In an interaction with CRN, Siddhesh Naik, Data, AI & Automation Sales Leader, IBM Technology Sales, IBM India/South Asia outlines how AI as an emerging technology can help the world achieve environmental sustainability.
How has the AI landscape evolved in the last couple of years and what is driving this change?
Clients are embracing digital technologies as a means for driving meaningful and substantive change within their business. Organizations need to meet rising customer expectations, operate more efficiently, and manage increasingly common and severe disruptions like new business models, major weather events or a global pandemic. They are looking at speed, efficiency, and innovation to stay ahead of the competition and the pandemic has only accelerated the adoption of AI.
AI for business is significantly different than consumer AI. AI is getting embraced by enterprises in two primary areas – Customer Experience and Operation Excellence. We are helping our enterprise clients embrace AI. Key AI Initiatives with enterprise customers include:
• Advanced NLP capabilities are providing new levels of personalization and productivity to customer care through AI.
• Telecom companies are using AI to bring new 5G services to consumers.
• Breakthrough AI capabilities for combating fraud, real time.
• AI powered automation to drive business processes
• AI is bringing new levels of efficiency to IT operations through automation
• Clients are using AI to lower carbon footprints, and bring new levels of intelligence and efficiency to their operations across asset management, supply chains and environmental intelligence
• The analytics landscape has evolved from just dashboard to insights to predictive analytics.
A key driver of this trend is the democratization of AI, especially with the pandemic as well as the awareness among the relevant set of stakeholders.
What are the most common pitfalls when companies are adopting AI across the organization and how can businesses address them?
The “Global AI Adoption Index 2021,” conducted by Morning Consult on behalf of IBM, revealed lack of AI skills and increasing data complexity as top challenges to AI adoption. In addition to it, data quality and lack of governance frameworks that enable trust in AI’s output are posing new challenges. To address these, here’s what organizations can do:
• Building a Data Fabric – Organizations have different siloed data sets running on-premises and across multiple public and private clouds which makes it hard to put their data to work and also creates quality and governance issues. A data fabric architecture solves these core issues and is a better way for clients to put data to work, no matter where it resides. IBM Cloud Pak for Data delivers a data fabric architecture that allows an enterprise to connect and access siloed data, across distributed environments without ever having to copy or move it – and with governance, security and privacy embedded.
• Data quality – Good training data is a prerequisite for better performance of machine learning model. Data quality for AI is fundamentally different from the one required for business intelligence, e.g., how to detect and correct wrong labels in the data is a pressing problem. IBM Research team is building novel algorithms and toolkits for data assessment and remediation so that the downstream AI model can be more accurate, fair, and robust.
• Building trust in AI – Organizations have a fundamental responsibility to foster trust in the technology they use including AI. IBM is helping companies achieve greater trust, transparency and confidence in business predictions and outcomes through data and AI governance tools like AI Fairness 360 Toolkit; AI Explainability 360 Toolkit; Adversarial Robustness Toolbox and AI Factsheets. These resources available via open-source aim to advance the industry-wide effort of addressing and mitigating bias in AI and building trust.
• AI skills – First, organizations need to invest in creating awareness about AI and how to use it effectively. Second is adopting AI with a trusted partner like IBM – who have the skills, talent and capabilities. IBM is helping organizations to meet this accelerating demand for AI with IBM Watson. Watson provides clients with pre-built AI applications that run anywhere like Watson Assistant, that are targeted at solving a specific business problem, such as customer care etc.
Which areas and industries are seeing an increased adoption of AI?
As per Global AI Adoption Index 2021, 62% of Indian IT professionals cite reasons such as driving great efficiencies in processes and tasks as considerations for using automation software or tools. Over half (54%) of Indian IT professionals cite that needing a better way to interact with customers influenced their decision to use automation software or tools.
Some of the use case scenarios that are prevalent include:
• Embedding AI in digital journeys: Enterprises need AI that understands the unique language of their industry and business, and that can extract insights from complex documents and data. Using Natural Language Processing (NLP) they are able to understand their data and offer a highly personalized customer experience and to seamlessly integrate online and offline engagements.
• Acceleration of intelligent automation: Companies are turning to automation to free up employees to focus on strategic, value-added tasks. Infusing AI into automation for driving innovation and efficiencies, help save costs, streamlining processes and generating actionable insights, and accelerating service delivery for improved experiences among customers, employees, and users, as well as help, improve bottom-line results.
