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Leaders versus Laggards in AI: Latest Findings on Generating ROI from Your AI Investments

The gap between leaders versus laggards in AI has widened significantly in the last 6 months, even as leaders are investing big time on pilot projects to transform business teams with AI and Deep Learning. In a powerful survey finding, market research firm ESI ThoughtLab has found out APAC region leads (14.1 Billion USD) in average revenue earned through the adoption of AI applications in 2020. North America ($13.9 billion) and EU ($12.7 Billion) have also reported significant revenue growth from AI adoption.

Culture Drives AI RoI

Laggards in AI can drive home success with AI investments by developing a culture of learning and sharing knowledge. ESI ThoughtLab reports AI leaders are constantly amplifying their data science talent pool by acquiring AI businesses.

Bulent Kiziltan, Chief Data & Analytics Officer, StealthX says, 

Unless companies promote learning, mentoring, training, and collaboration, they will become siloed. This will inevitably result in high turnover and add to the overhead cost of the analytics operation.” 

 

The impact of AI maturity on talent development
The impact of AI maturity on talent development [source: ESI ThoughtLab]
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If you are planning to adopt AI systems to digitally transform your enterprise, you must prepare for some disruptions to your operations. But, it’s not the sudden transformation that should worry you. Rather, it’s the express ROI you expect from the AI adoption that would startle you. If you are an AI follower and not a leader, gaining results from AI could take you one-third the time to actually report sizeable returns on investment. Also, where you apply AI makes a huge impact on your overall performance and efficiencies across the enterprise.

ESI ThoughtLab has found that two-thirds of senior executives across industries—and nearly nine out of ten leaders from the world’s largest enterprises—believe that artificial intelligence is vitally important for the future of their businesses. AI will be upping their investments in the post-pandemic era. Yet their companies are now seeing an average ROI of only 1.3%, and 40% of AI projects are not yet profitable.

Read More: HackNotice Launches HackNotice Teams, The First Company-Wide Threat Intelligence Platform

According to Driving ROI through AI, a just-released research study conducted by research firm ESI ThoughtLab and a coalition of AI leaders, including Appen, Cognizant, Cortex, Dataiku, DataRobot, Deloitte, and Publicis Sapient, AI initiatives require time, expertise, and scale to deliver on their promise of high returns.

With the pandemic speeding up the need for quick data-driven decision-making, companies should act now to develop the skills, platforms, and processes that can enable them to achieve the full strategic, operational, and financial benefits from AI.

As part of a rigorous research program, ESI ThoughtLab economists benchmarked the AI practices, performance results, and three-year plans of 1,200 companies in 12 industries and 15 countries, which together have combined revenue of $15.5 trillion (or about $12.9 billion per firm). Conducted during the COVID-19 outbreak, the study reveals the value that AI can bring in a socially distancing, the digital-first world—including access to time-critical data, event-driven forecasts, personalized digital experiences, flexible work processes, rapid decision-making, tighter cybersecurity, and greater cost efficiencies.

Fastest Way to Transform your Fate: Adopting InfoSec Ops

Success with AI is all about data management. Nearly half the CEOs (46%) and as many CISOs (53%) believe robust data management can support AI. Gary Grossman, SVP, Global Lead, AI Center of Excellence, Edelman, states that Big Data is essentially useless without AI and ML.

70% of the enterprises with revenue over USD 20 Billion have powerful data management systems that can virtually extract every iota of insights and intelligence from AI systems. However, data management frameworks differ from one company to another, and from industry to industry.

Each sector is expected to adopt and promote its own AI data strategy to separate from laggards in the AI value chain. In all these transformative evaluations within the AI ecosystem, CIOs and CISOs have to bind together InfoSec Ops, the new-gen technologies that automate tasks for threat intelligence reporting and risk assessment in Cloud and Virtualized IT networks.

According to Gartner, AI ML have an important role in security and digital business.

Gartner points out the three challenges to which CIOs and CISOs should work together.

The 3 key challenges are:

  1. Protect AI-powered digital business systems;
  2. Leverage AI with packaged security products to enhance security defense; and
  3. Anticipate the nefarious use of AI by attackers.

AI is a Slow Burning Process

The research shows that delivering ROI on AI can be elusive for the uninitiated and slow going even for experienced organizations. Those in earlier stages of AI adoption often see flat results. It is not until they scale AI more widely across their enterprises and become leaders that the ROI rises to 4.3%. With frequently high upfront costs in data preparation, technology adoption, and people development, it takes an average of 17 months for a firm to reach break-even and months more to generate significant returns.

