.

Results: We built a model that’s capable of handling a variety of input files in a single pipeline and providing accurate estimations of artefacts such as blood clots. This assistive solution helps reduce inference times from days down to seconds — a crucial statistic when it comes to saving lives.

Quantiphi’s machine learning model helped a healthcare system reduce surgery decision-making time from days down to seconds. Source: Quantiphi

Virtual Testing for Improved Automotive Manufacturing

Challenge: A manufacturer of auto-safety systems wanted an automated solution to identify critical features and predict the friction coefficient of materials. The goal was to reduce the number of iterations in designing brake pads.

Solution: We developed an ML model that allows the company to predict critical factors influencing the friction coefficient. We also built an intuitive web interface that enabled chemical formulators to submit input features and get the corresponding friction coefficient of the materials.

Results: Our solution helped the company optimize the formulation of brake pads, achieving the desired value of friction coefficient with an accuracy of more than 80 percent. This virtual-testing solution helped save time and money through an expected reduction in design iterations.

Virtual testing helped a manufacturer of auto-safety systems reduce design iterations to save time and money. Source: Quantiphi

Where do I start?

Finding Impactful AI Use Cases for Your Business

One of AI’s best traits is also its biggest source of confusion. Namely, the potential applications of AI and ML are so versatile that many businesses simply don’t know where to start. What’s more, “AI” and “ML” are all-encompassing terms that don’t refer to any specific application. An AI or ML solution could involve computer vision, speech recognition, natural language processing, statistical pattern recognition or a combination of the above.

That’s especially the case with applied AI applications. The techniques can be customized to each client’s needs when it comes to solving complex problems. As a result, most clients find it difficult to comprehend the spectrum of possibilities and pinpoint the specific use-cases that will create the most-transformative impact to their business.

Identifying the key areas in which AI can make an impact requires one to answer three sets of questions and find convergence points:

Key Targets For AI-Assisted Solutions

Key Targets For AI-Assisted Solutions

In our experience, having a guided workshop that uses this simple yet practical framework will help you apply these questions across your core business functions. The process can yield use-cases that are highly feasible and offer the best odds for success. These working sessions have helped organizations identify key targets for AI-assisted solutions, such as radiology-image segmentation for healthcare organizations, engine-defect detection for automotive companies, virtual assistants for insurance-claims processing and email-routing agents for customer-service functions.

AI: Perception VS. Reality

Most enterprises understand AI’s potential to transform the way they operate, but they may not fully grasp what goes into deploying a solution.

The perception: The most common misconception is that AI is “automagical.” The results can certainly seem that way, but it’s not as easy as feeding data into an algorithm running on a supercomputer and having it solve all your problems instantly. It takes a lot of iterations in the trenches to label data, train models, evaluate results and deploy a custom solution.

The reality: AI is just like any other engineering problem. Building a solution requires a sequence of fundamental steps.

  • Defining your business problem(s)
  • Collecting and labeling data
  • Multiple iterations of developing, training and evaluating models
  • Deploying the model into your workflow

What capabilities do I need for AI success?

AI and ML can benefit a variety of businesses, but it’s essential to create a solid foundation for a successful deployment.

When you approach an AI integration with the following four capabilities in mind, you’ll get a consistent process from idea to business impact, one that can scale to multiple business functions across the enterprise. Here are the four essential ingredients for a successful AI rollout:

Four essential ingredients for a successful AI rollout.

Four essential ingredients for a successful AI rollout.

Technology Platform

You need a scalable technology platform that can ingest, label and pre-process a large amount of data, train models in a distributed environment across multiple GPUs or TPUs and deploy your trained models to handle high-volume inference workloads while making your models re-usable across a variety of scenarios.

It is worth noting that in recent years, Google has pioneered widespread use of AI across its products and has largely been responsible for bringing AI out of academia and into the mainstream. The Google Brain team has published a variety of industry-leading AI research and released TensorFlow, the world’s most-popular open-source machine-learning framework. Through the Google Cloud Platform, the organization is democratizing AI and making fully managed services available that can enable any organization to take advantage of machine learning.


The mainstream belief is that building AI solutions requires “unicorns,” magical mythical creatures that can code on command line, have a deep understanding of the mathematical and statistical techniques underlying machine learning and have a strong hands-on feel for the business. The truth is that this type of talent is extremely rare and non-scalable.

In our experience, it is much more viable to staff AI projects with a combination of cloud, data and ML engineers working alongside a business domain expert, all under the guidance of a solutions architect who has a prior track record of success in AI initiatives. We call this model “synthetic unicorns,” and we’ve seen it scale to hundreds of team members across multiple business work streams, all working under a well-orchestrated AI Center of Excellence.

Reusable Model Repository

We have seen several successful implementations of point solutions in AI. However, most enterprises find themselves in situations where multiple groups are trying to solve similar problems and unable to benefit from work performed by other groups. In order to avoid this scenario and enable collaboration and reuse, we recommend that organizations set up a repository that includes the following artifacts:

  • Model objects
  • Source code used to train the model objects
  • Training data used to train the model objects
  • Training and inference pipelines
  • Notebooks
  • Documentation


You can have the right talent, data and technology in place, but it all comes together in the workflow. You’ll need to develop a workflow that outlines each step of the process — from defining the business problem to collecting data to developing models to deploying models. You’ll also need to ensure your workflow is repeatable, scalable and consistently followed across all AI initiatives.

How do I get going? The four key stages of the AI journey

To fully reap the benefits of AI and ML, think of it as a journey rather than a point implementation. Our recommendation can be best summarized in a four-step journey: Hack It, Prove It, Nail It, Scale It.

Stage 1: Hack It: The ideation phase. Here, we recommend applying the ideation framework described in the “Building AI Applications” section. This will help you carefully select a use-case with high feasibility and impact potential, then have a very quick iteration to develop a prototype. It can help the team construct a line-of-sight to the solution.

Stage 2: Prove It: The proof-of-concept phase. In this phase, we fully define the problem and set target acceptance criteria to prove to business stakeholders that the selected problem can be solved with AI. Once this is done, we collect and label data, train models and evaluate them against the acceptance criteria. The process is repeated until the acceptance criteria is met. It is important to set the scope to a “minimum viable model,” something that can eventually be used in a production workflow for a specific segment of your business.

Stage 3: Nail It: The product-development phase. This is where we deploy the “minimum viable model” into your production environment. We also ensure that all upstream and downstream processes are functioning as performed and that business stakeholders are seeing ROI from the solution.

Stage 4: Scale It: Once the AI solution has been validated in a sub-segment of your business, it is now time to expand the scope of the solution to encompass the full spectrum of opportunities across the enterprise. We often see several dozen team members involved in this phase, working across multiple business work streams under a well-orchestrated AI Center of Excellence.

Let’s put AI to work for you!

Successful AI adoption begins with a clear vision. First and foremost, you’ll need to understand the types of problems AI and ML can solve. This will help you identify your key business objectives.

After you define the problems you want to solve, an understanding of what it will take to get there is essential. You’ll need to optimize your technology environment, talent, assets and workflow to set yourself up for success. Once the development work starts, it’s important to take a measured approach to building, testing, refining and deploying your solutions — and to make sure they can scale and bring value to various parts of your organization.

There’s no better time to start that journey than now. As mentioned earlier, in this recent MIT Sloan Management Review study, 91 percent of respondents expect to derive new business value from AI implementations over the next five years. The question you should ask yourself is: In the spectrum of passives, experimenters, investigators and pioneers, where do I want my business to be?

Source: Quantiphi

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