What It Means To Be AI-Ready

15 minute read | 13 Jul 2023
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By Michael Bachman

What you’ll learn:

  • Why AI-readiness matters
  • How people, processes, and platforms play a role
  • Actionable takeaways and how to start now

I recently attended a conference in Atlanta where I spoke in a session with nearly 20 senior executives from diverse companies. It began as a casual discussion about integration in the time of artificial intelligence. From there, it evolved into a concentrated workshop on AI-readiness. For me, it was another example of how business leaders across all industries are eager to improve their AI comprehension, better understand the potential implications for their organizations, and answer one unifying question:

What does it mean to be AI-ready?

Leaders are constantly scanning the horizon, searching for the next game-changing innovation. Today, it’s here. As we’ve discussed throughout our series about preparing for the AI revolution, we’re all standing on the precipice of a systemic transformation. We’re entering an epoch where data has surpassed gold or oil (black gold) in value. Whether you think of data as fuel or precious metal (or even for those who don’t see data as a fungible asset at all), data is your enterprise’s primary currency. And AI has become a sophisticated extraction tool. And where an enterprise lies now on the stage of AI-readiness will determine its place in the future.

AI-readiness signifies having the appropriate people, processes, and platforms primed to incorporate AI into the nucleus of business operations seamlessly. What we came up with in Atlanta was a springboard to develop a full-fledged workshop for the AI-curious. In other words, a learning session for personas within enterprises who think that AI could or will help yet do not know where to begin. Right now, that’s where most enterprises find themselves. They, just like you, are trying to figure out how to incorporate machine intelligence into their processes, services, and operations. More on that a bit later. First, let’s shed some light on some critical factors when considering what it means to be AI-ready.

Get Your Enterprise AI-Ready

Rally Your Team With an AI Vision

The vision from the helm of the ship is crucial. Leaders need to steer their organizations safely toward a new frontier. You must craft a clear roadmap where AI is not an isolated project but woven into the fabric of your strategic plan. In recent months, I have heard dozens of stories about CEOs telling their staff: “We have to use AI to do X?” or “Can’t we use ChatGPT (or Bard) to do Y!” Leaders are justifiably AI-curious, and this type of energy sparks the beginning of a new kind of disruption.

AI, however, isn’t a tool you can just buy off the shelf and start reaping benefits. It requires a unique set of skills. A team of modern-day alchemists – data scientists, engineers, and machine learning experts – must turn your data into gold. But does using AI mean that everyone, or even most people in the organization, need to be experts in alchemy? Absolutely not.

The true value of AI is in the context of how these “golden insights” are applied. Once mined, the gold needs to be fit for purpose. The non-technical professionals in your organization may lack data science expertise but possess the contextual knowledge and experience and are the perfect “artisans” to utilize these insights.

Marketers can mold this data gold into targeted strategies or intricate campaigns. Due to its high conductivity and longevity, engineers can leverage this gold to build enduring, efficient systems for the enterprise’s hardware. Operations teams can turn this data gold into robust, sustainable processes that shield the organization from risk. Leaders can establish standards associated with the financial value of these insights. They can even use their golden insights, like masters of the Japanese art of kintsugi: to repair, heal, and strengthen the organization when necessary.

Reaping the full benefits of AI requires a team of people with the right skill and will to execute the vision. It also includes people who don’t have these skills, too.

Embracing AI calls for a change in your organization’s culture. It’s about preparing your crew for this new journey by preparing them for the shifts in workflows and roles that AI will bring. Fostering a culture of curiosity, openness, and learning becomes pivotal. And this will be necessary for every role in the enterprise, from intern to executive, and facility custodian to data custodian.

Finally, you cannot journey into these uncharted territories alone. You’ll need a strong network of partners and vendors who can guide, support, and even challenge you. Partners may be suppliers, integrators, auditors, advisors, and more. They are the conscripts who are experts in their domains or niche and are a symbiotic extension of your organization.

Learn the five essentials to consider when adding AI to your tech stack with our ebook, API Management in the Age of AI

Making Processes AI-Ready

Infusing intelligence into any process involves understanding your organization’s Process Maturity Ladder – assessing your current, “as-is” processes and your aspirational “to-be” state. From manual processes to automated, intelligent, optimized, and disruptive, the ladder offers a systematic roadmap to climb, guiding us to your destination.

