The Future of AI in Businesses
The Future of AI in Businesses
From Generic AI Tools to Business-Specific Solutions: Predicting the Next Paradigm of Enterprise AI
Olamide Oladimeji
MSc, Artificial Intelligence · Independent Researcher · DEC 2024
Abstract
The arrival of general-purpose artificial intelligence (AI) has given almost every business access to tools more capable than anything available a decade ago yet access has not translated into transformation. In this paper I argue that the defining limitation of the current era is not the capability of AI models but their generality: tools designed to do everything for everyone are poorly suited to the specific operational realities of an individual business, and the smaller the business, the sharper this mismatch becomes. I examine the present landscape the manual, fragmented operations that preceded AI, and the first wave of generic AI tools that has failed to close the gap and I predict the next paradigm: a decisive shift from generic tools to business-specific solutions. My central contention is that the next decisive advance in enterprise AI will be one of delivery and fit, not raw model power.
Keywords: artificial intelligence; AI agents; workflow automation; business-specific AI; SMEs; digital transformation; no-code; self-build platforms.
1. Introduction
I want to begin with contradictions. The tools now available in commodity AI systems would, only a few years ago, have been the preserve of organizations with substantial research budgets. And yet, when I look at how ordinary businesses actually operate, I see remarkably little changes. The capability is universal; the benefit is not. Most businesses and in particular the small and medium-sized enterprises (SMEs) that make up the overwhelming majority of firms in every economy extract only a fraction of the value the technology appears to promise.
My argument in this paper is that the cause of this gap is generality. The first widely available AI tools were built to be universal: a single, undifferentiated assistant offered to everyone, who is then left to work out how it applies to their own operations. For an organization with engineers and analysts, that is an opportunity. For a business without them, it is a dead end. The problem is not that the models are too weak. It is that they are not shaped to the business.
This paper proceeds in three movements. First, the present: how businesses operated before AI, and why the first generation of generic AI tools has not closed the gap. Second, the future: the shift I predict from generic tools to business-specific solutions, why I believe it will happen, and the system I expect to deliver it. Third, the demand: the size and nature of the market such a system would serve. I write this not as a description of something that exists, but as a prediction of something I believe must.
2. The Present: Generic Tools in a Specific World
2.1 The Pre-AI baseline: manual and fragmented operations
The day-to-day operation of most businesses still rests on human labor applied to repetitive, rules-based tasks: answering the same customer's enquiries, booking appointments, chasing invoices, re-keying data between systems, and compiling reports by hand. Where software is used, it is frequently fragmented, a booking tool that does not speak to the accounts package, a messaging channel disconnected from the customer's record. The result is operational drag: time and attention consumed by coordination rather than by the work that creates value. This is the baseline against which any genuine advance must be measured.
2.2 The first wave: generic AI tools and their limits
The first generation of accessible AI promised to change this, and in some narrow respects it has. General-purpose assistants can draft text, answer questions, and summarise information with real skill. But I would argue they share three limitations that prevent them from transforming how a business actually runs. First, they are generic: they offer the same undifferentiated capability to a law firm, a salon and a logistics company alike, and place the entire burden of adaptation on the user. Second, they advise but do not act they can tell an owner how to do something, but they do not book the appointment, update the record or dispatch the report, they sit beside the business rather than operating within it. Third, they are disconnected: they are not wired into the tools, channels and processes a business already depends on. The net effect is that the owner must still translate a general capability into a specific outcome and that translation is precisely the work most businesses lack the time or expertise to do.
2.3 The false choice: generic tools versus bespoke development
Faced with this, a business seeking real improvement has historically had only two options, and both are unsatisfactory. It can adopt generic, off-the-shelf tools — affordable and immediately available, but standardised and rarely aligned to the specific way the business works. Or it can commission bespoke development — precisely tailored, but slow, costly, and dependent on technical talent few SMEs can access. The space between them — solutions that are affordable and tailored to the individual business has remained almost entirely unserved. This unserved middle is, in my view, the defining failure of the present, and the space the future will fill.
