AI Transformation: Challenges for Businesses

As we entered 2025, one of the most notable advancements in AI is the emergence of the Chinese-developed model, DeepSeekThis model has captured attention within the AI sector and is viewed as comparable to renowned competitors like OpenAI's latest language modelsThe integration of DeepSeek into platforms developed by tech giants such as Microsoft, Nvidia, and Amazon marked a significant milestone, indicating the growing global impact of this innovationThe approach of making DeepSeek's technology open source not only invites imitation but also promises to reshape the global AI landscape, enabling quicker integration of AI into various applications and devices.

OpenAI's initiatives are also noteworthyEarlier in January 2025, the launch of 'Operator,' an agent capable of autonomously performing tasks like restaurant reservations or checking flight options, signified another leap forwardBy early February, another tool named 'Deep Research' was introduced, which utilizes OpenAI's inference model, enabling the generation of reports in minutes that would previously take hours

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OpenAI emphasized that this innovation is a leap towards achieving Artificial General Intelligence (AGI), which aims to match or exceed human capabilities.

AI's role is evolving from being a mere conduit for information to becoming an autonomous decision-making agent that pushes technology towards deeper interactionsThis dynamic shift represents a transition from 'fast thinking' to 'slow thinking' within machinesHowever, it also raises the risk of humans becoming overly reliant on AI, leading to diminished autonomy, creativity, and judgment—a troubling prospect in the age of the internet's addictive behaviors.

As we forge ahead into this AI-augmented landscape, businesses are increasingly confronted with the necessity of finding and actualizing value from AI technologiesEntrepreneurs must recognize the disruptive potential of AI, which goes far beyond the mere enhancement of production efficiencyThis technology is poised to redefine labor markets, alter productivity patterns, deepen technological authority, and raise serious concerns regarding energy expenditure.

The year 2024 witnessed notable transitions in AI, moving from static outputs to dynamic interactions across text, voice, and visual modalitiesAnticipation surrounds 2025, as it is poised to be a landmark year where AI applications will tangibly assert their value.

AI is infiltrating all operational facets within enterprises, from manufacturing to financial services, from supply chain management to consumer-facing applications

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Its integration will be critical for driving product innovation, boosting productivity, enhancing decision-making capabilities, and redefining customer interactions.

During the recent Consumer Electronics Show (CES), 'edge AI' emerged as a key topic among industry leadersBy running AI algorithms directly on local devices, such as smartphones and IoT devices, businesses can minimize reliance on cloud infrastructure, achieving faster responses, lower costs, and better privacy protectionsCompanies like BMW are adopting 'edge AI' technologies in their vehicles for local driving analytics and navigation optimization, while Ray-Ban Meta smart glasses utilize multi-modal sensing to integrate augmented reality into various sectors, enhancing consumer experiences.

AI is also challenging conventional decision-making frameworks within organizationsPreviously, businesses relied on intuition and experience to navigate operations, but now: smarter, more efficient AI-driven methods are taking precedenceCompanies are harnessing generative AI to optimize hardware supply chains—automatically predicting demand, controlling costs, and managing logistics in ways that render them more resilient and adaptable in times of disruption.

The financial sector is rapidly adapting to AI advancementsFor instance, Goldman Sachs utilizes AI to revolutionize its investment banking practices, reducing the time required to produce IPO prospectuses from weeks to mere minutes—capturing 95% of what traditionally required human insights

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Tools like 'investment banking co-pilot' delve into historical data to provide precise investment guidance, enabling bankers to make forward-thinking decisions within complex market landscapes.

Tech giants are also accelerating their AI strategiesMicrosoft has carved out a dedicated division called 'CoreAI-Platform and Tools,' aiming to integrate all resources associated with AI technologies and foster smart applications within business contextsIndustry leaders like Elon Musk predict that AI will soon dominate cognitive tasks, further proliferating the robotics sector while radically transforming business operations and the global workforce.

The ongoing shift towards AI within enterprises is characterized by various development stages defined as 'AI-in-ALL.'

