Techaisle Blog

Xerox, synonymous with photocopying, has embarked on a bold transformation to remain relevant in the digital age. From its humble beginnings as a copier manufacturer to its status as a diversified technology services company, Xerox's journey is a testament to its resilience and adaptability. Groundbreaking innovations punctuate Xerox's history. The introduction of the plain paper copier in the 1950s revolutionized document reproduction, and the company's subsequent development of the graphical user interface (GUI) and computer mouse laid the foundation for modern computing. The laser printer, another Xerox invention, further cemented its position as a technology pioneer. However, the rise of digital technology and the decline of traditional printing posed significant challenges to the company.  Xerox began a strategic shift towards IT Services with the 2010 acquisition of Affiliated Computer Services (ACS), rebranded as “Conduent” and spun off as a separate business services division in 2016.  The COVID-19 pandemic accelerated the shift towards remote work and digital document management.  While Xerox saw a decline in traditional office printing, it also identified new opportunities in emerging segments, demonstrating its agility and forward-thinking approach, which should give us all optimism about its future. 

It was my great pleasure to speak with John G. Bruno, Xerox's President and Chief Operating Officer. The conversation covered a broad array of subjects, focusing on Xerox's strategy for Reinvention. 

The traditional office environment has undergone a seismic shift with the rise of remote and hybrid work models. Once synonymous with physical document management, Xerox is adapting to this new reality by strategically balancing the preservation of its core print business with a bold venture into Digital Services and IT Services. While print remains a critical component of Xerox's operations, the company recognizes the growing demand for digital tools. It is investing heavily in technologies that can extract value from documents in a digital format. 

Xerox's foray into digital services is driven by the understanding that information is increasingly digitized. The company is developing services to capture, process, and analyze content from various sources, including physical documents. By doing so, Xerox aims to position itself as a trusted partner for businesses seeking to optimize their document workflows and extract valuable insights from their data. 

Furthermore, Xerox is expanding its service offerings to include IT services, particularly for small and medium-sized businesses. By providing a comprehensive suite of IT services, including managed security and cloud solutions, Xerox is addressing the growing technology needs of this market segment. This strategic move diversifies the company's revenue streams and strengthens client relationships. In essence, Xerox is evolving from a hardware-centric company to a technology-driven organization that empowers businesses to navigate the digital landscape. 

The company's Reinvention strategy is threefold. This three-pronged approach demonstrates Xerox's commitment to preserving its core print business, simplifying its operations to improve the client and employee experience, and capitalizing on the opportunities presented by the digital revolution. 

1. Strengthening the Core Print Business:

Xerox aims to maintain its leadership in the print industry by focusing on efficiency and productivity, reducing costs, and capturing growing segments like home office printing and production print. Despite the rise of digital platforms, print remains a significant market, and Xerox is determined to solidify its position as a leader in this space. As remote work and hybrid work models become the norm, the demand for home printers is expected to rise. Xerox is positioning itself to capitalize on this trend by offering high-quality, user-friendly printers to address the evolving needs of hybrid workers.   On the other end of the spectrum, Xerox also focuses on production print. This segment caters to businesses with high-volume printing needs, such as publishing houses, advertising agencies, and direct mail companies. Xerox aims to increase its market share in this lucrative sector by investing in advanced printing technologies and workflow solutions. 

2. Driving Efficiency and Growth through Global Business Services:

A key component of the Xerox Reinvention is the formation of a new Global Business Services organization. By centralizing internal processes and leveraging shared capabilities, Xerox aims to simplify operations, reduce costs, and improve the overall client and employee experience. This, in turn, frees up resources for investment in growth areas, such as emerging technologies and digital services. As Xerox continues to evolve, the Global Business Services organization will play a pivotal role in ensuring the company's long-term success. 

3. Expanding into Digital Services and IT Services:

Recognizing the digital transformation, Xerox is investing heavily in digital services, including intelligent document processing, content management, and data capture. The company also sees significant potential in IT services, particularly for small and medium-sized businesses. Recognizing the inevitable shift towards digitalization, Xerox is increasingly investing in digital services. At the heart of this strategy is intelligent document processing, which involves extracting valuable information from physical and digital documents. This technology is crucial for businesses looking to automate workflows, improve efficiency, and gain insights from their data. In addition to document processing, Xerox is focusing on customer engagement services, whereby Xerox helps companies utilize proprietary content to more effectively target and communicate with their customers, even designing and implementing omnichannel marketing campaigns. Xerox entered the IT services market to expand its digital footprint, particularly targeting small and medium-sized businesses (SMBs). By offering a range of IT services, including managed security, cloud solutions, and technical support, Xerox aims to become a one-stop shop for SMBs' technology needs. 

Techaisle’s recent survey of over 2100 businesses shows that 53% of midmarket firms have shifted their focus to smaller AI wins as they result in reduced risk, faster ROI, enable flexibility, build trust and capability, and target specific immediate pain points. These early wins can serve as a springboard for more significant, more ambitious AI initiatives, ultimately driving long-term growth and success. This trend was first highlighted on July 31st during a podcast recording, where I was asked about the specific AI trends Techaisle and I were watching. My response was clear: small wins. This insight was grounded in our data-driven research, and the evidence presented in this article further supports this conclusion.

