Tag: artificial intelligence

AI as-a-service: Big Tech Has Provided Platforms, But Where Will the Apps Come From? | Sherpas in Blue Shirts

Our digital services research suggests that 40 percent of enterprises have adopted AI in some shape or form. Of course, they’re relying on the foundational platforms from BigTech firms like Amazon, Google, Microsoft, and TenCent – and even from smaller tech start-ups –to drive meaningful business cases.

But while they can leverage Amazon Sage Maker or Microsoft Bot Framework to do the heavy lifting, they still need a meaningful application that operates on the platform in order to solve their business problems.

Enterprise Challenges with AI

Granted, tech vendors like Oracle, Salesforce, and SAP have made initial progress in integrating AI into their application platforms. But their products are very broad and focus on their own planned areas. And enterprises have multiple, complex requirements that fall outside the purview of these generic applications. Therefore, most enterprises must also build their own AI engines to get meaningful insights from these large-scale applications.

Essentially left on their own, enterprises have to build their own applications to address their needs. But Everest Group digital services research indicates that 60 percent of leading digital adopters struggle for the right talent. And because they lack high-caliber AI talent, they can only take scratch some of the surfaces necessary to create truly valuable apps that can deliver specific business outcomes.

Can Start-ups Help?

We believe this leaves the market wide open to an impending burst of start-ups that can build AI-led niche applications to solve industry-specific business problems. Areas like fraud detection in insurance, compliance management in financial services, and industry-oriented employee engagement and customer experience can significantly benefit from these types of applications. But the key to success here – for both enterprises and these start-ups themselves – will be a focus on building applications for specific business use cases, rather than broad-based platforms. Indeed, AI applications focused start-ups need to commoditize the platform and focus squarely on the application logic that leverages AI.

Enterprises will need to partner or invest in these start-ups to incentivize suitable AI-led applications. Going forward these enterprises should focus to procure off the shelf applications to drive business outcomes than over investing in AI platforms. Unlike today, which requires massive bandwidth to build on top of BigTech AI platforms, these applications will be easy to configure, train, and consume.

The Role of System Integrators

Given that system integrators (SIs) have a strong enterprise DNA and understand business processes, systems, and technologies very well, they can build these applications for enterprises leveraging a BigTech platform. Some of them have made early inroads in areas such as service desk, customer support, and IT operations. However, there is a massive opportunity for business applications and processes. SIs will need to develop point as well as platform-led AI applications that can be plug-and-play in an enterprise set-up. These applications must be pre-trained on industry-fed data for quick deployment and better time to value.

The Road Ahead

It is apparent that enterprises cannot leverage the power of AI on their own. They need to rely not only on large technology vendors, but start-ups and their service partners as well. Though each enterprise must have a pool of valued AI resources, they should not go overboard in investing in them. As AI is not enterprises’ core business, they’re better off letting it be done by companies that are experts.

However, if the AI industry continues to generate next-generation smarter platforms that are do heavy lifting for AI without creating meaningful applications, we will surely see one more AI winter in the near horizon.

AI Helping DevOps: Don’t Ask, Don’t Assume – KNOW What Users Really Want | Sherpas in Blue Shirts

With DevOps’ core goal of putting applications in users’ hands more quickly, it’s no surprise that many enterprises have started to release and deploy software up to five times a month, instead of their earlier once-a-quarter schedule. Everest Group research suggests that over 25% of enterprises believe DevOps will become the de-facto application delivery model.

However, there continues to be a disconnect between what business users want and what they get. To be fair to developers and IT teams, this disconnect is due, in part, to end-users’ difficulty in articulating their needs and wants.

Enter AI Systems

AI Systems have strong potential to help product management teams cut through the noise and zero-in on the features their users truly find most valuable. Consider these areas of high impact:

  1. Helping developers at run time: Instead of developers having to slog through requirements, feature files, and feedback logs – and likely miss half the input – AI-led “code assister” bots can help them, during the actual coding process, to ensure that the requested functionality is created
  2. Prioritizing feedback: Rather than wasting time on archaic face-to-face meetings to prioritize features requested in the dizzying amount of feedback received from users, enterprises should build an AI system to prioritize requests from high to low, and dynamically change them as needed based on new incoming data
  3. Stress testing feedback: After prioritization, AI systems should help enterprises segregate the features users really want, versus those they think they want. AI can do this by crunching the massive volume of feedback data though machine learning and finding recurring patterns that suggest consensus. The feedback data should also be fed back to business users to educate them on market alignment of demanded and desired features
  4. Aligning development, QA, and production: Through its inherently neutral perspective, an AI system can smooth through the dissonance among the different teams by crunching all the data across the feedback systems to outline disconnects and create the alignment needed to satisfy end-user needs
  5. Predicting features: While this is still far-fetched, enterprises and technology vendors should work toward AI solutions that can predict features that will be requested in the next sprint based on historical data. In fact, AI systems should be able to analyze data across other enterprises as well to suggest relevant features to developers. The predictions could then be validated with real feedback from beta users, and the AI system further trained based on the validations