Enterprises categorized as Stage A are in a phase of observation and learning, focusing on trends and assessing potential AI value.

Stage B involves enterprises where some employees are independently using AI to enhance personal productivityInternal pilots are emerging, focusing on AI-driven optimizations within operational processesAs we head towards 2025, continuous experimentation will characterize this stage with many AI solutions centering on these improvements.

For Stage C enterprises, AI has become an embedded component of their products and services; they now offer enhanced and customized experiences, strengthening their competitive edge while driving product innovations.

Stage D represents organizations where AI is deeply integrated into both internal operations and client interactions, fully embracing the 'AI-in-ALL' directive by propelling a 'process/product/service + AI' approach.

Recent research from the Wharton School indicates that the utilization rate of generative AI among global enterprises surged from 37% in 2023 to 72% by 2024, demonstrating the urgent need for businesses to adapt

Although domestic enterprises still exhibit significant room for improvement regarding AI applications, the momentum is palpable.

When embarking on AI transformation, companies must evaluate the dual aspects of potential AI value versus the feasibility of implementationEnterprises within the 'AI-in-ALL' transformation framework should distill their core business processes based on these two dimensions, prioritizing high-value, easily executable projects.

The AI transformation pathway typically reveals two distinct strategies. 'AI-in-ALL' integrates AI within existing business frameworks to refine processes and elevate efficiency, while 'ALL-in-AI' focuses on reshaping organizational structures, operational models, and business logic around AI to unlock new avenues of growth and value creation.

The ultimate goal of AI transformation should not be solely cost reduction or efficiency gainsEnterprises that regard AI merely as a standardized tool will struggle to achieve a lasting competitive edge; genuine potential is only realized when AI is intricately woven into the fabric of business strategy.

Different discussions surround the potential value of large language models; however, the underlying business model of AI is crucial for determining its success or failureCompanies need to engage in active practice to acquire insights, navigate hidden challenges, and make strides toward AI-driven goals.

When considering AI products and services, organizations should focus on their long-term strategic value

Studies from the Wharton School reveal that empathetic AI chatbots can effectively alleviate feelings of loneliness among users, sometimes rivaling human interactionNevertheless, overreliance on AI may jeopardize genuine interpersonal skillsAs such, businesses must strike a balance between immediate advantages and the prolonged societal impacts of AI deployment, ensuring they enhance user well-being without undermining fundamental human social needs.

Moreover, IT infrastructures will shift from application-centric models to AI agent and data-centered architecturesThe responsibilities of tech leaders to organize teams, manage talent, and oversee IT frameworks will potentially be transformed by generative AI.

The evolution of AI in IT structures will lead to the emergence of a multi-agent architecture, moving away from the traditional model focused on specific applicationsIT leaders will be tasked with guiding numerous different agents, enabling them to communicate internally and interact with the outside world to fulfill complex objectivesFor example, a group of agents might interact with inventory systems, supply chains, and intelligent analytics to monitor stock levels continuously, autonomously generate purchase orders when necessary, and send these requests to relevant suppliers, creating smart integrations and decision-making systems devoid of manual intervention.

Furthermore, the ongoing development of AI technologies is poised to profoundly influence corporate governance and the industrial software market

Recently, SAP announced the integration of DeepSeek into their ERP solutions, while capable enterprises with sufficient demand are also developing proprietary software utilizing large AI models for functions like CRM and HR, promising an increasingly intelligent and diversified software market in the age of AI.

Companies navigating their AI transitions must contend with two major uncertainties: technological evolution unpredictability and business value inconsistencies.

The rapid advancements of AI technologies come with blurred boundaries and varied industry perspectives, complicating any precise forecasts of future trendsLeaders must cultivate vigilant insights and develop adaptable strategies that avoid mere copycat behavior or missing out on emerging opportunities.

