Business leaders worldwide are eager to improve their understanding of AI and move their companies to the forefront of this massive new wave of innovation. But, before the “AI-curious” can embrace the technology, they might have a little work to do. A recent study of Fortune 1000 companies found that 59% of their C-level leaders lack the resources needed to meet generative AI expectations set by their companies’ CEOs and boards. Such companies need to learn how to walk the AI talk before they can run with this new technology.
What does it take to make your company “AI-ready?”
Companies want to use AI to run better, to scale more intelligently, to increase revenue, to decrease costs, and to stay compliant with regulations that they may face in their industry or with local laws. In short, there are numerous reasons why C-suite leaders or boards may be putting pressure on their teams to use AI. Being AI-ready means being in a position to leverage AI — and the data on which it is predicated — to respond to those pressures, and to do so in a compliant, ethical, and efficient way.
If you’re facing similar pressure to adopt AI in your organization, be aware of the full implications. Your people, processes, and platforms all need to be AI-ready before you can meaningfully implement this technology.
Six Tenets of AI Readiness
Since the Summer of 2023, I have been discussing AI Readiness with leaders around the world. From countless meetings, we came up with the six tenets of AI readiness :
- Set Clear Goals
- Know Your Processes
- Know Your Data
- Align and Be Accountable
- Prioritize Thoughtfully
- Automate with Intention
In order to get your organization AI-ready, you first need to develop clear AI goals.
You must craft a clear roadmap where AI is not an isolated project but woven into the fabric of your strategic plan. Reaping the full benefits of AI requires a team of people with the right skills and will to execute the vision. It also includes people who don’t have these skills, too — people who will use AI-driven insights and apply them to specific business cases. Everyone needs to be aligned with the broader vision.
Second, you must make your processes AI-ready.
Before thinking about infusing machine intelligence into any process, you must take inventory of how your processes, services, or tasks work, today. Warning! This may take work, but this work is necessary before implementing AI. Undocumented and under-documented procedures manifest risks to implementing AI.
If you do not understand how a procedure works–I mean at a detailed level–then introducing AI may compound a problem that you may never sort out.
Therefore, documentation of current processes and procedures is imperative. Now is the time to do it. Mapping out workflows, evaluating operating procedures, identifying inefficiencies, and recognizing opportunities will give you more confidence in knowing that automation and AI will enhance efficiencies.
If you do not know how your process works today, how can you be sure that introducing AI into the process is going to help or hurt the business or people?
Be sure to engage key stakeholders, ensure everyone is on the same page about what you’re trying to accomplish, and help them get ready for change.
Third, do you trust your data? Really trust it?
If your business is like many I’ve consulted, folks don’t always know how their data is generated, sourced, processed, and used. Knowing your data–process by process–is crucial to being able to trust your data. Knowing your data also allows you to leverage AI in useful ways.
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Having the right technology platforms–modern ERP systems, best-of-breed applications, scalable and intelligent integration, and automation platforms, for instance–is an excellent start to trusting data to operate your business. Knowing your data and application infrastructure ensures that you are ready to manage the massive amounts of volume and variety of inputs needed to make AI as effective as possible. Data pipelines for AI require platforms built for speed, scale, and security.
But first, you need to know where the data comes from and how it is being created. This is veracity and, now more than ever, you need to be able to trust the input that is feeding the system of intelligence. This is where a context pipeline emerges.
A context pipeline feeds AI with data relevant to your enterprise, increasing confidence in the data so that you can be sure it is capable of delivering accurate and relevant answers to the questions you give it. Elements of a context pipeline include systems for Retrieval Augmented Generation (RAG), data synchronization, cleansing and filtering, enriching, and much more.
Getting Your Enterprise AI-Ready
Of course, it’s not enough to ensure that you know your processes and data. Making your enterprise AI-ready also requires people to be on the same page, and that starts with leadership. Leadership needs to be clear on what they want to do with AI — and what they can do with it, based on existing resource constraints. Having the right vision, leadership, and talent needed to define, identify, and execute AI is a great way to start.
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With a clear goal in mind, alignment can truly begin.