Pursuing smaller, more manageable AI projects is increasingly becoming the preferred strategy for midmarket firms. This shift is primarily driven by a series of significant roadblocks hindering the widespread adoption of AI.

A staggering 82% of midmarket companies cite cost and a lack of sufficient investment as primary obstacles. The substantial financial commitment often required for large-scale AI initiatives burdens these organizations considerably. Additionally, 63% of midmarket firms grapple with insufficient technology infrastructure, highlighting the need for robust IT systems to support AI applications.

Uncertainty also plays a significant role. 59% of midmarket companies express a lack of clarity on AI implementation, underscoring the complexity and challenges associated with integrating AI into existing business operations. Furthermore, trust and security concerns, cited by 51% of respondents, pose substantial barriers to AI adoption. The sensitive nature of data and the potential risks associated with AI systems have led to a cautious approach among many organizations. Finally, data quality and accessibility remain critical challenges. 38% of midmarket firms struggle with a lack of curated data and the inability to ingest quality data, hindering AI model development and performance. These collective challenges have compelled midmarket organizations to adopt a more pragmatic approach to AI. By focusing on smaller, more attainable projects, these firms can mitigate risks, accelerate time-to-value, and build momentum while addressing the limitations imposed by these roadblocks.

Techaisle data shows that while the preference for small wins is consistent, there are notable differences in the intensity of this preference across vertical industries.

Once confined to the realm of science fiction, intelligent machines and artificial intelligence are now rapidly reshaping our world. AI streamlines tasks and boosts efficiency across industries, from personal productivity to complex professional operations. The boundary between imagination and reality blurs further as technology advances, with AI-powered devices becoming increasingly accessible.

AI PCs have emerged as the latest technological sensation, generating significant excitement in the industry. The prevailing narrative suggests that AI capabilities will soon become a standard feature in higher-end personal computers. HP, a long-standing leader in the PC market, is not just a participant but one of the driving forces. It recognizes the immense potential of AI PCs, particularly for running generative AI applications. These applications offer a compelling alternative to cloud-based solutions, boasting faster processing speeds, enhanced security and privacy protections, and lower implementation costs.

HP's commitment to AI extends beyond hardware. To ensure widespread adoption, the company is investing in comprehensive training programs for its partners and sales teams through role-based training programs. Additionally, platforms like the HP Workforce Experience Platform (HP WEX) are being developed to optimize the user experience and unlock the full potential of AI PCs.

HP’s AI PC Innovations: Leading the Charge

A shift towards AI PCs is at the heart of HP’s innovations. While AI in PCs isn't new—having AI-powered features like speech and face recognition, natural language processing, and predictive text—the rise of large language models and generative AI has changed the market. With advanced neural processing units (NPUs) combined with powerful CPUs and GPUs, AI PCs can handle even the most complex and resource-intensive tasks. These intelligent machines go beyond traditional computing, collaborating seamlessly to boost productivity across various industries. The essential advantage of AI PCs lies in their ability to run AI applications directly on the device, offering significant benefits: faster processing, lower costs, and enhanced privacy and security.

HP made its first foray into the AI PC market with the AI PC portfolio announced at the company’s 2024 Amplify Partner Conference (APC). It introduced HP Elite and Z HP PCs with Intel Core Ultra 5 and 7 processors or next-gen AMD Ryzen PRO processors. However, the announcements made in May propelled them into direct competition with its rivals. The company introduced the HP OmniBook X AI PC and HP EliteBook Ultra AI PC. These PCs, dubbed HP's first next-generation AI PCs, are built from the ground up with the latest ARM architecture and are designed and engineered around the Snapdragon X Elite processor, featuring a dedicated NPU capable of 45 trillion operations per second (TOPS). HP touted these devices as the world’s thinnest next-gen AI PCs at APC.

The landscape of GenAI is rapidly evolving, and midmarket firms are striving to keep pace with this change. New data from Techaisle (SMB/Midmarket AI Adoption Trends Research) sheds light on a fascinating trend: adopting multiple large language models (LLMs), an average of 2.2, by core and upper midmarket firms. Data also shows that 36% of midmarket firms are piloting with an average of 3.5 LLMs, and another 24% will likely add another 2.2 LLMs within the year.

The survey reveals a preference for established players like OpenAI, with a projected penetration rate of 89% within the midmarket firms currently adopting GenAI. Google Gemini is close behind, with an expected adoption rate of 78%. However, the data also paints a picture of a dynamic market. Anthropic is experiencing explosive growth, with an anticipated adoption growth rate of 100% and 173% in the upper and core midmarket segments, respectively. A recent catalyst in midmarket interest for Anthropic is the availability of Anthropic’s Claude 3.5 Sonnet in Amazon Bedrock.

This trend towards multi-model adoption signifies a crucial step – midmarket firms are no longer looking for a one-size-fits-all LLM solution. They are actively exploring the functionalities offered by various models to optimize their specific needs.

