For technology to deliver, it must enable people to achieve their desired goals. This means determining how to define value in a way that assures business fit at both a strategic level and in operational and organizational processes.

Let’s get to it.

Defining Value for Strategic Business Fit

Should we develop and implement the solutions we conceive of in our heads? That is as much a value question as an economic one. How can we weigh, compare, and contrast cost to value to proffer a decision? Should we consider cultural and societal impact? Sometimes, value is measured in terms of what happens if you don’t do something, rather than if you do.

At the core, a business is trying to do some fundamental things. If it’s publicly traded, executives are trying to drive shareholder value—that is, make money or save money, or save time (to make or save money). From this standpoint, being profitable is an ongoing concern.

My preferred definition of a business is exactly that—an ongoing concern. To succeed in business, you must successfully define and execute a strategy. Using business school principles, we can define a value-based strategy based on three pillars: operational efficiency, customer intimacy, and product/performance superiority.

• Operational efficiency is about saving, reducing, optimizing, and modernizing (waste, costs, processes, infrastructure, etc.). It’s an expense you’re trying to reduce or a process you’re trying to improve or optimize.
• Customer intimacy is about investing to gain more customers, drive more revenue, and make more profit. That’s an investment in doing business in the manner and location your customers wish to do business with you.
• Product/performance superiority is an investment in staying ahead of your competitors, attracting more business, or developing new business to generate more revenue and profit.

Digital transformation is nothing more than those three things: business strategy leveraging digital technology and delivery as a primary lever. Tying a solution to one (or more) of these three pillars provides the relevance required to convince decision-makers of fit for purpose within their business strategy.

For providers, trade-offs and leapfrogs can be translated into sales value. For instance, I can create a technology or system that will provide 100% data availability, or unfettered performance, or unlimited capacity. But what must I trade off to provide that, and what leapfrog technology must I use—or convince you of its value—to get you to accept it?

At a customer executive conference over 20 years ago, we asked the audience to consider what they could accomplish if we, as an industry, could provide them seemingly infinite data storage capacity, network bandwidth, and compute resources. Today, we are on the brink of achieving that aspiration. In many ways, we are already providing it.

Even so, why would a business buy? Sellers must relate, or translate, how a technology solution enables enterprises to accomplish their strategic business goals. It must fit the strategy—or strategies—they are trying to implement.

If technology providers want to align with value-based strategy, they need to ask three questions:

1. How and where does my solution impact the three pillars?
2. Does that align with the customer’s strategic direction?
3. How can I best translate my resources and capabilities to reflect that?

For vendors, that is how you create marketing value statements and how you tie your technology to business at the strategic level.

Defining Value For Operations Fit

Even with a technology solution that fits their business strategy, you have to convince decision-makers and budget holders to buy and implement the solution. To do this, you must address their specific, persona-based decision criteria that are inevitably based on people and operational models.

There are, broadly, three buying personas that need to be satisfied: the executive buyer, the architect buyer, and the engineer buyer. Each has unique buying decision criteria you must address by translating your solution to meet those terms. Their perspectives are going to be influenced by how they view the impact to the operating model. In my experience, here is how all that shakes out.

• For the executive, you must address aspects related to awareness, urgency, and trust.
• For the architect, you must address aspects related to technical fit, scalability, and security.
• For the engineer, you must address aspects related to use and function, performance, and support.

Vendors must address each persona’s unique buying decision criteria to convince them to allocate the scarce resource known as budget. You are fighting for that resource and must convince both decision-maker and budget-holder of the operational value of your solution.

Combining Value For Best Fit

Your marketing and sales organizations must be able, and enabled, to translate and bridge the technical aspects of your solution to an organization’s business aspects and buying criteria. And as a seller, you won’t be the only one attempting to do this. You will have competition.

Ultimately, as a provider, the goal is to map your own value-based business strategy onto your buyer’s business strategy and values, through value statements that reflect their needs and provide resources and capabilities to enable them to achieve their desired business outcomes.

You need to know what target you’re aiming at and how you are responding, at a business level, to hit that target. Technology marketing teams are always striving to develop a value statement, but they’re typically thinking in terms of speeds and feeds or technical capability and differentiators. This approach will have limited appeal and “legs” beyond the specific buying persona or decision-maker interested in these aspects.

As a buyer, challenge your providers and sellers to meet your organization’s needs, strategies, and values on your terms. Analyze, review, and judge them on that basis. Why buy, otherwise?

Next Steps

At GigaOm, we work with clients to develop, enable, and activate the value propositions and statements unique to them based on the research we produce, matching users and providers for best technical and operational fit to needs and requirements.

How do you feel about your organization’s resources and capabilities to do this? Would you like some help?

If so, contact us to get started.

The post Machines as Amplifiers: Constructing Value Statements appeared first on Gigaom.

I’ve learned to see it as the art of designing IT solutions that amplify human productivity, capability, and creativity. For these aspirations to be realized however, these solutions need to be reframed and translated into business value for acquisition and implementation. 

It’s a tricky proposition in this hypercompetitive world, which we’re seeing unfold in front of our eyes due to the current buzz around AI and Large Language Models (LLMs). The ‘arrival’ of AI onto the scene is really the delivery of the promise and aspirations of six decades of iterative effort.

However, its success – defined in terms of business value – is not a given. To understand this, let me first take you back to a technical article I came across early on in my career. “All machines are amplifiers,” it stated in a simple and direct manner. That statement was an epiphany for me. I’d considered amplifiers as just a unit in a stereo system stack or what you plugged your guitar into. 

