Rethinking employee benefits in a post-pandemic world

There’s no going back to the 2019 playbook, particularly for benefits. Learn what employees expect in the new normal, and how you can keep a competitive edge.

If your company has yet to embrace AI, you’re in a race against the clock. And by my calculations, you have just three years left.

How did I arrive at 2024 as the deadline for AI adoption? My prediction — formulated with KUNGFU.AI advisor Paco Nathan — is rooted in us noticing that many futurists’ J curves show innovations typically have a 12-to-15-year window of opportunity, a period between when a technology emerges and when it reaches the point of widespread adoption.

While AI can be traced to the mid-1950s and machine learning dates back to the late 1970s, the concept of deep learning was popularized by the “AlexNet” paper published in 2012. Of course, it’s not just machine learning that started the clock ticking.

Though cloud computing was initially introduced in 2006, it didn’t take off until 2010 or so. The rise of data engineering can also be traced to the same year. The original paper for Apache Spark was published in 2010, and it became foundational for so much of today’s distributed data infrastructure.

Additionally, the concept of data science has a widely reported inception date of 2009. That’s when Jeff Hammerbacher, DJ Patil and others began getting recognized for leading data science teams and helping define the practice.

If you do the math, those 2009–2012 dates put us within that 12-to-15-year window. And that makes 2024 the cutoff for companies hoping to gain a competitive advantage from AI.

You can still gain an edge — if you act now

If you look at the graph below — from Everett Rogers’ Diffusion of Innovations — you’ll get a sense of how those who wait to put AI into production will miss out on cornering the market. Here the red line shows successive groups adopting new technology while the purple line shows how market share eventually reaches a saturation level.

Source: Everett Rogers, Diffusion of Innovations

A 2019 survey conducted by the MIT Sloan Management Review and Boston Consulting Group explicitly shows how the Diffusion of Innovations theory applies to AI. Their research was based on a global survey of more than 3,000 executives, managers, and analysts across various industries.

Once the responses to questions around AI understanding and adoption were analyzed, survey respondents were assigned to one of four distinct categories:

Pioneers (20%) These organizations possess a deep knowledge of AI and incorporate it into their offerings and internal processes. They’re the trailblazers.

Investigators (30%) These organizations understand AI but aren’t deploying it beyond the pilot stage. They’re taking more of a “look before you leap” approach.

Experimenters (18%) These organizations are piloting AI without truly understanding it. Their strategy is fake-it-until-you-make-it.

Passives (32%) These organizations have little-to-no understanding of AI and will likely miss out on the opportunity to profit from it.

The 2020 survey, which uses the same questions and methodology, gives even greater insight into how executives embrace AI. 87% believe AI will offer their companies an advantage over others. Just 59% of companies, however, have an AI strategy.

Comparing the MIT and BCG 2020 survey responses to those since the survey’s inception in 2017, a growing number of execs recognize that competitors are using AI. Yet only one in 10 companies are using AI to generate significant financial benefits.

I anticipate this gap between leaders and laggards will continue widening, making this your company’s last chance to take action before 2024 (if it hasn’t already).

What it takes to realize success

MIT and BCG’s 2020 data reveals that companies focused on the initial steps of AI adoption (ensuring data, talent, and a strategy are in place) will have a 21% chance of becoming a market leader. When companies begin to iterate on AI solutions with their organizational users (effectively adopting AI and applying it across multiple use cases) that chance rises to 39%. And those that can orchestrate the macro and micro interactions between humans and machines (sharing knowledge amongst both and smartly structuring those interactions) will have a 73% chance of market leadership.

Building upon MIT and BCG’s success predictions, McKinsey & Company has specifically broken down how AI integration impacts revenue in this 2020 chart.

Source: McKinsey & Company Global Survey, 2020

While the ROI for AI integration can be immediate, that’s not typically the case. According to MIT and BCG’s 2019 data, only two out of three companies that have made some investment in AI (Investigators and Experimenters) report gains within three years. This stat improves to three out of five when companies that have made significant investments in AI (Pioneers) are included.

The 2020 MIT/BCG data builds upon this, claiming companies that use AI to make extensive changes to many business processes are 5X more likely to realize a major financial benefit vs. those making small or no changes to a few business processes.

So where will you be in 2024? On your way to reaping the rewards of AI, or lamenting that you missed an opportunity for market advantage?

Steve Meier is a co-founder and Head of Growth at AI services firm KUNGFU.AI.

Rethinking employee benefits in a post-pandemic world

There’s no going back to the 2019 playbook, particularly for benefits. Learn what employees expect in the new normal, and how you can keep a competitive edge.

Presented by BeyondMinds

In recent years, AI has gained strong market traction. Enterprises across all industries began examining ways in which AI solutions can be deployed on top of their legacy IT infrastructure, addressing a host of pain points and business needs, from boosting productivity to reducing production defects, streamlining cumbersome processes, identifying fraudulent transactions, optimizing pricing, reducing operational costs, and delivering hyper-customized service.

