Techaisle Blog

Generative AI is revolutionizing how businesses operate, offering unprecedented opportunities for innovation and efficiency. As per Techaisle’s research of 2400 businesses, 94% are expected to use GenAI within the next 12 months. Amazon Web Services (AWS) is at the forefront of this transformation, guiding business leaders through the adoption and implementation of generative AI technologies. AWS emphasizes the importance of understanding the potential of generative AI and identifying relevant use cases that can drive significant business value. By leveraging tools such as Amazon Bedrock, AWS Trainium, and AWS Inferentia, businesses can build and scale generative AI applications tailored to their specific needs. These tools provide the necessary infrastructure and performance to handle large-scale AI workloads, ensuring businesses can achieve their goals effectively. Moreover, AWS highlights the critical role of high-quality data in the success of generative AI projects. A robust data strategy, encompassing data versioning, lineage, and governance, is essential for maintaining data quality and consistency, enhancing model performance and accuracy. Additionally, AWS advocates responsible AI development, emphasizing the need for ethical considerations and risk management. Businesses can establish clear guidelines and safeguards to ensure their AI initiatives are innovative and responsible. Real-world success stories, such as those of Adidas and Merck, demonstrate the tangible benefits of generative AI, from personalized customer experiences to improved manufacturing processes. As businesses continue to explore and implement generative AI, they must prioritize adaptability, continuous learning, and a commitment to ethical practices to fully harness this technology's transformative power. AWS is taking a pivotal role in guiding businesses through the adoption and implementation of generative AI by encouraging business leaders to consider the possibilities if limitations were removed.

AWS’ Roadmap for Generative AI Success

Despite widespread GenAI adoption plans, Techaisle found that 50% of businesses struggle to define an AI-first strategy. Most businesses, from small to large corporations, struggle to define specific GenAI implementation strategies. This is particularly evident among small businesses (81%), midmarket firms (45%), and enterprises (41%). As Tom Godden, AWS Enterprise Strategist, said, “The question on every CEO’s mind is ‘What is our generative AI strategy?” To facilitate this journey, AWS outlines a clear roadmap encompassing several key stages: Learn, Build, Establish, Lead, and Act.

In the Learn phase, AWS recommends understanding the possibilities of generative AI and identifying relevant use cases. They offer resources like the AI Use Case Explorer, which provides practical guidance and real-world examples of successful implementations. Moving to the Build stage, AWS stresses the importance of effectively choosing the right tools and scaling. They provide a range of infrastructure and tools, including Amazon Bedrock, AWS Trainium and AWS Inferentia, Amazon EC2 UltraClusters, and SageMaker. These tools help businesses balance accuracy, performance, and cost while developing and scaling generative AI applications.

The Establish phase centers around data, a crucial component for successful generative AI implementation. AWS highlights the need for a robust data strategy that includes data versioning, documentation, lineage, cleaning, collection, annotation, and ontology. This ensures data quality and consistency, which is essential for optimal model training. In the Lead stage, AWS emphasizes the importance of humanizing work and using generative AI to empower employees rather than replace them. They recommend redesigning workflows to leverage AI effectively, adopting successful AI governance models, and preparing the workforce for new roles through upskilling and reskilling.

Finally, the Act phase focuses on building and implementing a responsible AI program to ensure generative AI's ethical and safe use. AWS advises proactively addressing potential risks and challenges, establishing clear risk assessment frameworks, and implementing controls and safeguards to prevent misuse. They also emphasize the importance of providing training and resources to ensure security and compliance teams are confident in the organization's AI practices.

AWS provides a comprehensive approach to guiding businesses through the adoption and implementation of generative AI. AWS helps leaders navigate this transformative technology and unlock its immense potential by offering a clear framework, practical tools, and real-world examples.

Amazon Bedrock: A Comprehensive Platform for Generative AI

Building upon this foundation, Amazon Bedrock emerges as a pivotal tool for businesses seeking to harness the transformative power of generative AI. By providing a curated selection of foundation models and simplifying their implementation, Bedrock empowers organizations to experiment, iterate, and scale their AI initiatives rapidly.

According to recent survey data from Techaisle, the use of Generative-AI is rapidly increasing within SMBs and midmarket firms. The survey found that AI has become a priority for 53% of small businesses, up from 41% in April 2023. Among core-midmarket firms, 87% prioritize AI, up from 75% in April 2023. Similarly, 89% of upper-midmarket firms prioritize AI, compared to 87% in April 2023. Overall, 60% of SMBs and 84% of midmarket firms are either using or planning to use Generative-AI within the next six months.

The survey also found that between 40% and 45% of midmarket firms have developers and architects specializing in AI/ML, DevOps, hybrid cloud, and app modernization. Additionally, between 35% and 45% of these firms plan to increase their investments in Edge computing, Containers, Open-source technologies, app development, and analytics. Most notably, 72% of midmarket firms are increasing their in-house hiring for Generative-AI.

