To scale AI, you need a platform business model. Here’s why.

Digital transformation has become essential in every industry today. But while we see it gaining traction, we need to step back and see if it is reaping the benefits it promised.

A report by Boston Consulting Group estimated that 70% of digital transformation projects fail to achieve the goals they set out for. While the number is pretty significant to be ignored, it is worth questioning why most projects fail, and only a few succeed.

It would be easy to categorize successful projects with reasons like better budgets, “born-digital,” clearer goals, or better strategies. But where the difference actually lies is where businesses are in terms of digital maturity and tech intensity.

Walking the fine line

How extensively do employees use technology? Are they able to drive innovation? How much of a role does technological proficiency play in achieving business goals? Tech intensity essentially answers these questions – it covers how much dependence a business has on the latest technologies and how it has been helping in achieving business goals. The more companies invest in worthy tools and upskill their employees, the higher the tech intensity.

Why does it matter? Because it directly impacts productivity, which results in higher levels of innovation and revenue. This is also why achieving a higher level of tech intensity should be a key goal for businesses looking to reap benefits from digital transformation.

Grow digital maturity

So how do businesses increase their tech intensity? The answer lies in implementing systems, solutions, and frameworks that help them grow their digital maturity levels. This includes altering organizational structures, processes, architecture, and deployment processes that fit the volume of innovation expected. It also helps to stream and manage data better to draw insights and bring sanity to workloads, following predictive maintenance and management powered by AI and Machine Learning (ML). And for this to happen, businesses need a whole, different model and ecosystem at play.

Modeling Digital Transformation

Modeling digital transformations

A recent study on Democratizing Transformation delved into this concept and found that most companies trying their hands with digital transformation are still at the traditional and bridge stages of digital maturity. This means their business units are still siloed, data is still not relayed in real-time between business units, data platforms are still not unified, and data-driven innovation is still a far-away concept.

Businesses need to get the most from digital transformation to reach and imbibe a platform model instead, to evolve into a native model as the next, future-ready step.

The stages of Digital Maturity

Digital maturity is made up of organizational structure, process, tech architecture, and tech deployment. How does your company stack up?

Under this model, businesses see data hubs merging into a comprehensive, cohesive foundation that helps businesses deploy AI-based applications quickly and easily. Under this model, companies focus on increasing their data engineering capabilities and integrating machine learning models for accurate analytics and predictions. Businesses also simplify operational tasks under this model with automation and AI-based predictions that enable innovation. 

So, where do businesses start with imbibing the platform model? Here is what businesses need.

  • A data lifecycle approach
  • Unified infrastructures for storage, networking, and compute
  • Clear strategies for cloud transformation
  • Centralized systems for data sanitation and analytics
  • Performance-oriented monitoring
  • Governance and testing for deployments
  • We’ve been helping businesses meet all these requirements for over 20 years. Talk to our experts to find out how.

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    Going the platform route

    One way to look at it is by splitting the efforts into three key fragments— architecture, technology, and expertise.

    Platform Model Architecture

    Architecture

    With most businesses moving towards a hybrid environment that relies heavily on cloud-based and on-premises applications, it is critical to have an architecture that can work seamlessly. One of the vital aspects of embedding seamlessness is ensuring a smooth flow of data to share, ingest, integrate, and normalize.

    Apart from this, they need to practice the insights from data analytics to make processes efficient, cut out repetitive processes, and save costs. Solutions for AI, Machine Learning, IoT data pipelines, and data engineering can make a difference here. Additionally, businesses need to look at security and governance across different environments to standardize the experience for business functions and employees.

    There are quite a few things businesses can try here. One critical route businesses can take is streaming with solutions like Amazon Managed Streaming for Kafka (Amazon MSK). This fully-managed solution makes it easier for businesses to manage high volumes of workloads like Apache Kafka to make data ingestion and process streaming better.

    Businesses also need to alter their process models to work for different environments and get data ingestion from various applications across on-premises, cloud, and the edge. The other level where businesses need to put effort is to add a layer of intelligence with AI and machine learning from IoT devices that moves seamlessly to devices. This is where data engineering and AI analytics solutions can make a massive difference.

    Technology

    For a platform model to work effectively, businesses must invest in the right technologies and easy-to-use, self-maintained, and customized tools. These technologies also need to be easily scalable across environments.

    The problem for businesses today is the saturation of Software-as-a-Service and platform-based technologies, with most of them coming with complexities for usability and deployments. What businesses need are technologies that rank high in user experience while the data from these are easily movable across environments for analytics.

    Platform Model Technology

    As-a-service solutions and DevOps models can go a long way here, as they are scalable and easily integrated. DevOps can bring in the agility businesses need by breaking silos to help teams communicate, collaborate, and plan to build products faster with the best practices needed.

    Expertise

    Identifying how businesses need to implement a platform model that suits their business is just one part of the process. The most critical part of the business model evolution is finding a partner who understands a business's unique needs, existing infrastructure, the right strategic roadmap, and solutions. These don’t just need to help a business meet its current goals but also lay the foundation for years ahead. And this is not an easy feat. There is a lot to consider while evolving towards a platform model approach, from getting the right experts and upskilling employees to finding the right stack, solutions, and infrastructure. Businesses need the right partner that understands what needs to be improved and helps evolve to a native model. However, the selection criteria for the right partner is what businesses need clarity on.

    The partner shouldn’t just help businesses narrow down the right strategies but also guide them on what they need to stay competitive and relevant in the years to come. Our expertise over the past 20 years has been to help businesses implement the right strategies, technologies, and infrastructures they need to make the most of their transformation journeys. With strategic partnerships with experts in the field like AWS, we have expertise in solutions that go a long way to help craft the platform model that businesses need. Additionally, with solutions like AWS Well-Architected Reviews, businesses can see exactly where their AWS ecosystem stands and how it can be improved.

    The competencies we have with AWS give our customers an edge to create a model that works for them and solves each business’ unique problems.

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