In 2025, adapting, refining and pivoting strategies will not be a matter of choice, but rather a necessity for survival and expansion for companies. Spending on technologies that support digital transformation is expected to reach 3.9 trillion dollars by 2027.
The figure shows the continued increasing effort by companies in this field. The road ahead is not simple, however, studies indicate that almost 70% of digital transformation endeavors fail due to mismanagement, unsupporting corporate culture, and vague goals.
Take the example of General Electric (GE). Once regarded as an industrial innovations leader, GE pursued a strategy of investing heavily into a digital unit with the hopes of transforming its operations and products. The project turned out to be underwhelming as a result of overly optimistic demand forecasts and internal pushback, and serves as a story of what not to do for other businesses with similar objectives.
Getting your digital transformation strategy right can lead businesses towards endless possibilities and provide a competitive advantage. Adopting a digital transformation strategy is not the challenge, rather mastering it is.
Ana Simion
Formulating digital transformation framework to achieve competitive advantage
In simple terms, digital transformation can be described as the integration of digital technologies in every aspect of a business.
This includes the modification of business processes and the manner in which value is provided to clients. Does this enhance competitive advantage? When done effectively, it enables companies to gain the following benefits:
- Operational excellence: Companies can use digital tools and platforms to automate the workflows, minimize unnecessary processes and increase productivity, thus improving operational effectiveness.
- Improved customers’ lifetime value (CLV): Digital and personalized customer interactions, along with extensive data analytics, help foster strong customer relationships, increase customer satisfaction, and consequently boost profitability in the longer run.
- Being the first to market: The adoption of groundbreaking digital solutions permits companies to claim a portion of the market and establish themselves as industry leaders at the same time.
For organisations to digitally transform efficiently, they can leverage pre-existing models, such as the McKinsey 7S Model. This model focuses on seven interrelated elements Strategy, Structure, Systems, Shared Values, Skills, Style, and Staff which help ensure alignment in the processes of transformation.
With the assessment and realignment of these components, businesses can develop an infrastructure that facilitates digital embedding.
Furthermore, the MIT Sloan Digital Business Model framework offers important analyses by investigating the rebalancing of important relationships: Minds and Machines, Products and Platforms, and the Core and the Crowd. This strategy helps the reconfiguration of a company’s primary strategies to make the most out of the digital economy.
As the digital world advances, no industry leader will dispute that for one to be competitive, the core of business strategy has to be based on digital transformation. Essentially, as McKinsey & Company noted, to succeed in digital transformation one has to make trade off decisions that will aid in the reinvention of the business.
Alleviating the complexity of digital transformation is possible when these frameworks and insights are adhered to, in turn facilitating the fostering of sustainable competitive advantage alongside the growing digital landscape.
Marisa Garanhel
Top trends with strategic cases and metrics
Integrating AI and machine learning
Strategy case
Many businesses now utilize AI for productivity growth. For example, AI is applied in writing codes, composing content, and even doing workflow management which tremendously increases efficiency.
Companies that have AI integrated into their systems report a ROI that is almost 2 times higher than those who apply AI platforms for specific tasks and functions only. Moreover, 92% of large companies report achieving returns on their deep learning and AI investments.
Real life example
The emergence of DeepSeek, a Chinese AI startup, marked a major turn in the AI scene with its advanced open-source AI model called R1. This model competes with the best in reasoning from OpenAI and Google but at a much lower cost.
DeepSeek trained their R1 for under $6 million, using 2,000 less powerful chips instead of the tens of thousands of specialised chips that would cost around $100 million. The launch of DeepSeek's R1 has caused established AI companies in Silicon Valley to revise their plans since it indicates a movement from a focus on raw power to one of reasoning and optimization.
In addition, the fact that the model is open source means that any researcher in the world can examine its architecture and build on it, making a more collaborative approach to AI.
Challenges faced
Most companies find it challenging to determine the ROI on AI spending with only 31% of leaders expecting to be able to measure ROI in 6 months.
Emerging patterns
The increased affordability of AI is shifting investments from hardware to software which is positive for companies such as Salesforce and Microsoft.
Expansion of cloud services
Strategy case
Companies are developing their capabilities in cloud services to enhance their flexibility and scalability. Around 45% of companies are upgrading their cloud infrastructure to facilitate the transformation process.
Metrics
Europe’s largest software company, SAP expects its cloud revenues to be between 21.6 bln euros and 21.9 bln euros in 2025 which will increase further due to the growth of cloud computing.
Real-life example
Airbnb uses cloud services to manage peak demand during holiday seasons by enabling “on-the-fly” scaling of infrastructure for high resource consuming events like Black Friday.
Challenges faced
Cloud cost management is a pain point for 82% of cloud decision makers.
Emerging patterns
People want more sustainable features in cloud services, which are often overlooked.
Mark Crosling
Application of Internet of Things (IoT)
Strategy case
According to IoT Analytics’ 418-page IoT Use Case Adoption Report 2024, IoT initiatives appear more successful than ever, as 92% of enterprises report positive ROI from IoT use case implementations.
