The emergence of artificial intelligence (AI), data science, Internet of Things (IoT) stack, and security connect to the issue of a new IT scale. CIOs are seeing a portfolio of immense continuous data, a massive growth in the number of devices connecting to us, and a colossal mix of vulnerabilities and threats — all at a breadth and rate of growth that is orders of magnitude greater than we’ve dealt with in the past. Our technology stacks and portfolios are now a constellation of networked devices, data flow, software, and people that demand different approaches to effectively manage the as-is and to-be states of technology for government and civic organizations.
There are riddles in this transformation that are not solved. That means we will work to harness vendor and partner relationships to cross the chasm, all aiming at providing the essential public services our communities require. The riddles include:
- How do we build a high degree of privacy and security into our solutions?
- How does customer-centric design evolve past a user-interface focus and into workflow, communications and configuration?
- What is the transition path for cybersecurity, training, IT infrastructure, and data risk management into pattern-based platforms that effectively tap the strengths of AI?
- What IoT platforms can emerge to simplify the technologies enough to effectively harness and support cross-domain solutions that define “smart cities”?
- What standards and constructs can leading governments agree on, so as to feed and share data in ways that finally generate better regional action on a consistent basis, starting with disaster management, homelessness, traffic, transportation planning and public safety?
- How do we learn, deploy, verify and correct algorithmic bias?
- As we hire and train staffs to handle them, we will lean on industry partners to support new approaches with an aim at iterative and additive learning. Or, as we call it in San Jose, “Nail it. Then, scale it.”
- We will have to broaden the data analytics and quality assurance capacities of our analysts.
- Our technology engineers will need to learn to use machine learning-based tools that help manage an enormous set of connected devices, compute and storage capacities, and expansion across cloud providers.
- Our cybersecurity analysts will use more deep learning and machine learning solutions to see the signal through the noise in terms of activities in our hardware, software, connectivity and data that could become security incidences or breaches, if not caught quickly.
- Developer and implementation analysts are finding ways to execute customer-centric design and use a mix of traditional and agile methodologies to help teams deliver better processes and software on a consistent basis.
- Product management expertise is weaving into our organizations as we realize every IT asset has a long life and investment pattern, the same as equipment and buildings.
- Other IT engineers will need to grow their skills to be full-stack across devices, connectivity, data flow, data management and applications, all to create the cross-domain solutions that the IoT era is taking us toward.
- And all managers will have to learn how to manage change, implement and re-engineer systems, and skill up their teams on a continuous basis.
For San Jose, we have initiatives for AI in the areas of autonomous vehicles and Bosch and Daimler, IT infrastructure management, and potentially security. Because of the trust-nature of distributed ledgers, we will look for solutions to come through other platforms and solutions we have. We do not see the return on investment to build something "blockchainy" for at-scale use in the use cases we’ve seen or considered; for learning at most.