However, the data also raises questions about the long-term sustainability of this model proliferation due to higher costs, demand for engineering resources (double-bubble shocks), integration challenges, and security. Additionally, market saturation might become a challenge with several players offering overlapping capabilities. Only time will tell which models will endure and which will fall by the wayside.

Furthermore, the survey highlights a rising interest in custom-built LLMs. An increasing portion of midmarket firms (11% in core and 25% in upper) will likely explore this avenue. In a corresponding study of partners, Techaisle data shows that 52% of partners offering GenAI solutions anticipate building custom LLMs, and 64% are building SLMs for their clients, indicating a potential shift towards smaller specialized solutions.

Why Multi-Model Makes Sense for Midmarket Firms

The journey from experimentation to full-fledged adoption requires a strategic approach, and many midmarket firms are discovering the need to experiment with and utilize multiple GenAI models. There are several compelling reasons why midmarket firms believe that a multi-model strategy might be ideal:

Specificity and Optimization: Various LLMs specialize in different tasks. Midmarket firms believe they can benefit from a multi-model strategy, using the best-suited model for each purpose. This may enhance efficiency and precision across a broad spectrum of use cases. Since GenAI can reflect biases from its training data, a multi-model approach also serves as a safeguard. Combining models informed by diverse datasets and viewpoints ensures a more equitable and efficient result.

Future-Proofing: LLMs are rapidly advancing, offering a stream of new features. Without a visible roadmap from LLM providers, midmarket firms hope to benefit from using various models to stay current with these innovations and remain flexible in a dynamic market. As business requirements shift, a diversified model strategy enables modification of their GenAI tactics to align with evolving needs. This strategy permits businesses to expand specific models to meet increasing demands or retire outdated ones as necessary.

Despite the benefits, midmarket firms are also experiencing challenges

High Cost: LLMs have a high price tag, particularly for smaller midmarket companies. Creating and maintaining an environment that supports multiple models leads to a substantial rise in operational expenses. Therefore, a small percentage of midmarket firms are conducting a thorough cost-benefit analysis for every model and optimizing the distribution of resources to ensure financial viability over time. Managing and maintaining multiple LLMs is time-consuming, as different models have varying data formats, APIs, and workflows. Developing a standardized approach to LLM utilization across the organization has been challenging, and a lack of engineering resources has surfaced.

Specialized Skills: Deploying and leveraging multiple LLMs necessitates specialized skills and knowledge. To fully capitalize on the capabilities of a diverse GenAI system, it is essential to have a team skilled in choosing suitable models, customizing their training, and integrating them effectively. Midmarket firms are investing in training for their current employees or onboarding new specialists proficient in LLMs.

Integration Challenges: Adopting a multi-model system has benefits but can complicate the integration process. Midmarket firms are challenged to craft a comprehensive strategy to incorporate various models into their current workflow and data systems. The complexity of administering and merging numerous GenAI models necessitates a solid infrastructure and technical know-how to maintain consistent interaction and data exchange among the models.

Midmarket Firms Intend to Adopt DataOps to Develop GenAI Solutions Economically

While large enterprises have shown how effective DevOps can be for traditional app development and deployment, midmarket firms notice that conventional DevOps approaches may not fit as well for emerging AI-powered use cases or GenAI. Techaisle data shows that only half of the midmarket firms currently have the necessary talent in AI/ML, DevOps, hybrid cloud, and app modernization. Although DevOps is great for improving the software lifecycle, the distinct set of demands introduced by GenAI, primarily due to its dependence on LLMs, poses new hurdles.

A primary focus for midmarket firms is ensuring a steady user experience (UX) despite updates to the foundational model. Unlike conventional software with updates that may add new features or bug fixes, LLMs are built to learn and enhance their main functions over time. As a result, while the user interface may stay unchanged, the LLM that drives the application is regularly advancing. However, changing and or even swapping out these models can be expensive.

DataOps and AnalyticsOps have emerged as essential methodologies tailored to enhance the creation and deployment of data-centric applications, much like those powered by GenAI. DataOps emphasizes efficient data management throughout development, ensuring the data is clean, precise, and current to train LLMs effectively. Conversely, AnalyticsOps concentrates on the ongoing evaluation and optimization of the GenAI applications' real-world performance. Through persistent oversight surrounding user interaction, DataOps and AnalyticsOps empower midmarket firms to pinpoint potential enhancements within the LLM model without requiring extensive revisions, facilitating an incremental and economical methodology for GenAI enrichment. Ultimately, midmarket firms are considering adopting DataOps and AnalyticsOps with a strategic intent to adeptly handle the intricacies inherent in developing GenAI solutions. By prioritizing data integrity, continuous performance assessment, and progressive refinement, these firms hope to harness GenAI's capabilities cost-effectively.

Final Techaisle Take

The success of GenAI implementation probably hinges on a multi-model strategy. Firms that effectively choose, merge, and handle various models stand to fully exploit GenAI's capabilities, gaining a considerable edge over competitors. As GenAI progresses, strategies to tap into its capabilities must also advance. The key to future GenAI advancement is employing various models and orchestrating them to foster innovation and success.