There is no doubt that Amazon AWS has been a cloud leader since 2006. Channel partners are an essential cog in the wheel of success. The AWS Partner Network (APN) is the umbrella under which its global community of partners builds solutions and services for their customers. Over the years, APN has evolved to include an MSP program, distribution program, marketplace channel program, and partner transformation program, amongst many others. Despite the evolution, AWS is not particularly well-known for its partner program, except if the partner is a significant consulting partner such as Accenture, Deloitte, Mphasis, and several others or a technology partner of size, stature, and brand recognition. However, the AWS Partner Network (APN) does include more than 100,000 Partners from more than 150 countries, with almost 70% headquartered outside of the US.

Over the last decade, there has been an industry-wide change in engagement models to support smaller channel partners. Except for top-tier partners, vendors have distanced themselves from direct oversight of channel marketing initiatives, relying on distributors to manage market development resources. The changes have made it more difficult for channel organizations to maintain predictable operational arcs. They have also made it more difficult for vendors to build and nurture high-performance partner networks. As a result, almost every week, we field two questions from the channel partner community. 1/ Does AWS have a partner program for the midsized to smaller partners? 2/ How does its program differ from Microsoft's (and increasingly from Google Cloud) channel partner initiatives?

The questions and reality are on parallel tracks. The overwhelming majority of AWS partners are smaller businesses. AWS has intentionally designed the entry point of its programs to be inclusive of small businesses. For example, consulting or SI partners only need four trained employees, two certified employees, and three engagements with customers. ISVs only need to complete a Foundational Technical Review.

Sandy Carter, Vice President of worldwide public sector partners and programs at Amazon Web Services (AWS), is transforming the program to be inclusive and diversified, at least for the partners focused on the worldwide public sector – government, healthcare, education, not-for-profit, space, federal financials, and energy. Mission, modernization, and migration are the three pillars of partner enablement and empowerment. Mission is not about simply migrating something over or performing an IT function; it is about delivering a business value for the organization, agency, state, or country. There are many examples, such as digitizing a hospital, leveraging supply chain technology to get food to the right place, or leveraging AWS technology to deliver vaccines. Modernization for AWS is about using artificial intelligence, machine learning, and IoT. Finally, migration is more wide-ranging than the other two, with three converging tracks – application migration, mainframe migration, and data-led migration.

Techaisle’s SMB and Midmarket Cloud adoption survey of 3200 midmarket firms and 3000 small businesses globally shows that hybrid cloud has been gaining momentum in small businesses, and is already entrenched in the mid-market firms. Hybrid accounts for 37 percent of cloud using mid-market businesses today, up 28% from 2018, and is expected to capture a lot higher proportion of new spending in the next one year. Midmarket firms are moving from public clouds to hybrid deployments with current hybrid workload at 17%, up from 12% in 2018. The current penetration is the highest in the US but planned usage is highest in Europe and Asia/Pacific.

There is no clear trend on the types of workloads on hybrid environments which shows that most deployments are very specific to a customer’s needs and application delivery partner’s expertise. Typical hybrid workloads include ERP, HR, CRM, finance, operations, IoT, analytics, AI, Machine Learning, SAP 4/HANA deployments, disaster recovery, critical event management, mass storage, cloud security and cloud database. Both Azure and AWS are being used by over 90% of US midmarket firms. Red Hat OpenStack is the preferred private cloud platform for 74% of US firms and Red Hat Cloudforms is the most used cloud management solution by 80% of US midmarket firms followed by VMware vRealize. Hypergrid, Morpheus, platform9 and Scalr are in low single digits. Ansible is being used by most channel partners for orchestration and automation.

Corresponding Techaisle survey with partners delivering cloud solutions to SMBs and midmarket customers reveals that Azure Stack is the most popular platform because of Microsoft’s proactive engagement, powerful and extensive Microsoft ecosystem as well as deep product portfolio. Google Anthos and AWS Outposts are picking up pace. Interesting trend is being seen from AWS partners who are beginning to use Google Anthos instead of AWS Outposts. These partners are not only working with AWS native solutions, but offering cloud solutions which are based around other cloud platforms like GCP, Oracle or Microsoft. Some of these partners prefer to use Anthos because they find it to be more of an open technology and AWS Outposts and can be easily implemented across other environments. It gives them a wider approach in terms of compatibility. They have to pay a fixed amount when using using Anthos which is variable with Outposts. None of the application delivery partners are using tools and technology from only a single vendor. The use of Open Source is dominant.

Another view of the data collected in the survey provides fascinating insight into the extent that midmarket cloud users are willing to align different delivery methods with internal requirements. Detailed analysis and segmentation of data reveals that there are pockets of demand (and overlap in these pockets) that exist for public, private and hybrid models in each segment.

Mid-market businesses
Looking at the mid-market segmentation, we see that larger firms are likely to employ multiple cloud delivery strategies. Overall, 51 percent rely on a single delivery approach for cloud, for example, 31 percent use only private. 29 percent of mid-market businesses use two different delivery approaches, with the most common being a combination of private and public models (but not in a hybrid setting). Firms in these overlap areas are not, on average, larger than those using a single delivery method, but they do face added complexity in that they tend to have more locations.