• AI in IT operations: AI is being used to simplify IT operations management and accelerate and automate problem resolution in complex modern IT environments. It enables IT operations teams to respond more quickly—even proactively—to slow down and prevent outages, with a lot less effort.
• Security – AI is changing the game for cybersecurity by analyzing massive quantities of risk data to speed response times and augment under-resourced security operations. Through predictive analysis, AI is able to identify network anomalies, detect malware, and analyze user behavior patterns to detect risky users within an enterprise, and potentially thwart fraud or insider threats.
Industries such as the banking and financial sectors, retail & consumer have been at the forefront of AI adoption. Other sectors like telecom, manufacturing and others are also looking at embedding AI into their core business processes. AI is now becoming part of business functions such as hiring, supply chains, and customer service.
How can AI as an emerging technology help the world achieve environmental sustainability?
AI, with its ability to help us make sense of data, offers infinite possibilities and is increasingly becoming critical to help businesses face emerging challenges including those related to sustainability, resiliency and supply chain. For instance – climate risk models ingest tremendous amounts of data – both heterogenous and unstructured data from aerial imagery, maps, IoT infrastructure, drones, LiDAR and satellites and so on to predict the risk and potential impact of upcoming climate and weather hazards.
AI can help in aggregating and analyzing this data aggregating and analyzing this data and predict evolving climate and weather risks. Similarly, AI and NLP algorithms can help in measuring carbon footprint reporting from a manual aggregation and measurement process to an automated method. This can also help in solving data quality issues, and capture its nuances more accurately.
IBM is applying AI to massive geospatial datasets and bringing together innovations for climate risk, carbon accounting and weather forecasting, together with companies’ operational data – providing a unique platform to address both sustainability and climate risk in this way.
How does IBM Environmental Intelligence Suite leverage AI to help organizations prepare for and respond to weather and climate risks?
The IBM Environmental Intelligence Suite (EIS) is an AI-powered SaaS solution that leverages weather data, advanced AI/geospatial analytics, climate risk analytics and carbon accounting APIs – to deliver environmental insights via APIs, dashboards, maps and alerts. This can help organizations address both immediate operational challenges as well as longer term planning and strategies. Broadly, IBM Environmental Intelligence Suite enables businesses to do four things:
• Monitor for disruptive environmental conditions such as severe weather, wildfires, flooding and air quality and send alerts when detected
• Predict potential impacts of climate change and weather across the business using climate risk analytics
• Gain insights into potential operational disruptions and prioritize mitigation and response efforts
• Measure and report on environmental initiatives and operationalize carbon accounting, while reducing the burden of this reporting on procurement and operations teams.
EIS is integrated with IBM’s broader software portfolio to help companies further operationalize environmental intelligence – including IBM Maximo Application Suite to help companies protect and extend the lifecycle of their critical assets and IBM Supply Chain Intelligence Suite to help build more sustainable and resilient supply chains.
Share a couple of success stories where this Suite has helped organizations optimize their business processes and resource consumption while minimizing wastage and pollution?
EIS can be used to solve challenges across a broad set of industries including energy & utilities, renewables, agriculture as well as retail & distribution. For example, a manufacturing company could use the framework to assess where high floods may be a risk in the future, and then decide where to maintain, build and move warehouses. The company could also overlay these future flood forecasting maps on top of current operational data and determine where they should invest in additional weather-hardening to protect existing investments.
In another scenario, an organization with a large vehicle fleet, such as a shipping and logistics company can use the EIS to collect data at a granular level to track how much fuel each truck consumes. Then, carbon accounting APIs could be applied to calculate mobile emissions into carbon equivalence. This individual fleet data can then be aggregated to generate an operational view to capture the fuel consumption of a full trucking fleet. The operational fleet data can then be analyzed with other dimensions of carbon emissions accounting to produce a holistic, enterprise-wide view. Similarly, insights from EIS could help:
• Retailers prepare for severe weather-related shipping and inventory disruptions, or factor flood risk into future warehouse locations
• Energy and Utility companies determine where to trim vegetation around power lines, or determine which of their critical assets may soon be at greater risk from wildfires
• Supermarkets gain a clearer picture of how refrigeration systems are contributing to their overall greenhouse gas emissions and prioritize locations for improvement.