3. The Inflection Point
Why predict change now? Because, for the first time, the ingredients of a different approach are simultaneously available. General-purpose models have reached a level of capability and affordability at which they can interpret natural language, reason over context and execute multi-step tasks. The emergence of agentic AI — systems that take actions rather than merely generate text — extends this from advice to execution. And the no-code movement has shown that sophisticated software behaviour can be configured by non-technical people through accessible interfaces. The raw materials of a business-specific approach now exist; what is missing is the system that assembles them.
This is the crux of my prediction. Studies of AI adoption consistently find that the binding constraints are not technological but organisational — businesses struggle to identify where AI applies to them, and lack the skills to implement it. If the barrier were the technology, the answer would be a better model. Because the barrier is application, the answer must be a better means of delivering AI in a form shaped to the business. That conviction drives everything that follows.
4. The Future: From Generic Tools to Business-Specific Solutions
4.1 The shift I predict
I predict that the next era of business AI will be defined not by access to ever more powerful general models, but by business-specific solutions. Value will accrue not to whoever provides a model, but to whoever provides the system that connects a firm’s concrete operational problems to working, deployed solutions — tailored to one business, yet repeatable across many. The unit of value shifts from the model to the fit. Where the first wave asked the business to adapt to the tool, the next will adapt the tool to the business.
4.2 The convergence of agents and workflows
I expect the defining technical move to be the unification of two strands of automation that have so far been pursued in isolation. AI agents converse, interpret and decide; workflow automation executes defined sequences of actions across systems. Their separation is artificial. An agent that can answer a query but cannot trigger the downstream process — book the appointment, update the record, dispatch the report — solves only half the problem. The architecture I anticipate will join the two, so that conversation and execution are continuous. In its most advanced form, such a system would let an agent not merely answer and act, but actively guide a user through a process or an application step by step, using visual, in-context cues to reduce friction and drive task completion — turning passive support into active assistance.
4.3 Democratisation through self-build delivery
Finally, I predict the delivery model will shift from the commissioned project to self-build. Just as the cloud turned infrastructure from a capital project into a subscription, business AI will turn from a bespoke engagement into a configurable product that an owner can browse, assemble and deploy directly — through a self-build portal, in minutes rather than months, and without writing a line of code. Accessibility, not raw capability, becomes the decisive feature.
5. Why I Believe This Shift Is Inevitable
Four forces, in my analysis, make this direction more than a possibility. First, economics: the productivity locked up in the long tail of small businesses vastly exceeds the marginal gains available from making already-sophisticated enterprises incrementally more efficient; capital and effort will follow that prize. Second, latent demand: the evidence (Section 7) indicates that businesses already recognise AI’s potential and are constrained only by accessibility — a demand awaiting a means of fulfilment. Third, technological readiness: the agentic and no-code components no longer need to be invented, only combined. Fourth, a compounding dynamic I regard as decisive: once a bespoke solution to a real business problem is built, it can be generalised into a reusable component, so that every deployment enlarges a shared library of capabilities. This flywheel rewards whichever system accumulates the most validated, real-world solutions, and makes the eventual emergence of a dominant, accessible platform structurally likely.
6. The System I Expect to Emerge
My analysis points to a specific design. The system that resolves the failures described above would, I propose, combine the following elements:
A unified agent-and-workflow core: autonomous AI agents and automated workflows operating as one integrated system, so that conversation and execution are continuous rather than siloed; workflows able to run standalone or embedded within an agent.
Multi-channel deployment: the same agent deployable wherever customers already are — a website, a shareable link, a telephone line, SMS, or messaging applications — rather than confined to a single channel.
A no-code self-build portal: an interface through which a non-technical owner can configure, assemble and deploy solutions directly, removing the dependency on developers and the delay of commissioned projects.
Each element answers a specific failure of the present. The unified core resolves fragmentation; multi-channel deployment meets customers where they are; the self-build portal and editable knowledge base dissolve the dependence on scarce technical labor; and the productised library, by spreading the cost of each bespoke build across all users, finally serves the unserved middle ground between generic tools and custom development.
7. The Demand for the Solution
A prediction is only as significant as the demand it anticipates, and here I believe the demand is large, latent, and growing. Surveys of AI adoption consistently report that only a minority of firms actively use AI, while a substantial majority neither use it nor have concrete plans to, yet, critically, the reasons given are not rejection of the technology but an inability to identify suitable use cases and a lack of relevant skills.
This is precisely the pattern one would expect of a market constrained by accessibility rather than appetite. Where firms are asked whether a unified solution tailored to their specific operational pain points would help them, and whether they would adopt it if it were affordable and demanded no technical expertise, the latent interest is high. I therefore conclude that the demand is not waiting to be created, but to be unlocked. The first system to make business-specific AI genuinely accessible will not have to manufacture a market; it will meet one that already exists.
8. Discussion and Implications
The implications of this shift, if I am right, are considerable. Economically, extending enterprise-grade capability to the long tail of smaller firms would raise productivity across the broadest base of the economy, rather than concentrating gains among organisations already well served. Competitively, as accessible, tailored AI becomes the norm; the operational advantages once reserved for large firms would erode, partially levelling a long-standing asymmetry between the largest businesses and the smallest.
The trajectory is not without risk. Questions of data governance, accuracy, accountability and over-reliance on automated decision-making will demand careful design and, in some sectors, regulation. A credible system must therefore pair accessibility with control — transparent oversight, the ability to correct and constrain behavior, and clear human accountability. I regard these as design requirements that shape the solution, not objections that negate it.
9. Conclusion
Businesses today operate in a specific world with generic tools. The pre-AI baseline was manual and fragmented; the first wave of AI, for all its capability, has remained too general, too passive and too disconnected to change how a business actually runs, leaving owners to choose between off-the-shelf tools that do not fit and bespoke development they cannot afford. I have argued that this is a transitional state, not a stable one. The future I predict belongs to business-specific solutions: tailored capability made accessible. Its defining architecture will unify AI agents and automated workflows in a single, self-build system that turns bespoke solutions into reusable components resolving, at last, the false choice between generic tools and custom development. The demand for such a system already exists, constrained only by accessibility. Whoever removes that constraint will not merely participate in the future of AI in business; they will define it. I intend to find out whether that future can be built.
References
Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction Machines: The Simple Economics of Artificial Intelligence. Harvard Business Review Press.
Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton & Company.
Davenport, T. H. (2018). The AI Advantage: How to Put the Artificial Intelligence Revolution to Work. MIT Press.
The Future of AI in Businesses
From Generic AI Tools to Business-Specific Solutions: Predicting the Next Paradigm of Enterprise AI
Olamide Oladimeji
MSc, Artificial Intelligence · Independent Researcher · DEC 2024
Abstract
The arrival of general-purpose artificial intelligence (AI) has given almost every business access to tools more capable than anything available a decade ago yet access has not translated into transformation. In this paper I argue that the defining limitation of the current era is not the capability of AI models but their generality: tools designed to do everything for everyone are poorly suited to the specific operational realities of an individual business, and the smaller the business, the sharper this mismatch becomes. I examine the present landscape the manual, fragmented operations that preceded AI, and the first wave of generic AI tools that has failed to close the gap and I predict the next paradigm: a decisive shift from generic tools to business-specific solutions. My central contention is that the next decisive advance in enterprise AI will be one of delivery and fit, not raw model power.
Keywords: artificial intelligence; AI agents; workflow automation; business-specific AI; SMEs; digital transformation; no-code; self-build platforms.
1. Introduction
I want to begin with contradictions. The tools now available in commodity AI systems would, only a few years ago, have been the preserve of organizations with substantial research budgets. And yet, when I look at how ordinary businesses actually operate, I see remarkably little changes. The capability is universal; the benefit is not. Most businesses and in particular the small and medium-sized enterprises (SMEs) that make up the overwhelming majority of firms in every economy extract only a fraction of the value the technology appears to promise.
My argument in this paper is that the cause of this gap is generality. The first widely available AI tools were built to be universal: a single, undifferentiated assistant offered to everyone, who is then left to work out how it applies to their own operations. For an organization with engineers and analysts, that is an opportunity. For a business without them, it is a dead end. The problem is not that the models are too weak. It is that they are not shaped to the business.
This paper proceeds in three movements. First, the present: how businesses operated before AI, and why the first generation of generic AI tools has not closed the gap. Second, the future: the shift I predict from generic tools to business-specific solutions, why I believe it will happen, and the system I expect to deliver it. Third, the demand: the size and nature of the market such a system would serve. I write this not as a description of something that exists, but as a prediction of something I believe must.
2. The Present: Generic Tools in a Specific World
2.1 The Pre-AI baseline: manual and fragmented operations
The day-to-day operation of most businesses still rests on human labor applied to repetitive, rules-based tasks: answering the same customer's enquiries, booking appointments, chasing invoices, re-keying data between systems, and compiling reports by hand. Where software is used, it is frequently fragmented, a booking tool that does not speak to the accounts package, a messaging channel disconnected from the customer's record. The result is operational drag: time and attention consumed by coordination rather than by the work that creates value. This is the baseline against which any genuine advance must be measured.
2.2 The first wave: generic AI tools and their limits
The first generation of accessible AI promised to change this, and in some narrow respects it has. General-purpose assistants can draft text, answer questions, and summarise information with real skill. But I would argue they share three limitations that prevent them from transforming how a business actually runs. First, they are generic: they offer the same undifferentiated capability to a law firm, a salon and a logistics company alike, and place the entire burden of adaptation on the user. Second, they advise but do not act they can tell an owner how to do something, but they do not book the appointment, update the record or dispatch the report, they sit beside the business rather than operating within it. Third, they are disconnected: they are not wired into the tools, channels and processes a business already depends on. The net effect is that the owner must still translate a general capability into a specific outcome and that translation is precisely the work most businesses lack the time or expertise to do.
2.3 The false choice: generic tools versus bespoke development
Faced with this, a business seeking real improvement has historically had only two options, and both are unsatisfactory. It can adopt generic, off-the-shelf tools — affordable and immediately available, but standardised and rarely aligned to the specific way the business works. Or it can commission bespoke development — precisely tailored, but slow, costly, and dependent on technical talent few SMEs can access. The space between them — solutions that are affordable and tailored to the individual business has remained almost entirely unserved. This unserved middle is, in my view, the defining failure of the present, and the space the future will fill.
3. The Inflection Point
Why predict change now? Because, for the first time, the ingredients of a different approach are simultaneously available. General-purpose models have reached a level of capability and affordability at which they can interpret natural language, reason over context and execute multi-step tasks. The emergence of agentic AI — systems that take actions rather than merely generate text — extends this from advice to execution. And the no-code movement has shown that sophisticated software behaviour can be configured by non-technical people through accessible interfaces. The raw materials of a business-specific approach now exist; what is missing is the system that assembles them.
This is the crux of my prediction. Studies of AI adoption consistently find that the binding constraints are not technological but organisational — businesses struggle to identify where AI applies to them, and lack the skills to implement it. If the barrier were the technology, the answer would be a better model. Because the barrier is application, the answer must be a better means of delivering AI in a form shaped to the business. That conviction drives everything that follows.
4. The Future: From Generic Tools to Business-Specific Solutions
4.1 The shift I predict
I predict that the next era of business AI will be defined not by access to ever more powerful general models, but by business-specific solutions. Value will accrue not to whoever provides a model, but to whoever provides the system that connects a firm’s concrete operational problems to working, deployed solutions — tailored to one business, yet repeatable across many. The unit of value shifts from the model to the fit. Where the first wave asked the business to adapt to the tool, the next will adapt the tool to the business.
4.2 The convergence of agents and workflows
I expect the defining technical move to be the unification of two strands of automation that have so far been pursued in isolation. AI agents converse, interpret and decide; workflow automation executes defined sequences of actions across systems. Their separation is artificial. An agent that can answer a query but cannot trigger the downstream process — book the appointment, update the record, dispatch the report — solves only half the problem. The architecture I anticipate will join the two, so that conversation and execution are continuous. In its most advanced form, such a system would let an agent not merely answer and act, but actively guide a user through a process or an application step by step, using visual, in-context cues to reduce friction and drive task completion — turning passive support into active assistance.
4.3 Democratisation through self-build delivery
Finally, I predict the delivery model will shift from the commissioned project to self-build. Just as the cloud turned infrastructure from a capital project into a subscription, business AI will turn from a bespoke engagement into a configurable product that an owner can browse, assemble and deploy directly — through a self-build portal, in minutes rather than months, and without writing a line of code. Accessibility, not raw capability, becomes the decisive feature.
5. Why I Believe This Shift Is Inevitable
Four forces, in my analysis, make this direction more than a possibility. First, economics: the productivity locked up in the long tail of small businesses vastly exceeds the marginal gains available from making already-sophisticated enterprises incrementally more efficient; capital and effort will follow that prize. Second, latent demand: the evidence (Section 7) indicates that businesses already recognise AI’s potential and are constrained only by accessibility — a demand awaiting a means of fulfilment. Third, technological readiness: the agentic and no-code components no longer need to be invented, only combined. Fourth, a compounding dynamic I regard as decisive: once a bespoke solution to a real business problem is built, it can be generalised into a reusable component, so that every deployment enlarges a shared library of capabilities. This flywheel rewards whichever system accumulates the most validated, real-world solutions, and makes the eventual emergence of a dominant, accessible platform structurally likely.
6. The System I Expect to Emerge
My analysis points to a specific design. The system that resolves the failures described above would, I propose, combine the following elements:
A unified agent-and-workflow core: autonomous AI agents and automated workflows operating as one integrated system, so that conversation and execution are continuous rather than siloed; workflows able to run standalone or embedded within an agent.
Multi-channel deployment: the same agent deployable wherever customers already are — a website, a shareable link, a telephone line, SMS, or messaging applications — rather than confined to a single channel.
A no-code self-build portal: an interface through which a non-technical owner can configure, assemble and deploy solutions directly, removing the dependency on developers and the delay of commissioned projects.
Each element answers a specific failure of the present. The unified core resolves fragmentation; multi-channel deployment meets customers where they are; the self-build portal and editable knowledge base dissolve the dependence on scarce technical labor; and the productised library, by spreading the cost of each bespoke build across all users, finally serves the unserved middle ground between generic tools and custom development.
7. The Demand for the Solution
A prediction is only as significant as the demand it anticipates, and here I believe the demand is large, latent, and growing. Surveys of AI adoption consistently report that only a minority of firms actively use AI, while a substantial majority neither use it nor have concrete plans to, yet, critically, the reasons given are not rejection of the technology but an inability to identify suitable use cases and a lack of relevant skills.
This is precisely the pattern one would expect of a market constrained by accessibility rather than appetite. Where firms are asked whether a unified solution tailored to their specific operational pain points would help them, and whether they would adopt it if it were affordable and demanded no technical expertise, the latent interest is high. I therefore conclude that the demand is not waiting to be created, but to be unlocked. The first system to make business-specific AI genuinely accessible will not have to manufacture a market; it will meet one that already exists.
8. Discussion and Implications
The implications of this shift, if I am right, are considerable. Economically, extending enterprise-grade capability to the long tail of smaller firms would raise productivity across the broadest base of the economy, rather than concentrating gains among organisations already well served. Competitively, as accessible, tailored AI becomes the norm; the operational advantages once reserved for large firms would erode, partially levelling a long-standing asymmetry between the largest businesses and the smallest.
The trajectory is not without risk. Questions of data governance, accuracy, accountability and over-reliance on automated decision-making will demand careful design and, in some sectors, regulation. A credible system must therefore pair accessibility with control — transparent oversight, the ability to correct and constrain behavior, and clear human accountability. I regard these as design requirements that shape the solution, not objections that negate it.
9. Conclusion
Businesses today operate in a specific world with generic tools. The pre-AI baseline was manual and fragmented; the first wave of AI, for all its capability, has remained too general, too passive and too disconnected to change how a business actually runs, leaving owners to choose between off-the-shelf tools that do not fit and bespoke development they cannot afford. I have argued that this is a transitional state, not a stable one. The future I predict belongs to business-specific solutions: tailored capability made accessible. Its defining architecture will unify AI agents and automated workflows in a single, self-build system that turns bespoke solutions into reusable components resolving, at last, the false choice between generic tools and custom development. The demand for such a system already exists, constrained only by accessibility. Whoever removes that constraint will not merely participate in the future of AI in business; they will define it. I intend to find out whether that future can be built.
References
Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction Machines: The Simple Economics of Artificial Intelligence. Harvard Business Review Press.
Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton & Company.
Davenport, T. H. (2018). The AI Advantage: How to Put the Artificial Intelligence Revolution to Work. MIT Press.
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