Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Unlocking greater performance gains in warehouses using artificial intelligence (AI) and machine learning (ML) helps make supply chains more resilient and capable of bouncing back faster from disruptions. Unfortunately, the severity and frequency of supply chain disruptions are increasing, with McKinsey finding that, on average, companies experience a disruption of one to two months in duration every 3.7 years. Over a decade, the financial fallout of supply chain disruptions in the consumer goods sector can equal 30% of a year's earnings before interest, taxes, depreciation and amortization (EBITDA).

AI has finally settled into the mainstream. Successful proof-of-concepts have emerged in a number of industries, and there have been many examples of successful plant-floor deployments of AI. Some organizations have applied AI/ML projects across the enterprise to complete pipelines. This overall maturity is changing the way companies view the strategic value of AI and the areas in which they want its benefits to be realized. Let's look at 10 AI company strategy trends currently diagnosed by industry experts.

Organizations that continued full speed ahead with their digital transformation initiatives during the COVID-19 pandemic are able to ruminate on what went right and what they would have done differently, with the benefit of hindsight. Some of what they've gleaned comes as no surprise: A successful digital transformation requires executive buy-in, constant communication with business units, and of course, financial commitment. A newly released report from Deloitte supports that, noting that a straightforward, compelling "north star" narrative is critical to success for 38% of executive respondents. A leader also needs to devote time and energy to drive a transformation forward. When a chief transformation officer contributed an additional 15% of their time, the probability of success improved by approximately 16%, according to the study. In terms of financial investment, half of the survey respondents indicated that their organizations invest between 1% and 5% of annual revenue on transformation programs.

Artificial intelligence has hit the mainstream. Across industries, companies have rolled out successful proofs-of-concept and have even been successful in deploying AI in production. Some organizations have even operationalized their AI and machine learning strategies, with projects proliferating across the enterprise, complete with best practices and pipelines. Today, companies at the leading edge of the AI maturity curve are making use of AI at scale. This overall maturation of how AI is deployed in enterprises is shifting how companies view the strategic value of AI -- and where they hope to see its benefits realized. Here is a look at 10 AI enterprise strategy trends that industry experts are seeing unfolding today.

Artificial intelligence has hit the mainstream. Across industries, companies have rolled out successful proofs-of-concept and have even been successful in deploying AI in production. Some organizations have even operationalized their AI and machine learning strategies, with projects proliferating across the enterprise, complete with best practices and pipelines. Today, companies at the leading edge of the AI maturity curve are making use of AI at scale. This overall maturation of how AI is deployed in enterprises is shifting how companies view the strategic value of AI -- and where they hope to see its benefits realized. Here is a look at 10 AI enterprise strategy trends that industry experts are seeing unfolding today.

USB drives are used by ransomware attackers to distribute malware across the air gap that all industrial distribution, manufacturing, and utility firms rely on as their first line of defense against cyber attacks. According to Honeywell's Industrial Cybersecurity USB Threat Report 2021, 79 percent of USB assaults have the potential to damage operational technologies (OT) that power industrial processing plants. The incidence of malware-based USB attacks is one of the most rapidly developing and difficult-to-detect threat vectors that process industries such as public utilities confront today, according to the research. As the Colonial Pipeline and JBS Foods demonstrate, this type of attack vector is particularly effective. Utility companies are also being targeted by ransomware criminals, as the thwarted water treatment plant attacks in Florida and Northern California illustrate.

Honeywell's global businesses have a strong legacy in India, built over the last eight decades. It delivers industry-specific solutions like aerospace products and services, control technologies for buildings and performance materials. Currently, it employs close to 13,000 people across different locations, including Bengaluru, Chennai, Gurugram, Dehradun, Hyderabad, Madurai, and Pune. At the centre of everything that Honeywell does, lies its data science team, which adds value by transforming products and services. They work on a wide array of key functional areas, including product development, advanced image, speech and audio recognition, text and video analytics, regression analytics and face detection.

Depalletizing is a common manufacturing operation that has attracted a lot of attention over the past few years due to a variety of industrial labor issues, ranging from a shortage of available workers to efforts to reduce risk of worker injuries. To help address these issues around depalletizing operations, Honeywell has introduced its Smart Flexible Depalletizer, which uses artificial intelligence to ease the implementation of robotic depalletizing technologies and minimize the need for manual labor to break down pallet loads. Honeywell's vision and perception technologies are used to guide the depalletizer's robotic arm, allowing cases to be picked from a single- or mixed-SKU pallet in fixed or mobile locations. The company's computer vision technology identifies the location of every case on the pallet, while its artificial intelligence-driven perception software automatically recognizes a variety of packaging formats. Honeywell's new Smart Flexible Depalletizer in action at PACK EXPO Las Vegas 2021, showing how it can depalletize unorganized pallets.The machine learning and motion planning used in the Smart Flexible Depalletizer optimize the movements of the robotic arm to ensure maximum picking speed.

All the sessions from Transform 2021 are available on-demand now. Thanks to AI advancements and applications, edge computing is already seeing widespread interest from industries ranging from manufacturing to healthcare and retail. Leveraging the growing power and ubiquity of CPUs and neural processing units, edge AI can process growing haystacks of data right where they're being created, finding their needles quickly for local or remote processing. Edge AI is an enabler for early networked autonomous cars -- instantly recognizing and sharing details on accidents, weather conditions, and traffic from vehicle sensors and smart infrastructures in real-time. Similarly, edge AI has empowered wearables to actively monitor seniors for chronic health conditions, alerting remote caregivers within seconds of detecting abnormalities in their biometric data. It's clear that edge AI has the ability to open up a whole new world of insights and opportunities across multiple industries, but connecting the distributed data processors to usefully aggregate their discoveries is a higher-level task.

Honeywell expects that as advances in quantum computing continue to accelerate over the next 18 to 24 months, the ability to replicate the results of a quantum computing application workload using a conventional computing platform simulation will come to an end. The company's System Model H1 has now quadrupled its performance capabilities to become the first commercial quantum computer to attain a 512 quantum volume. Ascertaining quantum volume requires running a complex set of statistical tests that are influenced by the number of qubits, error rates, connectivity of qubits, and cross-talk between qubits. That approach provides a more accurate assessment of a quantum computer's processing capability that goes beyond simply counting the number of qubits that can be employed. Honeywell today provides access to a set of simulation tools that make it possible to validate the results delivered on its quantum computers on a conventional machine.