AI techniques include:

  1. Machine Learning (ML)
  2. RPA
  3. Natural Language Processing (NLP)
  4. Deep Learning (DL)
  5. Computer Vision (CV)
  6. Big Data Management
  7. Intelligent Assistants / Chatbots

Kurt Muehmel, in his coolest poise, compares AI to electricity — “an important part of life in the relatively near future. Like electricity, most of us will take it for granted.”

However, doubts persist in AI’s definitive role in going beyond the initial spark and sustaining that fire beyond a few years of applications.
Most companies, even leaders, are still relatively early in their AI journey. Only about one-quarter of AI projects are now in widespread deployment among AI leaders.

Read Also: Dataiku Announces Partnership With Snowflake For Cloud Enterprise AI Solutions

Global leaders state that Automotive, banking and B2B SaaS markets are ripe with opportunities where the laggards in AI can scale big time.

Driving ROI Through AI

Media and VC space need to see more action. Though, we know that VC investments have increased significantly during the COVID-19, especially in the APAC region where leaders are springing ahead with new avenues.

Many AI projects are still in pilot or early deployment stages. However, firms are planning to boost their AI investments by an average of 8.3% annually over the next three years, bringing their annual AI to spend from $38 million currently (or 0.75% of revenue) to over $48 million.

Strategic Change Key to Transforming with AI

As companies progress in AI use, they often shift their focus from automating internal employee and customer processes to delivering on strategic goals.

At the time of this announcement, Lou Celi, ESI ThoughtLab CEO and Program Director for Driving ROI through AI said, “As the pandemic propels businesses into a digital-first world, AI will become a key driver of corporate growth  and competitiveness. But building proficiency in AI is not easy. AI is not a magic bullet. It can fail to deliver results if the wrong business case is selected, the data is  prepared incorrectly, or the model is not built for scale.”

For example, 31% of AI leaders report increased revenue, 22% greater market share, 22% new products and services, 21% faster time-to-market, 21% global expansion, 19% creation of new business models, and 14% higher shareholder value.

In fact, the AI-enabled functions showing the highest returns are all fundamental to rethinking business strategies for a digital-first world: strategic planning, supply chain management, product development, and distribution, and logistics. The study found that automakers are at the forefront of AI excellence, as they rush to use AI to deliver on every part of their business strategy, from upgrading production processes and improving safety features to developing self-driving cars.

Of the 12 industries benchmarked in the study, automotive employs the largest AI teams (557 people on average vs. 370 for all industries) and has the largest AI budgets ($59.4 million on average vs. $38.3 for all industries). With the government actively supporting AI under its Society 5.0 program, Japanese companies lead the pack in AI adoption. Unlike in the U.S., where AI is viewed often as a threat to jobs, firms in Japan tend to see AI as a way to fill the employment gap caused by an aging population and stringent immigration laws.

How to Make AI work for You?

To drive AI performance, executives should consider these best practices uncovered by the research:

Begin with pilots, then scale AI applications across the enterprise. 

Companies starting out should work closely with business teams to identify use cases and demonstrate AI’s worth through pilots. But the true value of AI can materialize only with widescale deployment when firms can offset their upfront costs with substantial business gains.

Lay a firm foundation.

Organizations should have the proper I.T. and data management system in place; have a secure and sufficient budget; work through the data security, privacy, and ethical risks of AI; develop a clear vision and plan that takes into account AI-driven strategic transformation; obtain senior management support, and have a robust ecosystem of partners and suppliers.

Get your data right.

Nine out of ten AI leaders are advanced in data management. But ensuring your data is in good shape is not enough; organizations should bring in a diverse set of data, such as psychographic, geospatial, and real-time data. The study found that combining different types of data can create a multiplier effect on AI returns.

Solve the human side of the equation. 

AI is as much about people as technology. AI leaders spend 27% of their AI budget on developing and hiring people, almost twice the percentage that AI beginners spend. They are also more apt to appoint specialists, such as Chief AI and Data Officers, to lead their AI initiatives. They outsource less and build internal teams more.

Adopt a culture of collaboration and learning.

About 85% of companies that generate large AI returns work to ensure close collaboration between AI experts and business teams. AI leaders are better at providing non-data-scientists with AI skills. They also decentralize AI’s authority to help ensure that AI’s responsibility and expertise are distributed across their organizations.

ESI ThoughtLab is an innovative thought leadership firm that creates fresh thinking and actionable insights through rigorous research and evidence-based analysis. Our firm specializes in using the latest quantitative and qualitative tools to examine the impact of technology on companies, cities, industries, and business performance. ESI ThoughtLab is the thought leadership arm of Econsult Solutions, a leading economic consultancy, with direct links to the academic community.

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