Transformation to an AI First organization – Decision Intelligence, Efficiencies and Modernization
Many organizations now believe that the impact of Artificial Intelligence (AI) and Machine Learning (ML) is real, and the change is inevitable. As the AI and ML technologies got standardized and simpler in the recent past, it has become a mandate to be an AI-first organization for survival. Harnessing the potential of AI and ML technologies starts with a business function, challenges, and a few AI use cases about the issues. As per McKinsey’s report on AI, almost 50% of the businesses adopted AI in at least one business function. But, scratching the surface may transform a process or function; it can’t make you the leader in the market but a mere follower of the technology trends.
Transforming to an AI-first organization requires rethinking every business process and operation model where machine learning and cognitive intelligence are deployed to support decision-driven thinking. As per the book, The AI Advantage by Thomas H. Davenport, AI-powered transformation for an organization is in three stages:
- Assisted Intelligence – Organizations that leverage data science techniques and cloud-based data models to transition to be data-driven.
- Augmented Intelligence – Machine learning models are augmented with the existing data management systems to support human decisions.
- Autonomous Intelligence – Processes are digitized, automated, and personalized experiences to impact revenue streams, process efficiencies, and decisions. Machines, bots, and humans work in tandem to fuel organizations with insights at the right time.
This transition between stages requires sustained commitment to redesign processes, core systems, and strategies around AI and ML and leveraging simplified AI and ML technologies.
Transition to AI-first Organization for Business Resilience
AI and ML are now a true differentiator in any industry irrespective of the size of the business – large, medium, or small. Organizations that leverage AI at the core were able to enhance their products/services, optimize internal/external operations and make better decisions. Modernizing the application landscape to improve the revenue streams and pro-active management of resources is the new competitive advantage in the fast-paced digital world. Let us look at a few examples of a few leaders who were AI-centric in recent times.
The Rollout of Pfizer Covid Vaccine
Do you know that AI was behind the fast rollout of the Pfizer Covid vaccine? Yes, Pfizer leveraged AI, automation, and intelligent insights to accelerate their time to market. Pharma companies are always at the front foot to leverage technology and Pfizer was an early adaptor to the AI-driven transformation approach. Initially, they leveraged AI for automation, faster drug development, regulatory compliance, and virtual physician and customer engagement. During the Covid vaccine rollout, Pfizer leveraged automation, virtual clinical trials, data management, and processing powered by AI to roll out the vaccine in a year.
In remote trials, Pfizer’s AI implementation could identify the signals among their 44,000 candidates, and a Machine learning model helped review the data in 24 hrs instead of 30 days. AI-powered data cleansing tools were also leveraged to ensure data quality and accelerate the vaccine rollout.
Zurich Insurance – Claims processing
Zurich Insurance, a leading innovator in the insurance space, reduced its property claims settlement times from many days to a few hours leveraging AI. Claims processing is the core activity for insurers, and it involves a lot of minuscule activities that need to be reinvented to experience overall effectiveness and efficiencies.
Zurich Insurance leveraged Azure ML to process customer information. It includes extracting information from car documents, processing customer forms for quotes, analyzing documents to understand any risk of recourse, and an MLOps platform to transform AI use cases to reality quickly. They leveraged support from several partners to deliver automated policy checking with around 98% accuracy better than humans. Zurich made AI very simple with ‘Explainable AI’ so that the claim handler could understand the context from customers and share it accordingly.
Top 3 initiatives for a successful AI and ML strategy
It is easy to talk about technology and all the top executives are alluded to making AI and ML their core competency irrespective of the business and industry vertical. A well-planned and better architected AI and ML strategy can only derive the best value from your investment. Here go the top 3 initiatives that we think are critical in an AI and ML strategy:
Diversified teams – AI roles are not limited to a codebase, and AI teams have to deal with data, underlying models, cognitive tools, strategy, and design of the applications. AI and ML teams are not just with data scientists and AI researchers but much more diversified with strategists, application designers, project managers, and functional experts. Varied backgrounds and understanding of teams can deliver significant business value.
Purpose and measure the value – Assessing the risk and impact of AI deployments is key to realizing the value; organizations need to frame their AI and ML strategy with a clear value proposition to witness the transformation. Any new initiatives also need measures to be in place to not go overboard with AI and ML investments.
Beyond POCs – Organizations invest a lot of effort on PoCs to try everything and focus on maturing the use case that worked. Now that everyone trusts the potential of AI and ML, organizations should limit the POCs number. As per Gartner, organizations operationalizing AI and ML do about 20% fewer PoCs than those planning to invest.
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A few applications of AI and ML for businesses
AI is now everywhere, and we are experiencing it every day; AI and ML power Google Home, Amazon Alexa, and your voice assistant. Yes, we are touched by AI and ML in our every walk of life, but we need to create sparkles soon. It is not about the natural face of AI and ML that we see, and we need to re-imagine every process in any business with AI and ML. Intelligent processes, application modernization, and smart business decision-making in every organization will soon be leveraging AI. Let us look at a few applications that may connect to your business needs:
Conversational AI to Boost the Agility in the Answer Economy
We are already witnessing the power of chatbots and conversational AI in every business. Today, most of them provide information as per our requests and become our next-door virtual assistants. But soon, this is expected to change, with chatbots powered by more intelligence to make decisions.
It is not just about your customer queries and information; chatbots can power your employees to serve customers better by providing actionable recommendations and the next best action. Employees can suggest the best service/product recommendation and take the conversations further with assistance from AI and ML.
Smart Operations – AI embedded everywhere
Operational processes in every business are different according to their industry and size. Retailers, for example, are trying to optimize their in-store experience with customer activity monitoring, inventory monitoring, and guided product selection. In healthcare, optimization of resources became crucial during the pandemic – forecasting the critical care, optimizing resources, and customized personal care have been priorities.
IT operations are not an exception, and AI is transforming the application security landscape and infrastructure problems. Service requests are monitored, allocated, and analyzed leveraging AI. Software development is not left behind; AI is used to create codebase and automate software tests. These are in the initial phases, but soon the transformation can be phenomenal.
Cost-effective Supply Chain with AI
Supply-chain is always data-intensive, and real-time visibility is now essential to resilience with market volatility after Covid-19. Demand forecasting, inventory management, and dynamic price and margin management are among the few areas that leaders in the supply chain are investing. A few early adopters of supply chain management solutions witnessed improvements in logistics costs by 15%, inventory management costs by 35%, and service levels by 65%, says McKinsey. Most organizations are predicted to leverage AI for their supply chain in the next three years.
HR, Finance and Marketing transformation with AI
Recruitment experience is now a critical factor in seeking potential candidates for your business. Employee experience is transforming too. HR departments are now leveraging AI to boost engagement, experience, and conversations with the talent pool and employees. Financial risk management is another crucial area that is being transformed with AI and ML.
Targeted campaigns, contextual messaging, and marketing operations have been improvised using AI and ML in recent times. But the usage is now limited to simple AI use cases and can potentially transform the entire marketing function in the near future.
Are you interested in modernizing your applications with AI and ML? Still have some queries!
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Gopi is the President and CEO of Saxon Global Inc since its inception and is responsible for the overall leadership, strategy, and management of the Company. As a true visionary, Gopi is quick to spot the next-generation technology trends and navigate the organization to build centers of excellence. As a digital leader responsible for driving company growth and ROI, he believes in a business strategy built upon continuous innovation, investment in core capabilities, and a unique partner ecosystem. Gopi has served as founding member and 2018 President of ITServe, a non-profit organization of all mid-sized IT Services organization in US.