The real impact of AI on business remains unclear, lacking comprehensive longitudinal dataCompanies need to strategically select appropriate business scenarios, beginning with pilot projects under the 'AI-in-ALL' umbrella to gradually verify commercial worth and efficacy.

Despite the acceleration of AI applications, high costs and data limitations remain long-term challengesMarket dominance by few corporations could lead to difficulties in significantly reducing AI-related expensesAdditionally, AI's reliance on established human knowledge means that as data is exhausted, model performance may decline, undermining its sustainable value

Companies must weigh costs against benefits to guarantee the long-term applicability of these technologies.

In the process of executing AI strategies, businesses face several critical challenges.

Unclear strategic goals and dispersed resource allocation contribute to inefficienciesA lack of definable objectives results in a scattering of resources and vague priorities, impeding the establishment of sustainable growth pathwaysThere is often a disconnect between technology and business requirements, where too much emphasis on technical capabilities overshadows essential business needs, causing AI solutions to fall short of addressing real problems.

High expectations for AI innovation create misaligned perceptionsWhile AI demonstrates promising incremental improvements, its role in disruptive innovation remains ambiguous; many companies lack breakthrough applications even after an entire year of explorationInadequate digital maturity and pronounced data silos further complicate matters, as inconsistent data quality leads to an inability to meet AI computational demands, negating effective deployment.

Organizational culture and execution capabilities can impede changeResistance to transformation at various hierarchical levels disrupts workflow optimization, and divergences in recognition between management and staff can hinder strategic implementation.

Like any fundamental transformation, the AI transition reflects a 'top-down initiative,' necessitating foresight and a readiness for change

Executives should specifically focus on several key areas:

1. Integrating growth drivers with operational optimization: Executives are increasingly pressured to showcase tangible ROI from AI investmentsBusinesses must orient themselves around market and competition, accurately aligning with customer demand, thereby deeply embedding AI within core operations for better workflows, enhanced experiences, and growth stimulation.

2. Ecosystem collaboration and strategic expansion: While generative AI holds remarkable promise, many leaders struggle to grasp how it can integrate with existing business models and affect the value chain and financial outcomesCompanies should cooperate with technology and supply chain partners to swiftly cover their gaps and utilize industry ecosystems to broaden their boundaries for differentiated benefits.

3. Fostering knowledge innovation systems: AI can deliver insights that transcend human cognitive limitations through deep learning and knowledge automation processes, significantly enhancing efficiency while reducing costsPresently, AI remains an auxiliary asset, with humans retaining primacy in ideation, knowledge integration, and strategic decisions; creating an innovation paradigm led by human needs supported by AI capabilities.

4. Cultivating innovative organization and culture: Companies should encourage a culture focused on innovation and growth, amplifying inter-departmental cooperation while dismantling information silos to facilitate smooth execution of their AI strategy and enhance collaboration agility.

5. Ensuring responsible AI governance: This is a prevalent issue regarding AI applications

Balancing the transformative power of AI with necessary safeguards is critical for realizing societal benefitsLeaders need to explore strategies to set guiding principles that foster growth, manage risks, and unlock the potential of AI for everyone.

6. Human-centric AI management: While AI boosts productivity, it also presents challenges to employee autonomyOrganizations must continuously explore new methods for retraining employees, fostering AI literacy, managing transformations, and promoting collaborative interaction between humans and AI.

7. Leadership commitment to AI and talent strategies: Appointing Chief AI Officers (CAIO) is vital to ensure deep integration of AI into business functions and to develop a workforce adept in utilizing technology across all organizational levels, driving transformation.

8. Progressive and systematic AI transformations are essential: The unlocking of AI's potential consumes time, necessitating a simultaneous focus on software development, employee training, and change management.

Over nearly two decades, the experience accumulated during digital transformation has proven invaluable to contemporary AI shiftsWithout stressing the integration of business and technology, as well as necessary organizational changes, one may find the outcome to be 'old structures + new technologies = just an expensive old business.'

