5 Steps to Unleashing the Power of Enterprise-Ready AI
This is the last installment of our 3-part blog series on integrating, automating, and scaling AI in your DevSecOps processes.
The first post described how scaling AI across the enterprise hits the accelerator on innovation, and it quantified the financial and business advantages. The second post summarized the requirements for scaling AI and how Digital.ai helps you meet those requirements.
Now let’s get to the journey itself: what specific actions should you take to maximize leadership-level support and success? Here are the five key steps:
1. Overcome the inertia of sticking with the status quo
Scaling up the use of AI across the software development lifecycle can seem risky, expensive, and potentially disruptive. However, when you begin to quantify the true costs of inaction—including missing out on the very real financial advantages of substantially higher developer productivity, stronger security built into DevSecOps practices, accelerated time-to-market, and an enriched reputation for innovation—the risks of maintaining the status quo are overwhelmingly high. Specifically, calculate the financial gains you will achieve with the transition, assuming even the low end of analyst estimates. For example:
How much cost savings would a 20% reduction in development time from using generative AI coding tools create for your enterprise?
What is the net financial gain of a 10% improvement in code optimization and the ability to spot and correct inefficiencies 10% faster?
What are the cost savings of an application onboarding process that is suddenly 20% faster due to AI-powered automation?
2. Define the benefits and requirements for each stakeholder group
Once you’re convinced that the financial advantages for the enterprise are both tangible and substantial, get specific about the benefits and requirements for all stakeholders: developers, development teams, enterprise architects, IT, operations, and security teams.
How will enterprise-ready AI improve their jobs and job satisfaction? How will it change their roles? What new skills will be required? How will they acquire those skills? What are the timeframes? Addressing these questions upfront is the key to getting the support and budget you’ll need.
3. Embrace the 3 pillars of an AI-centered approach
As described in blog post #2 of this series, which include:
AI-powered software delivery workflows: Automating and accelerating software delivery workflows by utilizing the power of AI.
AI-based code governance: Streamlining release processes, accelerating testing and debugging, and preventing problematic software deployments before they occur.
Predictive intelligence: Applying machine learning algorithms to integrated data sets to enhance team visibility and help managers more accurately predict outcomes to make better decisions.
4. Meet with a Digital.ai consultant
To discuss your specific business goals, current capabilities, and maturity level with AI in DevSecOps, and to explore the possibilities and business value of enterprise-ready AI.
Our professional services experts form a true partnership with your leadership team and DevSecOps organization—centered on open, honest communication. Our tailored advice is based on many years of experience assisting major enterprises worldwide, not just with AI but with all facets of digital transformation.
5. Create your roadmap to AI-Powered DevSecOps for the enterprise
Working with all key stakeholder groups and leadership teams, Digital.ai experts can help you create a detailed roadmap that defines all significant milestones and addresses all the core requirements, including:
Establishing responsible AI policies and governance guardrails
Making sure you have the data you need and that it remains under your control
Creating an AI model that considers your business priorities as well as operational realities
Identifying and implementing automation at every opportunity
Increasing and expanding collaboration among key stakeholder groups
Addressing skill gaps and new skill requirements
At last, an AI use case that’s a win for all stakeholders
AI-Powered DevSecOps stands out due to its ability to benefit everyone it touches, boosting the productivity of the entire software development lifecycle and enabling businesses to accelerate innovation and surpass time-to-market goals. For developers, it streamlines their work, allowing them to focus on what truly matters and pursue their passions. For IT, it unburdens them by predicting infrastructure requirements, minimizing redundancies, and consequently reducing costs. Additionally, it incorporates security and governance seamlessly into the process rather than trying to bolt it on as an afterthought. And for the business, it delivers significant, quantifiable financial advantages and competitive differentiation. Most importantly, it doesn’t disrupt previous workflows, lock businesses into vendor-specific tools and technologies, break budgets, or generate anxiety about unintended consequences.