As our latest installment in this year’s eWEEK predictions, we offer insights from two sources: Dave Padmos of EY Americas, and Diego Oppenheimer, CEO of Algorithmia.
EY makes EY Diligence Edge, an artificial intelligence platform hosted on IBM Cloud and supported by IBM Watson Discovery, designed to revolutionize the mergers and acquisitions due-diligence process. Algorithmia is a machine-learning model deployment and management solution that automates the MLOps for an organization.
Here are insights for 2021 from thought leaders representing two trendy companies.
From Dave Padmos (pictured): The industry-wide migration from traditional to consumption-based sales models for technology companies will continue to accelerate. This is a win-win, because it allows customers to remain current in technology, reducing up-front and switching costs while only paying for what they consume. Technology companies will gain sustainable, recurring revenue streams that generate cash flows to invest in new offerings. Consumption business models are morphing into different forms, including volume, time, pay-as-you-go and event-based fee structures, and the market will continue to evolve in 2021.
A consumption business model requires much stronger collaboration between sales, marketing, external partners and the back office due to the stronger role of data-driven lead generation. While most companies are moving or have moved to consumption models, according to EY research, only 56% of their leaders think their marketing teams are prepared for digital go-to-market activity. To adopt a consumption-based business model effectively, enterprises will retune their sales and marketing functions and transform processes, such as lead-to-cash, financial planning and analysis, revenue recognition and the digital supply chain.
Likewise, sales organizations will rethink how they go to market. We’ll see movement away from a transactional sales model toward ongoing relationship management that is focused on long-term customer success, continued adoption and new partnerships to promote and potentially add value. Success for tech companies has never been more closely linked to the customer’s successful use of the product.
It is critical for SaaS product and sales leaders to develop and offer packages with the same rigor as they develop their software. SaaS companies have lost sales because of poorly conceived bundling strategies. The urgent imperative to create better packaging emerged as a prevalent theme in a recent EY global study of 700 tech companies. Respondents from software companies said their top three challenges in activating a shift to subscription-based sales were adapting pricing models, defining solutions or integrated services and communicating a compelling value proposition to customers. All three of these challenges revolve around having a packaging design that can reinforce value propositions to meet buyers’ needs. In 2021, companies will better identify opportunities to improve revenue performance by preparing a case for package-market fit. Retraining of sales teams will continue to be an ongoing need as these products and offerings continue to be refined.
According to pre-COVID-19 EY research, 60% of corporations said cloud accounted for the largest share of their technology investments in 2018 and 2019, and 53% said cloud will likely account for the largest share of investment over the next two years. During the COVID-19 crisis, with work from home becoming the new normal, IT spending on technologies supporting remote work and investments in cloud is seeing a steep increase. Evolving cloud services present challenges for even mature digital companies to efficiently manage cloud spend while continuing to optimize cost. Cloud is no longer a cutting-edge experiment. It is, in fact, a business requirement, fueling better economics and more innovation at greater speed. Next, enterprises will be looking for cloud-enabled capabilities, such as data analytics, artificial intelligence and robotic software, to cut costs or generate revenues from new services.
The pandemic taught us that it’s crucial that supply chains be as resilient as possible or face the consequences. To understand what a fully transformed supply chain might look like — and the technology needed to support it — more companies will assemble supply chain rapid response teams (RRTs). RRTs will envision multiple scenarios to prepare for disruptions to manufacturing, transportation, parts and components, and decreased or increased demand.
The imperative to reduce supply chain costs will likely be the most pressing concern for companies in 2021. To reduce costs, tech companies will rationalize and optimize their sourcing strategies, product complexity mix, and manufacturing and distribution footprints, transforming a rigid, linear supply chain into an agile, networked ecosystem. Highly scalable scenario planning will become essential to supply chain operators. Additionally, 5G will enable and enhance a variety of smart factory solutions, including maintenance, quality control, and inventory tracking and replacement. For example, companies can use a network of sensors that leverages continuous data inputs to monitor each item from transit through the production process to allow management to better understand hidden downtime risks and improve logistics and yield.