Metrics
Various studies predict significant growth in IoT, particularly in industries where IoT can help save operational costs by more than 10-20%.
Real-world example
IoT has been used in environmental conservation through advanced sensing methods that promote monitoring and the protection of forest ecosystems. Advanced sensing technologies allow for a notable real world application of IoT.
Devices have gotten smaller and smarter, becoming much more interconnected in the process, transforming data collection to even the most harsh of conditions. Innovations such as Dryad Networks’ wildfire sensors that detect preemptive signs of a fire, Treevia’s digitized dendrometers for tree growth monitoring, and Rainforest Connection’s illegal activity and wildlife monitoring smartphone powered devices are great examples.
Such technologies like Switzerland’s eDNA-collecting drones, Freiburg University’s leaves sensors, Plant-e’s bacteria powered sensors, and seed dropping drones revolutionize forest conservation efforts by providing critical insight and reforestation.
Challenges faced
Protecting the data and the underlying integration issues remain challenges for IoT implementation.
Emerging patternsand
In 2023, the IoT integration market was predicted to be worth USD 3.83 bln, with an anticipated CAGR growth rate of 28.3% between 2024 and 2030.
This growth can be attributed to the rising number of connected devices, smart cities, advancements in AI and ML, a greater emphasis on cybersecurity, and the growing popularity and capabilities of edge computing.
Furthermore, the stronger focus on data-based decision-making is driving investment expansion, which is further aiding the growth of value from IoT platforms. These platforms enable businesses to collect data and provide analysis and visualization tools, permitting real-time decision-making.
The coming years are expected to provide further tailwinds to growth, enabling IoT’s full potential.
Generative AI can help with content creation
Strategy Case
The integration of AI within branding initiatives has automated the creation of effective marketing material like videos, images, and text which improves marketing and communications strategies.
Metrics
92% of Fortune 500 firms have adopted the technology, including major brands like Coca-Cola, Walmart, Apple, General Electric, and Amazon.
Real life example
A mid-sized tech company in Denver, six months after fully adopting AIContentPad, produced 30% more content at 62% less cost, and engagement doubled across key sales channel platforms.
Challenges faced
The inability of Generative AI to produce high quality content and preserving the brand voice are problems that are worth mentioning.
Emerging patterns
There is an increasing trend to deploy Generative AI with customisation to build multiple experiences for single AI model targets.
Digital twin technology
Strategy case
Currently, the engineering and manufacturing sectors primarily use Digital Twins as accurate virtual representations of an object or process simulation. Several publications examine the application of Digital Twins in operational and supply chain management, emphasizing the functions of operations tracking, transport servicing, remote support, asset seeing, and customized design.
Metrics
Organisations that have adopted digital twins have said that the time taken for designing processes was reduced by 30%. They also mention a 25% reduction in expenses related to system upkeep.
Real life example
Altum RF advanced the design of new semiconductor components through the use of a digital twin which enabled them to reduce the design process by 30%.
Challenges faced
The implementation of digital twins will need a considerable initial investment as well as difficulties with data processing from old systems.
Emerging patterns
Progressive cities are increasingly using digital twins for planning as a way to create a simulated city and effectively manage infrastructure and resources. Also, McKinsey research indicates the global market for digital-twin technology will grow about 60% annually over the next five years, reaching $73.5 billion by 2027.
Sabrinthia Donnelly
Robotic Process Automation (RPA)
Strategy case
RPA is considered a developing technology that can speed up business procedures by automating mundane and demanding tasks within the supply chain systems. RPA is also known as software robotics or ‘bots’ and is designed to follow instructions provided by the users in order to execute repetitive tasks in business organisations.
Metrics
Companies that have adopted RPA have achieved process time reduction of up to 80% and a decrease in operational costs by 10%-20%.
Real-world example
New Mexico Mutual incorporated RPA and saved 3.5 hours per day from redundant tasks, which allowed employees to focus on critical higher-value activities.
Challenges
Adaptation of RPA can present issues such as lack of standardization for the processes being automated and opposition from employees fearing job loss.
Emerging patterns
The use of RPA on its own is sufficient, however, we have seen a growth in the combining of AI with RPA to create more advanced automation that is capable of making complex decisions.
What’s next?
It's expected that the merger of AI technologies and 5G by 2025 will radically compress the developmental timeline of digital transformation efforts. Generative AI's ability to automate the process of content creation will allow firms to facilitate marketing, product design, and customer relations on completely different levels.
Consequently, productivity is expected to rise by at least 20% for companies that embrace these tools. Moreover, customer retention rates have the potential to rise up to 15% due to the use of hyper personalization techniques powered by increased data analytics to provide tailored customer experiences.
Adoption of 5G is expected to improve the connectivity of multiple sectors including healthcare, transportation, manufacturing, and more, enabling real-time data collection and analysis through IoT devices.
This, in conjunction with IoT, 5G is set to increase connectivity and make operations more data-centric. As a result, not only will operational efficiency increase, but more innovative developments like smart cities and self-driving cars will become a reality.
Adopting these advancements is expected to increase operational efficiency by 30%, providing a sustainable competitive advantage in this rapidly changing digital world.
