Information technology - Insights
Adopt a franchise framework to improve digital delivery
Informed by insights from more than 2,400 CIOs and IT executives, the 2024 CIO Agenda shows CIOs are being tasked with an increased demand in delivering business value from technology initiatives without additional resources. By adopting a franchise model, CIOs can co-own the responsibility of digital delivery with CxOs, overcoming budget and talent restraints and reducing risk.
Avoid these common mistakes made by new CIOs
Transitioning into the role of a CIO or other leadership roles can be one of the most stressful, yet rewarding, career milestones for an individual. Getting transitions right is critical.
Learn to recognize the five fatal pitfalls new CIOs must avoid, so you can enter your new role as a stronger and more effective leader
Download the 2024 CIO Agenda to:
- Discover the franchise approach to digital delivery
- Understand the three pillars to make franchising work
- Develop an action plan with immediate steps to take
Make your organization responsive to new cyber risks
Digital business creates unprecedented cybersecurity risk, and many organizations struggle to balance network security with the need to run the business. The IT Roadmap to Cybersecurity helps chief information security officers (CISOs) learn how they can develop processes that enable risk-based decisions while protecting against cybersecurity threats and prevent data breaches and any other cybersecurity attack. The roadmap provides cybersecurity leaders with: A cybersecurity framework with key stages and milestones Key resources to ensure successful execution Perspectives on the cross-functional teams to support cybersecurity awareness
Craft a Cloud Strategy to Optimize Value
Maximize the benefits of the cloud with a strategy that clarifies the cloud’s role in delivering IT-driven business value. Maximize the Benefits of Cloud Computing Almost every organization uses cloud computing, but many do so without a documented strategy that answers what the organization does in the cloud and why. Without that, you won’t maximize the benefits of working in the cloud. The mandala roadmap, equips CIOs to develop a concise cloud strategy document by following steps to: Align objectives about the cloud Develop a cloud action plan Prepare the organization for execution Establish governance and mitigate risk Optimize and scale
Leverage Mandala's Cloud Strategy Approach
The cloud strategy is a concise point of view on cloud computing and its role in your organization. It should be a short and living document of between 10 and 20 pages. It should work in conjunction with other strategic plans, starting with the organization’s midterm corporate strategic plan, as well as with related strategic plans for the data center, security, procurement and so on.
To effectively guide decisions, the cloud strategy requires support and sponsorship across the organization — including with stakeholders in business units, other technology departments and in functions such as operations, finance, legal and sourcing. Organizations that successfully craft a cross-discipline cloud strategy are more likely to succeed and realize full benefits with their cloud initiatives than those without one.
To formulate that cross-discipline strategy, start by forming a cloud strategy council with members from different teams who can share perspectives from across the organization. Most organizations build their councils to include a variety of IT and functional roles.
Each member of the council has a role to play in crafting the strategy and advocating for it, depending on where they sit in the organization. Procurement, legal and risk management, for example, can assess cloud transitions and highlight their implications. Human resources can help sell the transition to the workforce, and help identify and address cloud skills requirements. Finance can assess the financial implications of the cloud strategy and approve the savings and efficiencies.
Why does your organization need to define artificial intelligence
Mandala defines artificial intelligence (AI) as applying advanced analysis and logic-based techniques, including machine learning (ML), to interpret events, support and automate decisions, and take actions. This definition is consistent with the current and emerging state of AI technologies and capabilities, and it acknowledges that AI now generally involves probabilistic analysis (combining probability and logic to assign a value to uncertainty). Other organizations and individuals may use different definitions. There is no single, universally accepted descriptor for artificial intelligence as there is such a wide range of ways in which AI can support, augment and automate human activities, and learn and act independently. Leave room for differences of opinion, but make sure that business, IT and data and analytics leaders don’t fundamentally disagree about what AI means to the organization or you will be unable to design a strategy that captures the benefits. Note that AI technology vendors are also likely to have their own definitions of the term. Ask them to explain how their offerings meet your expectations for how AI will deliver value.
What are machine learning and deep learning
Machine learning is a critical technique that enables AI to solve problems. Despite common misperceptions (and misnomers in popular culture), machines do not learn. They store and compute — admittedly in increasingly complex ways. Machine learning solves business problems by using statistical models to extract knowledge and patterns from data. Machine learning is a purely analytical discipline. It applies mathematical models to data to extract knowledge and find patterns that humans would likely miss. ML also recommends actions, but it does not direct systems to Take action without human intervention. More specifically, machine learning creates an algorithm or statistical formula (referred to as a “model”) that converts a series of data points into a single result. ML algorithms “learn” through “training,” in which they identify patterns and correlations in data and use them to provide new insights and predictions without being explicitly programmed to do so. That said, machine learning is at the core of many successful AI applications, fueling its enormous traction in the market.
Roadmap: The Future of Digital Government Strategy
Seventy-two percent of digital government programs accelerated in response to pandemic demands, but most are still optimizing existing services and programs. This delivers clear benefits, such as cost savings and increased transparency, but government CIOs tend to confuse this type of digital progress with maturity. Transitioning to Digital Government shares major insights to the public sector leaders on how to avoid common pitfalls and lead smart, effective digital transformations to prepare for the future of government.
What are the main emerging AI techniques
The key emerging techniques, in descending order of maturity are:
- Natural language processing (NLP). NLP provides intuitive forms of communication between humans and systems. NLP includes computational linguistic techniques (symbolic and subsymbolic) aimed at recognizing, parsing, interpreting, automatically tagging, translating and generating (or summarizing) natural languages.
- Knowledge representation. Capabilities such as knowledge graphs or semantic networks aim to facilitate and accelerate access to and analysis of data networks and graphs. Through their representations of knowledge, these mechanisms tend to be more intuitive for specific types of problems. Adoption of knowledge graph techniques has accelerated quickly over the last three years.
- Agent-based computing.This is the least mature of the established AI techniques, but it is quickly gaining in popularity. Software agents are persistent, autonomous, goal-oriented programs that act on behalf of users or other programs. Chatbots, for example, are increasingly popular agents.