There has never been a time when IT security has been more important, and concerns over data privacy and security will only increase in 2021. Even when using websites they know and trust, 75% of consumers surveyed by EY remain cautious about disclosing personal details and financial information. However, 40% of users are unaware of recent data privacy regulations and their impacts. In the coming year, data collection and storage won’t be the only factors under a microscope. Data disposition will become increasingly critical to an enterprise’s overall governance efforts.
Building a robust and holistic data disposition program requires strategic decision-making and considerations, as well as input and integrations with a variety of key stakeholders. Disposing of data requires data owners, compliance, legal, IT and cybersecurity to work together to develop a strategy that enables the business while simultaneously meeting data disposition and retention requirements.
All companies will need to address big data, a term coined in the 80s that has now come home to roost. The cost of keeping and storing data is becoming trivial; however, the complexity introduced when attempting to understand and utilize data is often the largest impediment to completing a successful digital transformation. Plain and simple, most companies have neglected their data, thereby introducing complexity — not just in managing and understanding the data itself but actually introducing complexity in the underlying, multifaceted systems built to process the data.
The untapped value of data in legacy businesses is made ever more obvious by recent startups emerging as multibillion-dollar entities. Ironically, these startups are providing infrastructure, applications and tools to analyze and gain insight into complex sets, pools and lakes of data on behalf of those legacy businesses.
Moving forward, companies will need to more clearly understand the relationship between their data, business processes and systems. The need for data-specific platforms to provide cost-effective and efficient solutions to enable ecosystems, derive analytics and insights, or even deliver automation and new technologies (e.g., machine learning and blockchain) will be accomplished by architecting efficient data processing platforms and solutions.
Algorithmia’s new report uncovered some key trends for enterprises to focus on as they head into 2021. Here’s a look at some of the top themes in its findings.
From Diego Oppenheimer (pictured): Organizations were increasing their investments in AI/ML before the pandemic, according to Algorithmia’s 2020 report, and the economic uncertainty of COVID-19 has added to the urgency. The 2021 survey revealed that 83% of organizations have increased their budgets for AI/ML and that the average number of data scientists employed has increased 76% year-on-year.
In addition, organizations are expanding into a wider range of AI/ML use cases; the survey found that the percentage of organizations that have more than five use cases for AI/ML has increased 74% year-on-year. Notably, the top use cases that organizations are focusing on are related to customer experience and process automation—areas that can offer top- and bottom-line benefits during times of economic uncertainty.
Organizations are experiencing challenges across the ML lifecycle, with the top challenge by far being AI/ML governance. 56% of all organizations rank governance, security and auditability issues as a concern—and 67% of all organizations report needing to comply with multiple regulations for their AI/ML.
In addition to governance challenges, organizations continue to struggle with basic deployment and organizational challenges. 49% of organizations ranked basic integration issues as a concern, and the survey found that cross-functional alignment continues to be a major blocker to organizations achieving AI/ML maturity.
Despite the increase in budgets and headcount, organizations are now spending more time and resources on model deployment than they did before. Algorithmia found that the time required to deploy a trained model to production increased year-on-year, and that 64% of all organizations take a month or longer to deploy a model. 38% of all organizations are spending more than 50% of their data scientists’ time on model deployment—and organizations with more models spend more of their data scientists’ time on deployment, not less.
The bottom line is, organizations have increased their AI/ML resources without solving underlying challenges with operational efficiency. This has exacerbated the problem and led to organizations spending more time and resources on model deployment.
Algorithmia’s 2021 survey found that organizations see improved outcomes when they use a third-party solution to manage their machine learning operations (MLOps). Specifically, when compared to organizations that build and maintain their own systems from scratch, organizations that either integrate commercial point solutions into their systems or use a third-party platform spend an average of 19-21% less on infrastructure costs. The average amount of their data scientists’ time that’s spent on model deployment is also 22% lower and the average amount of time they take to put a trained model into production is 31% lower.
eWEEK is running a series of prediction articles throughout the month of December.
© 2021 TechnologyAdvice. All Rights Reserved
Advertiser Disclosure: Some of the products that appear on this site are from companies from which TechnologyAdvice receives compensation. This compensation may impact how and where products appear on this site including, for example, the order in which they appear. TechnologyAdvice does not include all companies or all types of products available in the marketplace.