Technologists and line-of-business stakeholders alike have a great opportunity to learn from each other.
Leaders in non-technical roles such as marketing, customer support, sales, operations, and more can gain an intuition of how AI works, and where it could catapult the business to soaring heights or create unforeseen risks that could cost the organization dearly.
Conversely, non-technical stakeholders can work with technologists to redouble efforts on backlogged projects that may be attainable with modern AI-infused processes. AI allows us to create a middle ground between techies and non-technical domain experts within the organization.
The AI journey might be a major change for your organization, and, so it’s important to foster a culture of curiosity, openness, and willingness to learn, working together from the top to the bottom of your organization to establish priorities enterprise-wide. With a baseline intuition of how AI works, articulated goals, and alignment of individuals and teams throughout the enterprise, attention can be placed on prioritizing the processes that create the outcomes critical to the success of the business.
When ChatGPT emerged in November 2022, the world paused to marvel at this disruption. Almost overnight, it seemed that imaginations were captured and priorities shifted to figuring out how to use this technology within any platform. We’ve since learned that it’s not that easy to implement generative AI into simply any process, yet priorities are shifting to figure out how. It is up to each company’s stakeholders to determine the priorities in this new world, and those priorities will be better informed with well-understood data and processes.
Making Processes AI-Ready
The final tenet of AI readiness is automating with intention. Oftentimes, this means to automate manual processes, workflows, or procedures.
Automating manual processes is an important undertaking when using AI. Machine intelligence processes data that is pipelined into it. The greater the volume and velocity of data flowing into an AI platform, the faster AI can find patterns and predict or prescribe outcomes more quickly. Manual procedures can become a major bottleneck to the outcomes that AI delivers; they can be rife with error, as well. This is why, before AI is to be used on existing procedures, it is helpful to rethink the goals of the current manual workflow, consider how the desired outcomes could be made better and, when ready, automate the process.
Stakeholders who desire to implement AI into their process(es) responsibly can be in for a painstaking journey. A good approach is to understand what processes are critical to the business or operation, and then determine where on a maturity scale each of these processes is.
I use a rubric we call the Process Maturity Ladder to help guide organizations on that path:
- Manual – Processes and data are created by humans. May be analog or digital, but tend to be siloed.
- Automated – Processes are beginning to be automated. These may be either derived or integrated.
- Intelligent – Refers to processes or data whose rules and patterns have been defined by intelligence, either biological or (human) or non-biological (artificial) intelligence.
- Optimized – At this stage, processes and data accurately model the real world, producing accurate projections and predictions with little bias.
- Disruptive – At the highest level of the ladder, processes, and data have undergone a paradigm shift, enabling organizations to leverage them in radically new ways — creating new products, new sources of revenue, and new ventures, potentially disrupting entire industries.
Assessing where each process is on the ladder, at present, is the first step to determining if AI can be (or should be) infused into the process at all. As you move towards automation of manual procedures, you need to understand what data is important in the current one, how that data has been sourced, how it’s consumed, and how data quality is measured and validated. All this is a necessary precursor to implementing more advanced AI processes in the optimized and disruptive stages of the ladder.
Finally, as you continue on this journey, don’t misplace your moral compass. AI is a powerful tool that comes with a unique set of ethical concerns. As leaders, we need to ensure that services or functions leveraging AI do so while respecting the rights of employees, partners, and customers, and at the same time, avoiding discrimination or bias.
AI-Readiness Is Just the Start of the Journey
Becoming AI-ready requires systemic transformation, which involves people, processes, and platforms. To bring all of those together effectively requires cultivating a culture of curiosity, openness, and continuous learning; having dependable partners and vendors for assistance is also crucial. This requires a major shift in mindset across the enterprise, demanding flexibility and adaptability at all levels of the organization. To guide you on your journey, tools like the six tenets of AI readiness and frameworks like the Process Maturity Ladder can guide your transition from AI-curious to AI-ready in a deliberate way.
Going from AI-curious to AI-ready is just the first step.
Putting in the work upfront will pay off in the long run, as your organization can capitalize on the disruptive potential of artificial intelligence while doing your best to minimize any negative consequences.