The individuals at the forefront will have the greatest opportunities in Artificial Intelligence (AI). It is advisable to observe the outcomes before making significant investments, as only imprudent individuals are currently spending exorbitantly. Given the rapid evolution of technology, a purchase made today will likely become obsolete the following month. Persisting with outdated methods for another year could lead to obsolescence by 2025.
The landscape surrounding AI expenditure can be perplexing, dictating where and why to allocate funds. This dilemma often leaves CEOs and CFOs torn between the Fear of Missing Out (FOMO) and the Fear of Failing Utterly (FOFU). The array of options available is undeniable. According to research by McKinsey & Company, AI contributes approximately $4.4 trillion annually to the global economy. However, at the nascent stages of technological advancement, it is easy to misallocate funds. This is where the experts come in. Major corporations initiated the formation of AI teams several years ago, anticipating the surge in inquiries from eager clients. Over the next three decades, Accenture aims to double its workforce with expertise in Artificial Intelligence to 800,000 employees. The secret ingredient, perhaps, lies in their ability to offer clients a comprehensive roadmap on how to generate and save revenue by leveraging insights from numerous clients, providing an exclusive perspective on successful strategies. Insights were shared by experts from Accenture, McKinsey, and BCG regarding customer demands and impactful outcomes.
The Overall Opportunity
Alex Singla, a senior partner at McKinsey and co-leader of QuantumBlack, the AI division of the company, highlighted, “In the past eight weeks, some individuals have displayed a fervor for adopting AI without a clear purpose.”
Singla emphasizes the importance of identifying the primary business challenges that need resolution. His team’s initial approach involves assessing the organization to determine if AI can effectively address these challenges and deliver desired outcomes.
Thus, he elaborated, “Our evaluation of the overall opportunity is grounded in a business context.” The subsequent step involves delineating the role that AI plays in capturing a certain percentage of this opportunity.
Being proactive in adopting cutting-edge technologies is crucial, according to Singla, who factors in the organization’s business context when evaluating the value proposition. Whether a business operates on a multinational, regional, or national scale significantly influences decision-making.
Businesses are prompted to evaluate their existing infrastructure, questioning the adequacy of their current systems, cloud integration, and data architecture.
Throughout collaborative engagements, clients have expressed diverse requirements, from enhancing chatbot functionality using advanced AI to transforming online customer interactions.
Singla and his team craft a strategic roadmap and value proposition for businesses within a timeframe of four to six weeks, or a maximum of eight weeks. Subsequently, he encourages clients to take decisive action, viewing the process as a continuous learning journey.
He delineated three primary ways in which businesses can leverage AI to enhance their Profit and Loss (P&L) metrics.
Singla elucidated, “Firstly, enhancing performance may lead to a reduction in workforce. Secondly, improving efficiency could delay hiring plans. Lastly, boosting sales targets may necessitate additional hiring in sales functions.”
While acknowledging the innovative strides being made, Singla cautioned against overlooking the tangible impact on the P&L. He recounted a recent interaction with a healthcare provider, emphasizing the importance of aligning finance, HR, and revenue functions to establish a cohesive framework for financial management and performance evaluation.
Consider the scenario of software development progress. Singla highlighted the success seen with clients utilizing Microsoft’s Copilot program to expedite code development, resulting in efficiency gains of 20% to 40%. This efficiency boost does not imply a corresponding reduction in workforce but signifies an enhancement in productivity through script modernization.
Singla collaborates with clients to revamp customer experiences, spanning from claims processing to insurance policy issuance, with a predominant focus on the healthcare sector. For instance, implementing relational AI tools could elevate claims processing capacity from 10 to 15 claims per day, potentially leading to workforce optimization.
“There’s a Hint of FOMO,” she remarked.
Through a blend of recruitment, acquisitions, and training initiatives, Accenture aims to double its cadre of AI practitioners in the Data and AI domain over the next three decades.
When contemplating AI investments, clients often seek clarity on the rationale behind their investments and the optimal timing to dive into AI initiatives. The Fear of Missing Out (FOMO) is palpable among certain clients, prompting them to seek insights on industry benchmarks and best practices.
Daugherty emphasized the significance of anchoring the AI strategy in the business scenario. This entails mapping out the industry’s value chain, identifying primary use cases, determining the requisite model types, and evaluating the necessity for process modifications.
He advised a meticulous examination of both cost implications and revenue generation opportunities. Daugherty stressed the importance of identifying use cases that offer tangible Return on Investment (ROI) and are scalable. He underscored the criticality of investing in upskilling initiatives to harness the full potential of AI technologies.
According to Daugherty, the governance framework for conceptual AI systems should align with the organizational structure. Effective communication of this framework from the C-suite to operational teams is pivotal for seamless integration and alignment.
Adopting a standardized approach and fostering flexibility are recommended strategies, especially in the rapidly evolving landscape of generative AI technologies. Daugherty highlighted the imperative of future-proofing solutions to adapt to evolving AI paradigms efficiently.
He cited the example of relational AI applications in HR functions, such as job description generation, as foundational use cases offered by various HR systems providers.
Identifying transformative applications of AI, Daugherty emphasized the potential for life sciences companies to expedite drug discovery processes using conceptual AI. This approach aims to streamline market entry timelines, reduce costs, and enhance transparency in drug development.
Daugherty recounted a collaboration with a telecommunications company leveraging conceptual AI to enhance call analytics, resulting in a 30% productivity surge in call handling and a substantial improvement in customer satisfaction.
Understanding the Core Technology
Sesh Iyer, Managing Director and Senior Partner at Boston Consulting Group (BCG), shared insights from numerous discussions with CEOs regarding conceptual AI technologies. He highlighted the importance of demystifying complex AI concepts and elucidating the underlying technologies to facilitate informed decision-making.
A significant portion of BCG’s engagements involves developing a capabilities framework for relational AI, encompassing a comprehensive assessment of available systems supporting these capabilities.
Iyer recommended exploring software platforms featuring conceptual AI functionalities, such as Git Hub, Microsoft 365, or Salesforce’s Einstein, as an initial step to derive value from AI technologies.
He emphasized the transformative impact of conceptual AI in vertical functions like customer service, finance, and HR, revolutionizing these functions to deliver a seamless user experience.
Clients engaging with BCG are keen on enhancing customer service quality while achieving substantial efficiency gains, with productivity improvements ranging from 25% to 50% through the deployment of virtual agent and agent-assist models.
Singla, Daugherty, and Iyer underscored the paramount importance of responsible AI adoption and data security measures to safeguard sensitive information. Establishing Centers of Excellence for responsible AI practices is envisioned as a common trajectory for most businesses.
Despite apprehensions surrounding conceptual AI, experts unanimously advocate for a robust strategic framework before embarking on AI investments, emphasizing the need for a well-defined game plan despite the urgency of technological advancements.