In China, many companies previously disrupted industries in the internet era, but AI is now transforming the business models and competitive frameworks that arose during that time

Failure to keep pace with this AI revolution could see these companies relegated to being labeled simply as 'traditional establishments' within a few short years.

Embracing AI

In the face of technological upheaval, humanity often instinctively resists forces that may unravel established norms.

AI's opacity, emotional detachment, and challenge to human subjective judgment have led to widespread skepticism in societyThe past two years have intensified these anxieties, amid fears of technological unemployment and concerns regarding AI's capacity to supplant human roles and diminish individual worthThis resistance also stems from disrupted interests and the traditional profession's fragility in the face of AI interventions, which threaten established norms.

However, history has shown that technological advancements are irreversible; fears and resistance cannot prevent AI from permeating societyShifting from a mindset of 'man versus machine' is necessaryInstead, we must recognize that AI serves as an efficiency machine that relentlessly improves effectiveness, while humans, being inherently deficient in efficiency, must strive to maintain autonomy, creativity, and judgmentThe contrasting attributes and valuation standards between the two aptly highlight the need for cooperative coexistence.

Executives and employees alike are warned that perceiving AI merely as a tool will only lead to temporary job stabilize

Just like the once-critical prompt engineering roles that depended upon human input for adjustments, advancements in AI have led to self-optimizing capabilities that may render such positions obsolete.

Despite high expectations from business leaders regarding AI, employee hesitance and skills gaps pose barriers to the transition from 'AI-in-ALL' to 'ALL-in-AI.' Successful widespread adoption of AI depends significantly on employee motivation, readiness, and external supportCompanies must provide clear application scenarios and align incentives with training to help employees perceive AI as collaborators rather than adversariesThis means lowering the technological entry barriers and fostering a culture of proactive exploration, transforming employees from passive receivers to active creatorsWe must avoid scenarios in which AI evolves into an overarching tool for workforce replacement while creating an environment dominated by robotic oversight, merely aiding capital in slashing production costs.

The rapid advancements in AI open the door to unprecedented opportunities, yet they inevitably bring forth ethical and environmental challengesScholars like Cornelia Walther from the Wharton School emphasize the concept of 'Prosocial AI,' advocating for AI to transcend mere commercial incentives and focus on societal welfare, thus achieving a harmonious coexistence between technology and humanity.

The principles underpinning Prosocial AI ensure technology genuinely benefits societyThis includes customization for specific societal needs, diversification of training data to mitigate biases, rigorous testing to align AI with social values, and a focused approach tackling measurable social issues like carbon reduction and educational equity to deliver impactful outcomes.

For organizations, pursuing Prosocial AI necessitates integrating societal and environmental objectives into their success criteria, like how Nvidia aids in optimizing renewable energy

Google’s DeepMind has partnered with AI to reduce data center energy consumption, championing sustainability efforts.

The push for inclusivity entails employing diverse data to overcome discrimination pitfalls while implementing accountability mechanisms, as seen with Salesforce's ethics audits aimed at enhancing AI transparency and trustworthinessCompanies like Unilever have embraced AI to optimize supply chains, ensuring a balance between commercial viability and environmental accountability.

Prosocial AI is expected to be instrumental in guiding businesses and society towards a sustainable futureEnterprises must adhere to ethical principles that guarantee AI delivers benefits broadly, fostering beneficial interactions between technology and humanityAs technological advancements continue, it is vital to prioritize AI's energy efficiency and embed green development into strategic planning, ensuring responsibility accompanies progress in technology.

The rapid development of AI profoundly reshapes the cognitive frameworks and decision-making patterns of businesses and individuals alikeNevertheless, excessive reliance on AI poses significant risks of eroding critical thinking and individual judgment, which may lead individuals to function merely under the influence of technologyTo maintain competitive viability in the age of AI, companies and individuals must rationally assess the strengths and limitations of AI, refraining from blind trust while also embracing ethical responsibilities to ensure AI empowers rather than replaces human capabilities.

In an AI-driven commercial ecosystem, human roles are transforming from executors to strategic leaders

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