The reason that AI can power such a rich variety of use cases is that AI in itself is a very broad term. It covers diverse domains such as natural language processing (NLP), computer vision, speech recognition, and time series analysis. Each of these domains can serve as the base for developing AI solutions tailored to a specific use case of one company, utilizing its particular datasets, environment, and desired outputs.

Despite AI’s immense potential to transform any business, oftentimes this potential is not realized. The somber reality is that most AI projects fail. According to a Gartner research, only 15% of AI solutions deployed by 2022 will be successful, let alone create ROI positive value.

This disparity between the big promise of revolutionizing businesses and the high failure rate in reality, should be of interest to any enterprise embarking on the digital transformation journey. These enterprises should ask themselves two key questions:

“Why do most AI projects fail?” and “Is there any methodology that can overcome this failure rate, paving the way to successful deployments of AI in production?”

The answer to the first question starts with data. Specifically, the challenge of processing data in a real-life production environment, as opposed to a controlled lab environment. AI solutions are based on feeding algorithms with input data, which is processed into outputs in the desired form that serves the business case, such as data classification, predictions, anomaly detection, etc.

To be able to produce accurate outputs, AI models are trained with the company’s historical data. A well-trained model should be able to deal with data that is similar to the samples it was trained with. This model may keep running smoothly in a controlled lab environment. However, as soon as it’s fed with data originating from outside the scope of the training data distribution, it will fail miserably. Unfortunately, this is what often happens in real-life production environments.

This is perhaps the core reason most AI projects fail in production: the data used to train the model in sterile lab environments is static and fully controlled, while data in real-life environments tends to be much messier.

Let’s take, for example, a company that deploys a text analysis model at its support center, with the aim of automatically analyzing emails from customers. The model is trained with a massive pool of text written in proper English and reaches high accuracy levels in extracting the key messages of each document. However, as soon as this model is deployed in the actual support center it runs into text riddled with typos, slang, grammar errors, and emojis. Facing this kind of “dirty data,” most AI models are generally not robust enough to produce meaningful outputs, let alone provide long-term value.

Launching an AI solution in production is only half the battle. The second pitfall in AI implementation is maintaining the solution on course once deployed. This requires continuous control of data and model versions, optimization of a human-machine feedback loop, ongoing monitoring of the robustness and generalization of the model, and constant noise detection and correlation checks. This ongoing maintenance of an AI solution in production can be an extremely challenging and expensive aspect of deploying AI solutions.

The combined challenge of launching an AI solution in a noisy, dynamic production environment and keeping it on the rails so it continues to deliver accurate predictions is the core reason that makes AI implementation profoundly complex. Whether trying to tackle this technological feat in-house or turning to an external provider — companies struggle with AI implementation.

So, what can companies do in order to overcome these challenges and beat the 85% failure rate? While the textbook on guaranteed AI implementations has yet to be written, the following four guidelines can be used to curb down the risk factors that typically jeopardize deployments:

1. Customizing the AI solution for each environment

Every AI use case is unique. Even in the same vertical or operational area, each business uses specific data to achieve its own goals, according to a particular business logic. For an AI solution to provide the perfect fit for the data, environment, and business needs, all these specificities need to be translated and built into the solution. Off-the-shelf AI solutions, in contrast, are not customized to specific needs and constraints of the business and will be less effective in creating accurate outputs and value.

2. Using a robust and scalable platform

The robustness of AI solutions can be measured by their ability to cope in extreme data scenarios, facing noisy, unlabeled and constantly changing data. In evaluating AI solutions, enterprises should ensure that the anticipated outcomes will withstand their real-life production environments, instead of relying just on performance tests in lab conditions.

3. Staying on course once in production

AI solutions must also be evaluated on their stability over time. Companies should familiarize themselves with the process of retraining AI models in live production environments, constantly fine-tuning them with feedback by human inspectors.

4. Adding new AI use cases over time

An AI solution is always a means to an end, not a goal in itself. As such, the contribution of AI solutions to the business should be evaluated considering the broad perspective of the enterprise’s business needs, goals, and digital strategy. One-point solutions may deliver on a specific use case but will create a painstaking patchwork once additional AI solutions will be deployed to cover additional use cases.

The potential that lies in AI may very well transform businesses and disrupt entire industries by the end of this decade. But as with any disruptive technology, the path to implementing AI is treacherous, with most projects falling by the wayside of the digital transformation journey. This is why it’s crucial for enterprises to learn from these past failures, identify the pitfalls in deploying AI into production, and familiarize themselves with the technology to a point in which they have a solid understanding of how specific AI solutions can deliver on their expectation.

Sharon Reisner is the CMO of BeyondMinds, an enterprise AI software provider delivering AI solutions and accelerating AI transformations

Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. Content produced by our editorial team is never influenced by advertisers or sponsors in any way. For more information, contact [email protected].