Before thinking about infusing machine intelligence into any process, you must take inventory of current manual processes. This involves mapping out workflows, identifying inefficiencies, and recognizing opportunities for automation and AI to bring significant improvements. It’s crucial to involve key stakeholders, ensure everyone understands what’s at stake, and be ready to take the first step toward change.

With an understanding of manual processes, we pave a path to automation. This might involve using software or hardware systems to perform tasks that previously required human intervention, effectively increasing efficiency and reducing human error. While automation doesn’t inherently require AI, automating manual processes is often a necessary precursor to more advanced intelligent processes.

But as we navigate processes to become AI-ready, don’t misplace your moral compass. AI, while powerful, brings its own set of ethical challenges. It’s your responsibility as a leader to ensure any processes that infuse machine intelligence respect your employees’, partners’, and customers’ rights – refraining from discrimination or bias. This principle underscores the need for Responsible AI, which includes fairness, interpretability, reliability, and safety in AI systems. It’s essential to ensure that AI systems are transparent, inclusive, and respectful to all individuals and communities involved.

Also, the decisions made by AI models should be transparent and understandable to humans – Explainable AI (XAI) – to build trust with users and stakeholders. Further, ethical AI insists on aligning AI with the principles of human dignity, rights, freedoms, and cultural diversity. It includes ensuring privacy and data protection, preventing harmful use of AI, and promoting AI that is free from bias and trustworthy. It’s imperative you infuse these values in the design, development, and deployment of AI technologies. This balance ensures AI initiatives serve humanity and positively contribute to society while driving efficiency and growth.

And in this age of data breaches and cyberattacks, you need to protect your treasure – data. You’re responsible for strong data governance practices that protect individual privacy and ensure compliance with all relevant regulations. This also includes using AI indiscriminately with your corporate IP. Using your data moat to train an AI service or even bringing an open-source AI model in-house could spell disaster for your company’s protected secrets if left unchecked.

While automating manual processes is table stakes for infusing machine intelligence into them, it is imperative to carefully consider governance, oversight, and security to determine whether processes are AI-ready.

Connecting the Context Pipeline To AI

Data is the backbone of AI. But it’s not just about having data. It requires the right data, at the right time, in the right form. That necessitates a sturdy data infrastructure capable of storing, processing, and managing the colossal amounts of data that will power your AI systems. It’s about refining that raw data into valuable insights, creating your own data moat.

Previously, the only way to apply intelligence to data – machine or human – would involve data traversing labyrinthine pipelines of extraction, cleaning, batching, micro-batching, quality controls, and refinement. Fortunately, those days are over. Just kidding! They’re still here. However, relatively new architectural patterns allow you to pipeline data from smaller data sources – even single endpoints – in (near) real-time for machine intelligence to work analytical magic. This provides quite a powerful punch to getting value from data. There are, of course, factors to take into consideration.

Data pipelined through AI-ready processes require platforms built for speed, scale, and security. This means any technical platforms an enterprise may use to incorporate machine intelligence into its processes should have key attributes.

But first, you must know where the data comes from and how it’s generated. This is where a context pipeline starts. It’s about increasing your confidence in the data so that it will provide accurate answers to questions. A context pipeline feeds AI with data relevant to your enterprise that fuels machine intelligence.

For instance, if you rely on a Generative AI like ChatGPT (or another Large Language Model system) to produce answers sourced from only your data, how can you be certain that it’s correct and not just making up information – ( i.e. hallucinating)? Supplementing data trained in Generative AI with other independent data can reduce the possibility of hallucination by providing greater context.

Another common example is if your company has multiple systems of record and the data is not harmonized (or mastered). How do leaders make decisions if the CRM data differs from the marketing data, and they say different things than the ERP system? Which one is right? Maybe none. Maybe all. You may have a data lake. But if the data is not harmonized before getting into that repository, context may be lost in the massive volume of data within the lake. Furthermore, misaligned data may be rooted in the OKRs of the current leadership or, worse, a bygone stakeholder or, worse still, overlooked processes not updated for years.