Mind blown.

As I have pondered this realization across my career, I have come to consider IT as a collection of machines offering similar amplification, albeit on a much broader scale and with greater reach.

IT amplifies human productivity, capability, and creativity. It allows us to do things we could never do before and do them better and faster. It helps us solve complex problems and create new opportunities – for business and humanity.

To augment or to replace – THAT was the question

However, amplification is not an end in itself. In the 1960s, two government-funded research labs on opposite sides of the University of Berkeley Stanford campus pursued fundamentally different philosophies. One believed that powerful computing machines could substantially increase the power of the human mind. The other wanted to create a simulated human intelligence. 

These efforts are documented in John Markoff’s book, “What The Dormouse Said – How the Sixties Counterculture Shaped the Personal Computer Industry”.

One group worked to augment the human mind, the other to replace it. Whilst these two purposes, or models, are still relevant to computing today, augmenting the human mind proved to be the easier of the two to deliver – with a series of miniaturization steps culminating in the general consumer availability of the personal computer (PC) in the 1980s. PCs freed humans to be individually productive and creative, and changed how education and business were done around the globe. Humanity rocketed forward and has not looked back since.

Artificial Intelligence (AI) is now becoming commercially viable and available at our fingertips to replace the human mind. It is maturing rapidly, being implemented at breakneck speeds in multiple domains, and will revolutionize how computing is designed and deployed in every aspect from this point forward. While it came to fruition later than its 1960s sibling, its impact will be no less revolutionary with, perhaps, an end-state of intelligence that can operate itself.

Meanwhile, automation on the augmentation front has also rapidly advanced, enabling higher productivity and efficiencies for humans. It’s still a human world, but our cycles continue to be freed up for whatever purpose we can imagine or aspire to, be they business or personal endeavors.

Systems engineering – finding a path between trade-offs

From a high-level fundamental compute standpoint, that’s all there really is – augment or replace. Both models must be the starting point of any system we design. To deliver on the goal, we turn to systems engineering and design at a more detailed, complex, and nuanced level. 

The primary task has always been simple in concept – to move bits (bytes) of data into the processor registers where it can be operated upon. That is, get data as close to the processor as possible and keep it there for as long as practical. 

In practice this can be a surprisingly difficult and expensive proposition with a plethora of trade-offs. There are always trade-offs in IT. You can’t have it all.  Even if it were technically feasible and attainable you couldn’t afford it or certainly would not want to in almost every case. 

To accommodate this dilemma, at the lower levels of the stack, we’ve created a chain of different levels of various data storage and communications designed to feed our processors in as efficient and effective a manner as practical, enabling them to do the ‘work’ we request of them. 

For me, then, designing and engineering for purpose and fit is, in essence, simple. Firstly, am I solving for augmentation or replacement? Secondly, where’s the data, and how can I get it where it needs to be processed, governed, managed, and curated effectively? 

And one does not simply store, retrieve, manage, protect, move, or curate data. That stuff explodes in volume, variety, and velocity, as we are wont to say in this industry. These quantities are growing exponentially. Nor can we prune or curate it effectively, if at all, even if we wanted to. 

Applying principles to the business value of AI

All of which brings us back to the AI’s arrival on the scene. The potential for AI is huge, as we are seeing. From the systems engineer’s perspective however, AI requires a complete data set to enable the expected richness and depth of the response. If the dataset is incomplete, ipso facto, so is the response – and, thus, it could be viewed as bordering on useless in many instances. In addition AI algorithms can be exhaustive (and processor-intensive) or take advantage of trade-offs. 

This opens up a target-rich environment of problems for clever computer scientists and systems engineers to solve, and therein lies the possibilities, trade-offs, and associated costs that drive all decisions to be made and problems to be solved at every level of the architecture – user, application, algorithm, data, or infrastructure and communications.

AI has certainly ‘arrived’, although for the systems engineer, it’s more a continuation of a theme, or evolution, than something completely new. As the PC in the 1980s was the inflection point for the revolution of the augmentation case, so too is AI in the 2020s for the replacement case. 

It then follows, how are we to effectively leverage AI? We will need the right resources and capabilities in place (people, skills, tools, tech, money, et al) and the ability within the business to use the outputs it generates. It resolves to business maturity, operational models and transformational strategies.

Right now I see three things as lacking. From the provider perspective, AI platforms (and related data management) are still limited which means a substantial amount of DIY to get value out of them. I’m not talking about ChatGPT in itself, but, for example, how it integrates with other systems and data sets. Do you have the knowledge you need to bring AI into your architecture?

Second, operational models are not geared up to do AI with ease. AI doesn’t work out of the box beyond off-the-shelf models, however powerful they are. Data scientists, model engineers, data engineers, programmers, and operations staff all need to be in place and skilled up. Have you reviewed your resourcing and maturity levels?

Finally, and most importantly, is your organization geared up to benefit from AI? Suppose you learn a fantastic insight about your customers (such as the example of vegetarians being more likely to arrive at their flights on time), or you find out when and how your machinery will fail. Are you able to react accordingly as a business?

If the answer to any of these questions is lacking, then you can see an immediate source of inertia that will undermine business value or prevent it altogether. 

In thinking about AI, perhaps don’t think about AI… think about your organization’s ability to change and unlock AI’s value to your business.

The post What’s the Business Value of AI? A Systems Engineer’s Take appeared first on Gigaom.