There are multiple other areas in which AI can potentially assist in understanding what the users want. For example, as we discussed in earlier research, AI can help developers create secure, performance-tuned, and production-ready code without being bogged down by typical feedback on features from the field.

What about Budget?

The good news is such an AI system will not burn a massive hole in enterprises’ budgets and should not require the zillions of data points that most typical, complex AI systems do. I believe these systems can be based on simple log data, performance feedback cycles, feature files databases, requirements catalogues, and other already existing assets. If that’s the case, they have great potential to help enterprises develop software their end-users really want.

Have you deployed AI in your Agile DevOps delivery cycle? I’d love to hear about it at [email protected].

Will AI Take the “H” Out of HR? Not if Done Well | Sherpas in Blue Shirts

Most people talk about how AI will transform both the transactional and strategic HR functions across recruiting, performance management, career guidance, and operations. Technology vendors such as Belong.co, Glider.ai, Hirevue, MontageTalent, and Talla, are often quoted as transforming the HR functions across different facets.

So the burning question here is, will AI technologies eventually transform the HR function for good? Or will it dehumanize it? Let’s look at some fundamental issues.

HR Works within the Enterprise’s Constraints

Focuses on creating individual-centric training, incentives, performance management, and career development plans are noble. However, HR may well not have the budget, and the organization’s processes may well not allow these in reality. Most organizations have a fixed number of roles (bands) and employees are fit into them. And there is a fixed L&D budget, which is treated as a cost that prevents meaningful investment in programs for individual employees.

HR Hardly Understands Technology

Most of today’s enterprises are looking to hire “digital HR” specialists who understand the confluence of technologies and HR. Because very few exist, the businesses themselves need to teach and handhold non-digital HR people about the value of AI principles in their mundane tasks, such as CV/resume shortlisting, as well as in their creative work, such as performance management and employee engagement.

Senior Leadership’s Flawed Perception of HR

While every enterprise claims that their employees are their greatest asset, they don’t always perceive HR to be a strategic function. Many senior executives view HR as a department they need to deal with when team members are joining or leaving the organization, and that everything in between is transactional. This perception does not allow meaningful investments in HR technologies, much less AI-based services. As AI systems are comparatively expensive, they require senior leadership’s full support for business case and execution, and HR will likely not be on the radar screen.

HR’s Flawed Perception of Itself

Most HR departments consider themselves to be recruitment, training, and performance management engines. They fail to strategically think about their role as a crucial enabler of a digital business. Because most HR executives don’t perceive themselves to be C-level material, their view becomes self-fulfilling. Many HR executives also silently fear, that their relevance in the organization will be eliminated if seemingly rote activities are automated by AI.

I believe that AI systems provide tremendous opportunities for HR transformation – if the HR function is willing to transform. It needs to make a strong business case for adopting AI based on hard facts, such as delay in employee hiring, number of potential candidates missed due to timelines constraints, poor retention because of gaps in performance management, inferior employee engagement due to limited visibility into what they really want, and compliance issues.

However, there is a tightrope to be walked here. As HR is fundamentally about humans, AI should be assisting the function, not driving it. A chatbot, which may become the face of HR operations, is just a chatbot. AI should be leveraged to automate rote transactional activities and mundane HR operations, and help enhance the HR organization.

Unfortunately, many enterprises myopically and dangerously believe that AI should lead HR. Because HR is not about AI, those that do are bound to dehumanize HR and drive their own demise.

HR’s broader organizational mandate will have to change for AI adoption to truly succeed without dehumanizing the function and its purposes. Doing so will not be easy. Various enterprises may take a shortcut, such as deploying chatbots for simple HR operations, to appease their desire for a transformational moniker. But in today’s digital age, these organizations will be short lived. Enterprises that weave AI into their HR functions – akin to ambient technology – to fundamentally enhance employee experience, engagement, and creativity, will succeed.

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