Being AI-ready also means having the right tech. The computational power, hardware, cloud services, integration, and automation capabilities must be in place to reach your goals. Think of them in terms of Systems of Integration, Systems of Operation, and Systems of Analysis or, in even more contemporary parlance, Systems of Intelligence. My friend Arijit Sengupta, the CEO of Aible, collaborated with me on these systems, and I described them in a book I co-authored called “Modern Enterprise Data Pipelines.”

This triumvirate of systems sources, transforms, and analyzes data in a simple, end-to-end process by supplementing or even eliminating batch processes. As mentioned earlier, enterprises use staging repositories such as data lakes, data warehouses, data lake houses, and data marts so that machine intelligence can develop models for the data. The drawback is it’s difficult to implement real-time changes in those environments.

Fortunately, modern platforms now exist to source data from anywhere, transform data without ETL, and analyze data on the fly without needing to be filtered through a staging repository. Regardless of the platform types you currently use, becoming AI-ready requires doing more than merely accommodating AI. Your strategic plan should be crafted around AI. In other words, you can make your operational systems of record AI-ready when paired with modern integration systems. This coupling ensures getting the most from AI by providing the context pipeline into your system of intelligence.

How Enterprises Can Adopt an AI-Ready Mindset

Stepping into the AI era isn’t just about adopting new technology. It’s a shift in mindset. That was the message from our workshop in Atlanta. What were the specific takeaways? Here is a short list we came up with for the hallmarks of an AI-ready organization. (Remember that this is not an exhaustive list of AI-readiness.)

1. Clear Goals. Know what you want to do with AI. Know what you can do with AI. Know what you can do with AI based on the constraints of your resources and investments. Having the right vision, leadership, and knowledge workers to define, identify, and execute the use case processes and desired outcomes is a GREAT way to start.

2. Good Documentation. Processes must be clearly defined, created, maintained, or deprecated to enable AI-readiness.

3. Accountability and Alignment. Process owners have complete knowledge of and stewardship of their controlled processes. Stakeholders are appointed, assembled, and aligned by the process owner. All stakeholders have a say in the application of machine intelligence. Having accountable and aligned stewards of processes is equivalent to well-defined processes. Only with both is an enterprise AI-ready.

4. Prioritization. There needs to be a hierarchy of defined priorities in multiple dimensions. Some attributions of prioritization include complexity, risk, level of effort, internal v. external services, KPIs or OKRs, skill vs. will, responsible-ethical-sustainable, etc. Re-examining priorities periodically to determine the roadmap of AI-readiness is crucial.

5. Automation. Automating manual processes is essential to creating a context pipeline for intelligence to be infused with data endpoints. The benefits of automation are immense, but attenuating risk, reducing complexity, increasing speed to value, and creating scale is predicated on eliminating manual processes.

6. Data. AI’s power to turn data into information and actionable outcomes is unprecedented. It’s also essential. Access to that data, regardless of its type and the speed with which to access it, transform it, and move it, must be a priority in feeding AI. This includes information system records, metadata, master data, reference data, labels, and logs. Getting AI ready requires a map of where the data lives, why it is valuable, knowing who owns it, and how it’s currently generated, mined, refined, secured, and governed.

Becoming AI-ready is a systemic transformation encompassing people, processes, and platforms. It requires skilled personnel like data scientists, machine learning experts, and non-technical staff who can harness AI insights and apply them to various roles. A culture of curiosity, openness, and learning should be fostered, with strong partners and vendors to provide support. Processes must be mapped, evaluated, and automated where possible while respecting ethical considerations and maintaining robust data governance. Platforms must be equipped to handle data at the right time, in the right form, and in a secure manner.

In the AI era, you need clear goals, comprehensive documentation, accountable stakeholders, prioritization, and a high level of automation to be ready. This is a substantial shift in mindset, necessitating constant learning and adaptation. The Process Maturity Ladder (PML) can be your guide, especially when automating manual processes. There’s practical information in each series of articles to help move you from AI-curious to AI-ready and beyond.

So, what will your AI-readiness look like? My Boomi colleagues and I can help you chart a course no matter where you are on this journey. Don’t hesitate to contact us about a workshop to help your enterprise become AI-ready and launch your transformation journey.

Check out the earlier posts in this series: